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Solving Problems in Food Engineering

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Solving Problems in Food Engineering

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Año académico: 2018

Academic year: 2018

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Department of Food Science and Technology Agricultural University of Athens

Athens, Greece

ISBN: 978-0-387-73513-9 eISBN: 978-0-387-73514-6 Library of Congress Control Number: 2007939831

# 2008 Springer Science+Business Media, LLC

All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC., 233 Spring Street, New York, NY10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden.

The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

Preface . . . vii

1. Conversion of Units . . . 1 Examples

2. Use of Steam Tables. . . 5 Review Questions

Examples Exercises

3. Mass Balance. . . 11 Review Questions

4. Energy Balance . . . 21 Theory

Review Questions Examples

5. Fluid Flow . . . 33 Review Questions

6. Pumps. . . 41 Theory

7. Heat Transfer By Conduction. . . 55 Theory

8. Heat Transfer By Convection. . . 67 Theory

9. Heat Transfer By Radiation. . . 95 Review Questions

10. Unsteady State Heat Transfer . . . 101 Theory

11. Mass Transfer By Diffusion. . . 141 Theory

12. Mass Transfer By Convection . . . 155 Theory

13. Unsteady State Mass Transfer. . . 163 Theory

14. Pasteurization and Sterilization . . . 181 Review Questions

15. Cooling and Freezing . . . 193

Review Questions Examples Exercises 16. Evaporation . . . 215

Review Questions Examples Exercises 17. Psychrometrics. . . 237

Review Questions Examples Exercises 18. Drying . . . 253

Review Questions Examples Exercises References . . . 273

Appendix: Answers to Review Questions . . . 275

Moody diagram . . . 280

Gurney-Lurie charts . . . 281

Heisler charts . . . 284

Pressure-Enthalpy chart for HFC 134a . . . 285

Pressure-Enthalpy chart for HFC 404a . . . 286

Psychrometric chart . . . 287

Bessel functions . . . 288

Roots ofdtand=Bi . . . 290

Roots ofdJ1(d)-Bi Jo(d)=0 . . . 291

Roots ofdcotd=1-Bi . . . 292

Error function . . . 293

Food engineering is usually a difficult discipline for food science students because they are more used to qualitative rather than to quantitative descrip-tions of food processing operadescrip-tions. Food engineering requires understanding of the basic principles of fluid flow, heat transfer, and mass transfer phenomena and application of these principles to unit operations which are frequently used in food processing, e.g., evaporation, drying, thermal processing, cooling and freezing, etc. The most difficult part of a course in food engineering is often considered the solution of problems. This book is intended to be a step-by-step workbook that will help the students to practice solving food engineering problems. It presumes that the students have already studied the theory of each subject from their textbook.

The book deals with problems in fluid flow, heat transfer, mass transfer, and the most common unit operations that find applications in food processing, i.e., thermal processing, cooling and freezing, evaporation, psychometrics, and drying. The book includes 1) theoretical questions in the form ‘‘true’’ or ‘‘false’’ which will help the students quickly review the subject that follows (the answers to these questions are given in the Appendix); 2) solved problems; 3) solved problems; and 4) problems solved using a computer. With the semi-solved problems the students are guided through the solution. The main steps are given, but the students will have to fill in the blank points. With this technique, food science students can practice on and solve relatively difficult food engineering problems. Some of the problems are elementary, but problems of increasing difficulty follow, so that the book will be useful to food science students and even to food engineering students.

A CD is supplied with the book which contains solutions of problems that require the use of a computer, e.g., transient heat and mass transfer problems, simulation of a multiple effect evaporator, freezing of a 2-D solid, drying, and others. The objectives for including solved computer problems are 1) to give the students the opportunity to run such programs and see the effect of operating and design variables on the process; and 2) to encourage the students to use computers to solve food engineering problems. Since all the programs in this CD are open code programs, the students can see all the equations and the logic behind the calculations. They are encouraged to see how the programs work

and try to write their own programs for similar problems. Since food science students feel more comfortable with spreadsheet programs than with program-ming languages, which engineering students are more familiar with, all the problems that need a computer have EXCEL1spreadsheet solutions.

I introduce the idea of a digital SWITCH to start and stop the programs when the problem is solved by iteration. With the digital SWITCH, we can stop and restart each program at will. When the SWITCH is turned off the program is not running, so that we can change the values of the input variables. Every time we restart the program by turning the SWITCH on, all calculations start from the beginning. Thus it is easy to change the initial values of the input variables and study the effect of processing and design parameters. In the effort to make things as simple as possible, some of the spreadsheet programs may not operate on some sets of parameters. In such cases, it may be necessary to restart the program with a different set of parameters.

I am grateful to Dr H. Schwartzberg, who read the manuscripts and made helpful suggestions. I will also be grateful to readers who may have useful suggestions, or who point out errors or omissions which obviously have slipped from my attention at this point.

Athens Stavros Yanniotis

Show me and I will understand

Involve me and i will learn’’, conversion of units.

Table 1.1 Basic units

Time Length Mass Force Temperature

SI s m kg – K,0C

CGS s cm g – K,0C

US Engineering s ft lbm lbf 0R,0F

Table 1.2 Derived units

SI US Engineering

Force N (1 N = 1 kg m/s2 ) –

Energy J (1 J = 1 kg m2/s2) Btu

Power W (1 W = 1 J/s) HP, PS

Area m2 ft2

Volume m3(1m3= 1000 l) ft3

Density kg/m3 lb

Velocity m/s ft/s

Pressure Pa (1 Pa = 1 N/m2 )

bar (1 bar = 105 Pa)

torr (1 torr = 1 mmHg) atm (1 atm = 101325 Pa)

psi=lbf/in2

Table 1.3 Conversion factors

1 ft = 12 in = 0.3048 m 0 F = 32 þ 1.8* 0 C

1 in = 2.54 cm 0C = (0F-32)/1.8

1 US gallon = 3.7854 l 0 R = 460 þ 0 F

1 lbm= 0.4536 kg K = 273.15þ0C

1 lbf= 4.4482 N

1 psi = 6894.76 Pa 0C =0F/1.8

1 HP =745.7 W 0C =K

1 Btu = 1055.06 J = 0.25216 kcal 0F =0R 1kWh = 3600 kJ

S. Yanniotis,Solving Problems in Food Engineering. ÓSpringer 2008

Example 1.1

Convert 100 Btu/h ft2oF to kW/m2oC Solution

h ft28 F ¼100 Btu h ft28 F

1 Btu 1 kJ 1000 J 1 h 3600s 1ft2 0:3048 m

1 kJ=s¼0:5678 kW m28 C

Example 1.2

Convert 100 lb mol/h ft2to kg mol/s m2 Solution

100lb mol h ft2 ¼100

lbmol h ft2

0:4536 kg mol

1 ft2 0:3048 m

ð Þ2¼0:1356

kg mol s m2

Example 1.3

Convert 0.5 lbfs/ft2to Pas

0:5lbfs ft2 ¼0:5

4:4482 N lbf

1 Pa 1 N=m2

ð Þ¼23:94 Pa s

Exercise 1.1

Make the following conversions: 1) 10 ft lbf/lbmto J/kg, 2) 0.5 Btu/lbm

1) 10ft lbf lbm

¼10ft lbf lbm

::::::::::::::m

:::::::::::::::N 1 lbf

::::::::::::::lbm

:::::::::::::J m N

2) 0:5 Btu lbm8F

¼0:5 Btu lbm8F

::::::::::::: :::::::::::::

18C ¼2094:4 J kg8C

3) 32:174lbmft lbfs2

¼32:174lbmft lbfs2

:::::::::::::::

:::::::::::::::lbm

::::::::::::::::::m

::::::::::::::: 4:4482 N

¼1 kg m N s2

4) 1000lbmft s2 ¼1000

0:4536 kg ::::::::::::::::

::::::::::::::::

1 kg m=s2¼138:3 N

min fto F ¼10

kcal min fto F

1055:06 J 0:252 kcal

:::::::::min

::::::::::::ft :::::::::::m

:::::::::::8F ::::::::::K

::::::::::J=s¼4121 W m K

6) 30 psia¼30lbf in2

::::::::::::::::in2

:::::::::::::::::m2

::::::::::::::::::N ::::::::::::::::::lbf

:::::::::::::::::Pa

::::::::::::::N=m2

:::::::::::::::::atm

:::::::::::::::::Pa ¼2:04 atm 7) 0:002kg

m s¼0:002 kg m s

:::::::::::::::kg

:::::::::::::::m

::::::::::::::::::ft¼0:0013 lbm

8) 5 lb mol h ft2 mol frac ¼5

lb mol h ft2 mol frac

::::::::::::::::kg mol :::::::::::::::lb mol

:::::::::::::::::h ::::::::::::::::::s

:::::::::::::::::ft

:::::::::::::::::::m2¼6:7810

sm2 mol frac

9) 1:987 Btu

lb mol8R¼1:987 Btu lb mol8R

::::::::::::::cal ::::::::::::::Btu

::::::::::::::::lb mol ::::::::::::::::g mol ¼

::::::::::::::8R

::::::::::::K ¼1:987 cal g mol K

10) 10:731 ft

in2 lb mol 8 R ¼10:731

in2 lb mol 8 R

::::::::::::::::m3

:::::::::::::::::::ft3

::::::::::::::::N ::::::::::::::::::lbf

::::::::::::::::in

:::::::::::::::::::m2

:::::::::::::lb mol :::::::::::::kg mol

Exercise 1.2

Make the following conversions:

251oF tooC (Ans. 121.7oC) 500oR to K (Ans. 277.6 K) 0.04 lbm/in3to kg/m3

(Ans. 1107.2 kg/m3 )

12000 Btu/h to W (Ans. 3516.9 W ) 32.174 ft/s2 to m/s 2

(Ans. 9.807 m/s2 )

0.01 ft2/h to m2/s (Ans. 2.58x10-7m2/s) 0.8 cal/goC to J/kgK (Ans. 3347.3 J/kgK) 20000 kg m/s2 m 2 to psi

(Ans. 2.9 psi)

0.3 Btu/lbmoF to J/kgK

(Ans. 1256 J/kgK) 1000 ft3 /(h ft 2 psi/ft) to

cm3 /(s cm 2 Pa/cm)

Use of Steam Tables

Review questions.

Which of the following statements are true and which are false? 1. The heat content of liquid water is sensible heat.

2. The enthalpy change accompanying the phase change of liquid water at constant temperature is the latent heat.

3. Saturated steam is at equilibrium with liquid water at the same temperature. 4. Absolute values of enthalpy are known from thermodynamic tables, but for

convenience the enthalpy values in steam tables are relative values. 5. The enthalpy of liquid water at 273.16 K in equilibrium with its vapor has

been arbitrarily defined as a datum for the calculation of enthalpy values in the steam tables.

6. The latent heat of vaporization of water is higher than the enthalpy of saturated steam.

7. The enthalpy of saturated steam includes the sensible heat of liquid water. 8. The enthalpy of superheated steam includes the sensible heat of vapor. 9. Condensation of superheated steam is possible only after the steam has lost

its sensible heat.

10. The latent heat of vaporization of water increases with temperature. 11. The boiling point of water at certain pressure can be determined from steam

12. Specific volume of saturated steam increases with pressure. 13. The enthalpy of liquid water is greatly affected by pressure.

14. The latent heat of vaporization at a certain pressure is equal to the latent heat of condensation at the same pressure.

15. When steam is condensing, it gives off its latent heat of vaporization. 16. The main reason steam is used as a heating medium is its high latent heat value. 17. About 5.4 times more energy is needed to evaporate 1 kg of water at 1008C

than to heat 1 kg of water from 08C to 1008C.

18. The latent heat of vaporization becomes zero at the critical point.

19. Superheated steam is preferred to saturated steam as a heating medium in the food industry.

20. Steam in the food industry is usually produced in ‘‘water in tube’’ boilers. 21. Water boils at 08C when the absolute pressure is 611.3 Pa

22. Water boils at 1008C when the absolute pressure is 101325 Pa.

23. Steam quality expresses the fraction or percentage of vapor phase to liquid phase of a vapor-liquid mixture.

24. A Steam quality of 70% means that 70% of the vapor-liquid mixture is in the liquid phase (liquid droplets) and 30% in the vapor phase.

25. The quality of superheated steam is always 100%.

Example 2.1

From the steam tables:

Find the enthalpy of liquid water at 508C, 1008C, and 1208C. Find the enthalpy of saturated steam at 508C, 1008C, and 1208C. Find the latent heat of vaporization at 508C, 1008C, and 1208C.

From the column of the steam tables that gives the enthalpy of liquid water read:

Hat508C¼209:33kJ=kg Hat1008C¼419:04kJ=kg Hat1208C¼503:71kJ=kg Step 2

From the column of the steam tables that gives the enthalpy of saturated steam read:

Hat508C¼2592:1kJ=kg Hat1008C¼2676:1kJ=kg Hat1208C¼2706:3kJ=kg Step 3

Calculate the latent heat of vaporization as the difference between the enthalpy of saturated steam and the enthapy of liquid water.

Example 2.2

Find the enthalpy of superheated steam with pressure 150 kPa and temperature 1508C.

Find the enthalpy from the steam tables for superheated steam:

Hsteam¼2772:6kJ=kg

Alternatively find an approximate value from:

Hsteam ¼Hsaturatedþcp vaporðTTsaturationÞ ¼2693:4þ1:909ð150111:3Þ

¼2767:3kJ=kg

Example 2.3

If the enthalpy of saturated steam at 508C and 558C is 2592.1 kJ/kg and 2600.9 kJ/kg respectively, find the enthalpy at 538C.

Find the enthalpy at 538C by interpolation between the values for 508C and 558C given in steam tables, assuming that the enthalpy in this range changes linearly:

H¼2592:1þ5350

5550ð2600:92592:1Þ ¼2597:4 kJ=kg

Exercise 2.1

Find the boiling temperature of a juice that is boiling at an absolute pressure of 31.19 Pa. Assume that the boiling point elevation is negligible.

Exercise 2.2

A food product is heated by saturated steam at 1008C. If the condensate exits at 908C, how much heat is given off per kg steam?

Find the the enthalpy of steam and condensate from steam tables:

Hsteam¼::::::::::::::::::::::::::::::::::kJ=kg;

Hcondensate¼::::::::::::::::::::::::::::::::::kJ=kg:

Calculate the heat given off:

H¼::::::::::::::::::::::::::::::::::::::::::::::::::::¼2299:2 kJ=kg

Exercise 2.3

Find the enthalpy of steam at 169.06 kPa pressure if its quality is 90%. Solution

Find the enthalpy of saturated steam at 169.06 kPa from the steam tables:

Hsteam¼:::::::::::::::::::::::::::::::::::::::::::::::::::

Find the enthalpy of liquid water at the corresponding temperature from the steam tables:

Hliquid¼::::::::::::::::::::::::::::::::::::::::::::::::::::::::

Calculate the enthalpy of the given steam: H¼xsHsþð1xsÞHL¼

¼::::::::::::::::::::::::::::::::::::::þ:::::::::::::::::::::::::::::::::::¼ 2477:3kJ=kg

Exercise 2.4

Find the vapor pressure of water at 728C if the vapor pressure at 708C and 758C is 31.19 kPa and 38.58 kPa respectively.

Exercise 2.5

The pressure in an autoclave is 232 kPa, while the temperature in the vapor phase is 1208C. What do you conclude from these values?

The saturation temperature at the pressure of the autoclave should be ... Since the actual temperature in the autoclave is lower than the saturation temperature at 232 kPa, the partial pressure of water vapor in the autoclave is less than 232 kPa. Therefore air is present in the autoclave.

Exercise 2.6

Lettuce is being cooled by evaporative cooling in a vacuum cooler. If the absolute pressure in the vacuum cooler is 934.9 Pa, determine the final tem-perature of the lettuce.

Mass Balance

Which of the following statements are true and which are false? 1. The mass balance is based on the law of conservation of mass.

2. Mass balance may refer to total mass balance or component mass balance. 3. Control volume is a region in space surrounded by a control surface

through which the fluid flows.

4. Only streams that cross the control surface take part in the mass balance. 5. At steady state, mass is accumulated in the control volume.

6. In a component mass balance, the component generation term has the same sign as the output streams.

7. It is helpful to write a mass balance on a component that goes through the process without any change.

8. Generation or depletion terms are included in a component mass balance if the component undergoes chemical reaction.

9. The degrees of freedom of a system is equal to the difference between the number of unknown variables and the number of independent equations. 10. In a properly specified problem of mass balance, the degrees of freedom

must not be equal to zero.

Example 3.1

How much dry sugar must be added in 100 kg of aqueous sugar solution in order to increase its concentration from 20% to 50%?

Draw the process diagram:

State your assumptions:

l dry sugar is composed of 100% sugar.

Write the total and component mass balances in the envelope around the process:

i) Overall mass balance

100þS2¼S3 (3:1)

ii) Soluble solids mass balance

0:20100þS2¼0:50S3 (3:2)

Solving eqns (3.1) and (3.2) simultaneously, find S2=60 kg and S3=160 kg. Therefore 60 kg of dry sugar per 100 kg of feed must be added to increase its concentration from 20% to 50%.

Example 3.2

10000 kg/h X Y

MIXING EVAPORATION

Write the total and component mass balances in envelopes I and II: i) Overall mass balance in envelope I

10000¼WþX (3:3)

ii) Soluble solids mass balance in envelope I

0:1210000¼0:60X (3:4)

iii) Overall mass balance in envelope II

XþF¼Y (3:5)

iv) Soluble solids mass balance in envelope II

0:60Xþ0:12F¼0:42Y (3:6)

From eqn (3.4) find X=2000 kg/h. Substituting X in eqn (3.3) and find W=8000 kg/h. Solve eqns (iii) and (iv) simultaneously and Substitute X in eqn (3.3) and find=1200 kg/h and Y=3200 kg/h.

Therefore 8000 kg/h of water will be evaporated, 1200 kg/h of fresh juice will be added back and 3200 kg/h of concentrated orange juice with 42% soluble solids will be produced.

Exercise 3.3

and then the slush is separated in a centrifugal separator into ice crystals and concentrated juice. An amount of 500 kg/h of liquid is recycled from the separator to the freezer. Calculate the amount of ice that is removed in the separator and the amount of concentrated juice produced. Assume steady state.

10% FREEZING SEPARATION

1000¼IþJ (3:7)

0:101000¼0:40J (3:8)

From eqn (3.8) find J=250 kg/h and then from eqn (3.7) find I=750 kg/h. Comment: Notice that the recycle stream does not affect the result. Only the streams that cut the envelope take part in the mass balance.

Exercise 3.1

10000 kg/h X

EVAPORATION

Write the mass balance for sugar on the envelope around the process:

0:10X¼::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

Solve the above equation and find

X= ... .. kg/h

Exercise 3.2

How much water must be added to 200 kg of concentrated orange juice with 65% solids to produce orange juice with 12% solids

Write the mass balance for solids on the envelope around the process:

...¼... Solve the above equation and find J=... kg

Milk with 3.8% fat and 8.1% fat-free solids (FFS) is used for the production of canned concentrated milk. The process includes separation of the cream in a centrifuge and concentration of the partially defatted milk in an evaporator. If the cream that is produced in the centrifuge contains 55% water, 40% fat, and 5% fat-free solids, calculate how much milk is necessary in order to produce a can of concentrated milk that contains 410 g milk with 7.8% fat and 18.1% fat-free solids. How much cream and how much water must be removed in the centrifuge and the evaporator respectively? Assume steady state.

FFS 8.1% CENTRIFUGATION EVAPORATION

FFS 18.1% Fat 7.8% Water

::::::::::::::::::::::¼::::::::::::::::::þWþ:::::::::::::::::::::: (3:9)

ii) Fat-free solids mass balance

iii) Fat mass balance

0:038X¼:::::::::::::::::::::::::::::::::þ::::::::::::::::::::::::::::::::: (3:11)

Solve eqns (3.9), (3.10) and (3.11) simultaneously and find X=... g, C= ... g and W= ... g.

Exercise 3.4

According to some indications, crystallization of honey is avoided if the ratio of glucose to water is equal to 1.70. Given the composition of two honeys, find the proportions in which they have to be mixed so that the ratio of glucose to water in the blend is 1.7. What will be the composition of the blend?

Honey H1: glucose 35%, fructose 33%, sucrose 6%, water 16%. Honey H2: glucose 27%, fructose 37%, sucrose 7%, water 19%.

Glucose 27% Fructose 37% Sucrose 7% Water 19%

Glucose 35% Fructose 33% Sucrose 6% Water 16%

Select 1000 kg of blend as a basis for calculation (Hb=1000 kg). Step 3

Write the total and component mass balances in the envelope around the process: i) Overall mass balance

::::::::::::::::::::::::::þ::::::::::::::::::::::::::::¼::::::::::::::::::::::::::::::::: (3:12)

ii) Glucose mass balance

iii) Fructose mass balance

::::::::::::::::::::::::::::þ::::::::::::::::::::::::::::¼:::::::::::::::::::::::::::: (3:14)

iv) Sucrose mass balance

::::::::::::::::::::::::::::þ::::::::::::::::::::::::::::¼:::::::::::::::::::::::::::: (3:15)

v) Water mass balance

::::::::::::::::::::::::::::þ::::::::::::::::::::::::::::¼:::::::::::::::::::::::::::: (3:16)

vi) Ratio of glucose to water in the blend

G=W¼1:70 (3:17)

Solve eqns (3.12) to (3.17) simultaneously and find:

H1¼... kg H2¼... kg H1/H2¼...

The composition of the blend will be:

glucose¼ ... fructose¼ ... sucrose¼ ... water¼ ...

Exercise 3.5

How much glucose syrup with 20% concentration has to be mixed with 100 kg glucose syrup with 40% concentration so that the mixture will have 36% glucose?

Exercise 3.6

How many kg of saturated sugar solution at 708C can be prepared from 100 kg of sucrose? If the solution is cooled from 708C to 208C, how many kg of sugar will be crystallized? Assume that the solubility of sucrose as a function of temperature (in8C) is given by the equation: % sucrose ¼63.2þ0.146T þ

Exercise 3.7

Find the ratio of milk with 3.8% fat to milk with 0.5% fat that have to be mixed in order to produce a blend with 3.5% fat.

Exercise 3.8

For the production of marmalade, the fruits are mixed with sugar and pectin and the mixture is boiled to about 65% solids concentration. Find the amount of fruits, sugar, and pectin that must be used for the production of 1000 kg marmalade, if the solids content of the fruits is 10%, the ratio of sugar to fruit in the recipe is 56:44, and the ratio of sugar to pectin is 100.

Exercise 3.9

For the production of olive oil, the olives are washed, crushed, malaxated, and separated into oil, water. and solids by centrifugation as in the following flow chart. Find the flow rate in the exit streams given that: a) the composition of the olives is 20% oil, 35% water, and 45% solids; b) the composition of the discharged solids stream in the decanter is 50% solids and 50% water; c) 90% of the oil is taken out in the first disc centrifuge; and d) the ratio of olives to water added in the decanter is equal to 1.

2000 kg/h olives

WASHER HAMMER

MILL MALAXATOR DECANTER

DISC CENTRIFUGE DISC

water oil oil

solids water

Energy Balance

The overall energy balance equation for a system with one inlet (point 1) and one outlet (point 2) is:

The overall energy balance equation for a system at steady state with more than two streams can be written as:

where H = enthalpy, J/kg

vm= average velocity, m/s

= correction coefficient (for a circular pipe= 1/2 for laminar flow,1 for turbulent flow)

z = relative height from a reference plane, m m = mass of the system, kg

m = mass flow rate, kg/s

q = heat transferred across the boundary to or from the system (positive if heat flows to the system), W

Ws= shaft work done by or to the system (positive if work is done by the system), W

E = total energy per unit mass of fluid in the system, J/kg t = time, s

In most of the cases, the overall energy balance ends up as an enthalpy balance because the terms of kinetic and potential energy are negligible com-pared to the enthalpy term, the system is assumed adiabatic (Q¼0), and there is no shaft work (Ws¼0). Then:

Which of the following statements are true and which are false?

1. The energy in a system can be categorize as internal energy, potential energy, and kinetic energy.

2. A fluid stream carries internal energy, potential energy, and kinetic energy.

3. A fluid stream entering or exiting a control volume is doing PV work. 4. The internal energy and the PV work of a stream of fluid make up the

enthalpy of the stream.

5. Heat and shaft work may be transferred through the control surface to or from the control volume.

6. Heat transferred from the control volume to the surroundings is considered positive by convention.

7. For an adiabatic process, the heat transferred to the system is zero. 8. Shaft work supplied to the system is considered positive by convention. 9. The shaft work supplied by a pump in a system is considered negative. 10. If energy is not accumulated in or depleted from the system, the system is at

steady state.

Example 4.1

1000 kg/h of milk is heated in a heat exchanger from 458C to 728C. Water is used as the heating medium. It enters the heat exchanger at 908C and leaves at 758C. Calculate the mass flow rate of the heating medium, if the heat losses to the environment are equal to 1 kW. The heat capacity of water is given equal to 4.2 kJ/kg8C and that of milk 3.9 kJ/kg8C.

1000 kg/h milk

HEAT EXCHANGER 45°C

l The terms of kinetic and potential energy in the energy balance equation are negligible.

l A pump is not included in the system (W

l The heat capacity of the liquid streams does not change significantly with temperature.

l The system is at steady state. Step 3

Write the energy balance equation:

Rate of energy input¼m_w inHw inþm_m inHm in

Rate of energy output¼m_w outHw outþm_m outHm out þ q

(with subscript ‘‘w’’ for water and ‘‘m’’ for milk). At steady state

rate of energy input¼rate of energy output or

mw inHw inþm_m inHm in¼m_w outHw outþm_m outHm outþq (4:1)

Calculate the known terms of eqn (4.1) i) The enthalpy of the water stream is:

Input: Hw in ¼cpT¼4:2 90¼378 kJ=kg

Output: Hw out¼cpT¼4:2 75¼315 kJ=kg

ii) The enthalpy of the milk stream is:

Input: Hm in ¼cpT¼3:9 45¼175:5 kJ=kg

Output: Hm out¼cpT¼3:9 72¼280:8 kJ=kg

Substitute the above values in eqn (4.1), taking into account that:

mw in¼m_w out¼m_wandm_m in ¼m_m out

Step 6 Solve form_w

mw¼1728:6 kg=h

Example 4.2

A dilute solution is subjected to flash distillation. The solution is heated in a heat exchanger and then flashes in a vacuum vessel. If heat at a rate of 270000 kJ/h is transferred to the solution in the heat exchanger, calculate: a) the temperature of the solution at the exit of the heat exchanger, and b) the amount of overhead vapor and residual liquid leaving the vacuum vessel. The following data are given: Flow rate and temperature of the solution at the inlet of the heat exchanger is 1000 kg/h and 508C, heat capacity of the solution is 3.8 kJ/kg8C, and absolute pressure in the vacuum vessel is 70.14 kPa.

q HEAT EXCHANGER

VACUUM VESSEL

mL, TL, HL III

T Fo HFo mFi

l The heat losses to the environment are negligible.

l The heat capacities of the liquid streams do not change significantly with temperature and concentration.

Write the energy balance equation in envelope II:

mFiHFiþq¼m_FoHFo (4:2)

mFicpFTFiþq¼m_FocpFTFo (4:3)

Substitute known values:

10003:850þ270000¼10003:8TFo (4:4)

Solve for TFo:

Write the mass and energy balance equations in envelope I: i) Overall mass balance:

mFi¼m_Vþm_L (4:5)

ii) Energy balance:

mFiHFiþq¼m_VHVþm_LHL (4:6)

mFicpFTFiþq¼m_VHVþm_LcpLTL (4:7)

Calculate mvusing equations (4.5), (4.6) and (4.7):

i) From eqn (4.5):

mL¼m_Fim_V (4:8)

ii) Substitute eqn (4.8) in (4.7):

mFicpFTFiþq¼m_VHVþðm_Fim_VÞcpLTL (4:9)

iii) Find the saturation temperature and the enthalpy of saturated vapor at 70.14 kPa from the steam tables:

TL=908C V=2660 kJ/kg iv) Substitute numerical values in eqn (4.9):

v) Solve form_V

mv¼50:9 kg=h

Alternatively, an energy balance in envelope III can be used instead of envelope I:

i) Write the energy balance equation:

mFocpFTFo¼m_VHVþm_LcpLTL (4:10)

ii) Combine eqns (4.5) and (4.10) and substitute numerical values:

10003:8121¼m_V2660þð1000m_VÞ 3:890

iii) Solve form_V

Exercise 4.1

How much saturated steam with 120.8 kPa pressure is required to heat 1000 g/h of juice from 58C to 958C? Assume that the heat capacity of the juice is 4 kJ/ kg8C.

HEAT EXCHANGER

5°C 95°C juice

condensate 120.8kPa steam

120.8kPa mji=1000 kg/h juice

mjiHjiþm_sHs¼:::::::::::::::::::::::::::::::þ::::::::::::::::::::::::::::::::

mjicpjTjiþm_sHs¼:::::::::::::::::::::::::::::::þ::::::::::::::::::::::::::::::::

Substitute numerical values in the above equation.

(Find the enthalpy of saturated steam and water [condensate] from steam tables):

::::::::::::::::::::::::::::þ:::::::::::::::::::::::::::¼:::::::::::::::::::::::::þ::::::::::::::::::::::::

Step 4 Solve form_s

ms¼::::::::::::::::::::::::::::::::::::::kg=h

Exercise 4.2

How much saturated steam with 120.8 kPa pressure is required to concentrate 1000 kg/h of juice from 12% to 20% solids at 958C? Assume that the heat capacity of juice is 4 kJ/kg8C.

EVAPORATOR water vapor

95oC 95oC juice

120.8kPa mji=1000 kg/h

Write the overall mass balance equation on the juice side:

1000¼m_Vþm_jo

Write the solids mass balance equation:

0:121000¼:::::::::::::::m_jo

Solve form_joandm_V

mjo¼:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::kg=h

mV¼:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::kg=h

i) Write the enthalpy balance equation:

mjicpjTjiþm_sHs¼:::::::::::::::::::::::þ:::::::::::::::::::::::::¼:::::::::::::::::::

ii) From steam tables, find the enthalpy of water vapor at 958C, of saturated steam at 120.8 kPa, and of water (condensate) at 120.8 kPa.

iii) Substitute numerical values in the above equation:

::::::::::::::::::þ:::::::::::::::::¼::::::::::::::::::þ:::::::::::::::::þ:::::::::::::::::

iv) Solve form_s

ms¼::::::::::::::::::::::::::::::::::::::::kg=h

Exercise 4.3

given: The temperature of the milk at the inlet of H is 408C, the temperature of the cooling water at the inlet of the condenser is 208C, the steam introduced into the chamber H is saturated at 475.8 kPa pressure, and the heat capacity of the milk is 3.8 kJ/kg8C at the inlet of the infusion chamber and 4 kJ/kg8C at the exit of the infusion chamber.

cooling water

mwo, Hwo mmo, Hmo

mms, Hms ms, Hs

l The water vapor pressure of the milk is equal to that of water at the same temperature.

l The water vapor pressure in the condenser is equal to the water vapor pressure in the flash vessel.

Write the mass and energy balance equations in envelope I: i) Energy balance in envelope I:

mmiHmiþm_sHs¼m_msHms

ii) Overall mass balance in envelope I:

iii) Substitute numerical values and combine the last two equations:

::::::::::::::::::::::::::::::::::þ2746:5m_s¼:::::::::::::::::::::::::::::::::

ms¼::::::::::::::::::::::::::::::::::::::::::::::::kg=h

i) Write the energy balance in envelope II:

:::::::::::::::::::::::::¼:::::::::::::::::::::::::::þ::::::::::::::::::::::::::::

ii) Substitute values taking into account thatm_s¼m_v, in order to avoid

dilution of the milk:

:::::::::::::::::::::::::::::::::¼::::::::::::::::::::::::::::::þ:::::::::::::::::::::::::::::::: or

13891587600T¼395:1HV

iii) Solve the last equation by trial and error to find the value of T that will give a value of HVin agreement with steam tables.

T =. . .. . .. . .. . .. . .. . .. . ...8C. Step 5

Write the overall mass balance and energy balance in envelope III: i) Overall mass balance:

::::::::::::::::::::::::::::þ:::::::::::::::::::::::::::¼:::::::::::::::::::::::::::::::

iii) Substitute numerical values in the last equation and solve for mwi. The temperature of the water at the exit of the condenser must be equal to ::::::::::::::::::::C, because the water vapor pressure in the condenser was assumed equal to that in the flash vessel F.

Exercise 4.4

Find the amount of saturated steam at 270.1 kPa required to heat 100 kg of cans from 508C to 1218C, if the heat capacity of the cans is 3.5 kJ/kg8C.

Exercise 4.5

One ice cube at108C weighing 30g is added to a glass containing 200ml of water at 208C. Calculate the final water temperature when the ice cube melts completely. Assume that 3 kJ of heat are transferred from the glass to the water during the melting of the ice? Use the following values: the latent heat of fusion of the ice is 334 kJ/kg, the heat capacity of the ice is 1.93 kJ/kg8C, and the heat capacity of the water is 4.18 kJ/kg8C.

Exercise 4.6

For quick preparation of a cup of hot chocolate in a cafeteria, cocoa powder and sugar are added in a cup of water and the solution is heated by direct steam injection. If the initial temperature of all the ingredients is 158C, the final temperature is 958C, the mass of the solution is 150g initially, and the heat capacity of the solution is 3.8 kJ/kg8C, calculate how much saturated steam at 1108C will be used. State your assumptions.

Exercise 4.7

1. The Reynolds number represents the ratio of the inertia forces to viscous forces. 2. If the Reynolds number in a straight circular pipe is less than 2100, the flow

is laminar.

3. The velocity at which the flow changes from laminar to turbulent is called critical velocity.

4. The Reynolds number in non-Newtonian fluids is called the Generalized Reynolds number

5. The velocity profile of Newtonian fluids in laminar flow inside a circular pipe is parabolic.

6. The velocity profile of Newtonian fluids in laminar flow is flatter than in turbulent flow.

7. The maximum velocity of Newtonian fluids in laminar flow inside a circular pipe is twice the bulk average velocity.

8. The average velocity of Newtonian fluids in turbulent flow inside a circular pipe is around 80% of the maximum velocity.

9. The maximum velocity of pseudoplastic fluids in laminar flow inside a circular pipe is more than twice the bulk average velocity.

10. The Hagen-Poiseuille equation gives the pressure drop as a function of the average velocity for turbulent flow in a horizontal pipe.

11. The pressure drop in laminar flow is proportional to the volumetric flow rate. 12. The pressure drop in turbulent flow is approximately proportional to the

7/4 power of the volumetric flow rate.

13. In a fluid flowing in contact with a solid surface, the region close to the solid surface where the fluid velocity is affected by the solid surface is called boundary layer.

14. The velocity gradients and the shear stresses are larger in the region outside the boundary layer than in the boundary layer.

15. Boundary layer thickness is defined as the distance from the solid surface where the velocity reaches 99% of the free stream velocity.

16. The viscosity of a liquid can be calculated if the pressure drop of the liquid flowing in a horizontal pipe in laminar flow is known.

17. The viscosity of non-Newtonian liquids is independent of the shear rate. 18. The flow behavior index in pseudoplastic liquids is less than one.

19. In liquids that follow the power-law equation, the relationship between average velocity and maximum velocity is independent of the flow behavior index.

20. The apparent viscosity of a pseudoplastic liquid flowing in a pipe decreases as the flow rate increases.

Example 5.1

Saturated steam at 1508C is flowing in a steel pipe of 2 in nominal diameter, schedule No. 80. If the average velocity of the steam is 10 m/s, calculate the mass flow rate of the steam.

Find the inside diameter for a 2 in pipe schedule No. 80 from table:

Calculate the inside cross-sectional area of the pipe:

pð0:04925mÞ2

4 ¼0:001905 m

Calculate the volumetric flow rate:

Q¼Avaver:¼ ð0:001905m2Þð10m=sÞ ¼0:01905m3=s

Find the specific volume of saturated steam at 1508C from the steam tables: v = 0.3928 m3/kg

Calculate the mass flow rate:

0:01905 m3 = s

Example 5.2

A 50% sucrose solution at 208C is flowing in a pipe with 0.0475 m inside diameter and 10 m length at a rate of 3 m3/h. Find: a) the mean velocity, b) the maximum velocity, and c) the pressure drop of the sucrose solution. The viscosity and the density of the sucrose solution at 208C are 15.43 cp and 1232 kg/m3respectively.

Calculate the cross-section area of the pipe:

pð0:0475mÞ2

Calculate the mean velocity of the liquid:

8:33104 m 3 = s

1:77103 m 2 ¼0:471m=s

Calculate the Reynolds number:

ð0:0475mÞð0:471m=sð1232kg=m3Þ

15:43103 kg = ms ¼1786

Since Re<2100, the flow is laminar and

vmax¼2vm¼20:471m=s¼0:942m=s

Calculate the pressure drop using the Hagen-Poiseuille equation:

32ð0:471m=sÞð0:01543PasÞð10mÞ

ð0:0475mÞ2 ¼1030:Pa

Exercise 5.1

Convert the units to SI:

Q¼5 m3=h¼:::::::::::::::::::::::::::::::::m3=s

D¼2 in¼::::::::::::::::::::::::::::::::::m

m¼1 cp¼::::::::::::::::::::::::::::::::::kg ms

r¼0:998 g=ml¼::::::::::::::::::::::::::::::::kg=m3

A¼::::::::::::::::::::::::::::::::::::::::m2

vm¼:::::::::::::::::::::::::::::::::::m=s

Re¼:::::::::::::::::::::::::::::::::::::::::

For the flow to be laminar, Re must be less than or equal to 2100. i) Calculate the velocity from the Reynolds number:

2100¼ð0:0508mÞðvmÞð998Kg=m 3

Solve for vm:

vm¼::::::::::::::::::::::::::::::::::::::m=s

ii) Calculate the flow rate for the flow to be laminar using vmfound above:

Exercise 5.2

Calculate the Reynolds number for applesauce flowing at 5 m3/h in a tube with 2 in inside diameter if the consistency index is 13 Pa s0.3, the flow behavior index is 0.3, and the density is 1100 kg/m3.

Q¼5 m3=h¼:::::::::::::::::::::::::::::::::::m3=s

D¼2 in¼::::::::::::::::::::::::::::::::::::::::::::m Step 2

A¼::::::::::::::::::::::::::::::::::::::::::::m2

vm¼::::::::::::::::::::::::::::::::::::::::m=s

Since n6¼1, the fluid is non-Newtonian. The Generalised Reynolds number will be:

k ¼::::::::::::::::::::::::::::::::::::::::::::::::::::::::: Exercise 5.3

Olive oil is flowing in a horizontal tube with 0.0475 m inside diameter. Calculate the mean velocity if the pressure drop per meter of pipe is 1000 Pa. The viscosity of olive oil is 80 cp and its density is 919 kg/m3.

Assume the flow is laminar and calculate the mean velocity using the Hagen-Poiseuille equation:

Verify that the flow is laminar:

Re¼:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: Exercise 5.4

Honey at 1 liter/min is flowing in a capillary-tube viscometer 2cm in diameter and 50 cm long. If the pressure drop is 40 kPa, determine its viscosity.

The viscosity can be calculated using the Hagen-Poiseuille equation. Step 1

Find the mean velocity:

::::::¼:::::::::::::::::::::::m=s Step 2

Calculate the viscosity:

¼:::::::::::::::::::::::::Pas

Exercise 5.5

Tomato concentrate is in laminar flow in a pipe with 0.0475 m inside diameter and 10 m length at a rate of 3 m3/h. Find: a) the mean velocity, b) the maximum velocity, and c) the pressure drop of the tomato concentrate. The consistency index and the flow behavior index are K = 18.7 Pas0.4and n = 0.4 respectively. Compare the pressure drop for the sucrose solution of Example 5.2 with the pressure drop of tomato concentrate.

4 ¼:::::::::::::::::m

Use the relationship between mean and maximum velocity for a power-law non-Newtonian fluid in laminar flow to calculate vmax:

nþ1 Therefore

vmax¼:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::m=s

Find the relationship between mean velocity and pressure dropP in laminar flow for non-Newtonian fluids:

vm¼ P 2kL 1=n

where K is consistency index (Pasn), n is flow behaviour index, L is pipe length (m), and R is pipe diameter (m).

Solve for the pressure drop, substitute values, and findP:

P¼::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::Pa

Compare the above pressure drop with the pressure drop calculated for the sucrose solution.

Exercise 5.6

Develop a spreadsheet program to find and plot the velocity distribution as a function of pipe radius for the sucrose solution of Example 5.2 and for the tomato concentrate of Exercise 5.5. Compare the results.

Find the equations for the velocity distribution in laminar flow in a circular pipe i) for a Newtonian fluid:

ii) for a non-Newtonian fluid:

vr¼ P 2KL 1=n

Rðnþ1Þ=n 1 r

Calculate the velocity for various values of radius using the above equations. Step 3

Plot the results.

You must end up with the following figure for the sucrose solution and the tomato concentrate:

0.03 0.02 0.01 0.00 0.01 0.02 0.03

Velocity, m/s

sucrose solution tomato concentrate

0 0.2 0.4 0.6 0.8 1

Compare the results. Exercise 5.7

The mechanical energy balance equation is used to calculate the required power for a pump. The mechanical energy balance equation for a system with one inlet (point 1) and one outlet (point 2) is:

r þðz2z1ÞgþF¼ ws (6:1)

where vm= average velocity, m/s

a= kinetic energy correction coefficient (for a circular pipe¼1=2 for laminar flow,1 for turbulent flow)

P = pressure, Pa r= density, kg/m3

z = relative height from a reference plane, m g = acceleration of gravity, 9.81 m/s2

F = friction losses per unit mass of fluid, J/kg

ws= work supplied by the pump per unit mass of fluid, J/kg The available Net Positive Suction Head (NPSHa) is:

Pp v rg þz1

where P = pressure in the suction tank, Pa

pv= vapor pressure of liquid in the pump, Pa

z1=distance of the pump from the liquid level in the suction tank, m (z1 positive if the pump is below the liquid level in the tank, z1negative if the pump is above the liquid level in the tank)

Fs= friction losses in the suction line, J/kg

1. Mechanical energy includes kinetic energy, potential energy, shaft work, and the flow work term of enthalpy.

2. Mechanical energy cannot be completely converted to work.

3. The fluid pressure drop due to friction in a straight pipe is proportional to the velocity of the fluid.

4. The pressure drop due to skin friction in a pipe can be calculated from the Fanning equation.

5. The friction factor f in laminar flow depends on the Reynolds number and the surface roughness of the pipe.

6. The friction factor f in turbulent flow can be obtained from the Moody chart.

7. In turbulent flow, the higher the surface roughness of the pipe the higher the influence of the Reynolds number on the friction factor f.

8. A sudden change of the fluid velocity in direction or magnitude causes friction losses.

9. Equation 6.1 gives the energy added to a fluid by a pump.

10. The energy added to a fluid by a pump is often called the developed head of the pump and is expressed in m.

11. The required power for a pump is independent of the liquid flow rate. 12. The brake power of a pump depends on the efficiency of the pump. 13. If the pressure in the suction of a pump becomes equal to the vapor pressure

of the liquid, cavitation occurs.

14. Under cavitation conditions, boiling of the liquid takes place in the pump. 15. The difference between the sum of the velocity head and the pressure head in the suction of the pump and the vapor pressure of the liquid is called available net positive suction head (NPSH).

16. To avoid cavitation, the available NPSH must be greater than the required NPSH provided by the pump manufacturer.

17. The higher the temperature of the liquid, the lower the available NPSH. 18. It is impossible to pump a liquid at its boiling point unless the pump is

below the liquid level in the suction tank.

19. Centrifugal pumps are usually self-primed pumps.

20. Positive displacement pumps are usually self-primed pumps.

21. Positive displacement pumps develop higher discharge pressures than cen-trifugal pumps.

22. The discharge line of a positive displacement pump can be closed without damaging the pump.

23. The discharge line of a centrifugal pump can be completely closed without damaging the pump.

25. The flow rate in a positive displacement pump decreases significantly as the head increases.

26. Centrifugal pumps are used as metering pumps. 27. Liquid ring pumps are usually used as vacuum pumps.

28. The capacity of a centrifugal pump is proportional to the rotational speed of the impeller.

29. The head developed by a centrifugal pump is proportional to the speed of the impeller.

30. The power consumed by a centrifugal pump is proportional to the cube of the speed of the impeller.

Example 6.1

A liquid food at 508C is being pumped at a rate of 3 m3/h from a tank A, where the absolute pressure is 12350 Pa, to a tank B, where the absolute pressure is 101325 Pa, through a sanitary pipe 1.5 in nominal diameter with 4:6105m surface roughness . The pump is 1 m below the liquid level in tank A and the discharge in tank B is 3.3 m above the pump. If the length of the pipe in the suction line is 2 m, the discharge line 10 m, and there are one 908 elbow in the suction line, two 908elbows in the discharge line, and one globe valve in the discharge line, calculate the power required, the developed head, and the avail-able Net Positive Suction Head (NPSH). Which of the three pumps that have the characteristic curves given below could be used for this pumping job? The viscosity and the density of the liquid are 0.003 mPas and 1033 kg/m3 respec-tively. The efficiency of the pump is 65%. Assume that the level in tank A is constant.

z2 V 2 , P 2

Level of reference A

Calculate the mean velocity in the pipe: i) Calculate the mass flow rate,m:_

¼0:861 kg=s

ii) Find the inside pipe diameter:

The inside pipe diameter of 1.5 in nominal diameter pipe is 1.402 in

D¼1:402 in0:0254 m

in ¼0:03561 m iii) Calculate the cross-section area of the pipe, A:

pð0:03561 mÞ2

iv) Calculate the mean velocity in the pipe, v:

=ð3600 s=hÞ

ð Þð0:837 m=sÞ1033 kg=m3 0:003 kg=ms

Select two points, points 1 and 2, with known v, P, and z values to which to apply the mechanical energy balance equation. The pump must be between points 1 and 2.

Calculate the frictional losses in the straight sections of the pipe, the elbows, and the valves that are between points 1 and 2:

i. Find the friction factor, f, for straight pipes. The friction factor f can be found from the Moody diagram (see Fig A.1 in the Appendix). If roughnesse¼0:000046m, the relative roughness is:

0:03561 m ¼0:0013

Alternatively, f can be calculated by an empirical relationship e.g., the Colebrook equation:

1:255 Repffiffif

0:0013 3:7 þ

1:255 10263pffiffif

Solving the above equation by trial and error, find f¼0:0082.

ii. Find the equivalent length of a 908standard elbow: Le/D = 32, Equivalent length of straight pipe for 3 elbows:

Le¼3 32Dð Þ ¼3ð320:03561Þ ¼3:42 m

iii. Find the equivalent length of the globe valve: Le/D = 300:

Le¼300D¼3000:03561¼10:68 m

iv. Use the above results to calculate the frictional losses in the straight pipe sections, the elbows, and the valve:

2DL¼40:0082

0:8372 20:03561

m ð12þ3:42þ10:68Þm¼

Units equivalence: m2

m mN s2 N ¼

mJ s2 kgm = s 2 ¼

Calculate the frictional losses in the sudden contraction (entrance from the tank to the pipeline) from:

A2 A1 2 v2 2 2

A2=A1ffi0 since A1A2. Also,¼1 because the flow is turbulent. Therefore,

v2 2 2¼0:55

m2 s2 ¼0:19

Calculate the total frictional losses:

Apply the Mechanical Energy Balance Equation between points 1 and 2 in the diagram. Since the liquid level in the tank is constant, v1= 0:

þðz2z1ÞgþF¼

10132512350 1033

þð3:31Þm9:81m s2þ8:61

¼117:7 J kg

Units equivalence: Pa kg=m3¼

kg ¼ N m2 m3 kg¼ Nm kg ¼ J kg Step 9

Calculate the required power:

W¼ wsm_ ¼117:7

s ¼101:33 J

s ¼ 101:33 W Since the efficiency of the pump is 65%, the required power (brake power) will be:

0:65 ¼155:9 W

Calculate the developed head Hm:

J=kg m=s2¼12:0

Calculate the available Net Positive Suction Head (NPSHa) using eqn (6.2): i. The total pressure in the suction tank is P = 12350 Pa.

iii. The frictional losses in the suction line are:

a) Frictional losses in the straight pipe section and the elbow of the suction line:

The straight pipe section of the suction line is 2 m.

The equivalent straight pipe length of one 908standard elbow for Le/ D=32, as found in step 5, is

Le¼1ð32DÞ ¼320:03561¼1:14 m

20:03561ð2þ1:14Þ ¼1:01 J kg

b) Frictional losses in the entrance from the tank to the pipeline:

hc¼0:19J=kgðas calculated in step 6Þ

c) Total losses in the suction line:

Fs¼hssþhc¼1:01þ0:19¼1:2 J=kg

iv) Substitute values in eqn (6.2) and calculate NPSHa:

1235012349 10339:81 þ1

9:81¼0:88 m

Select the pump:

Find the volumetric flow rate for each one of the pumps A, B, and C for a developed head of 12 m. Find also the required NPSH at the corresponding flow rate:

l Pump A: gives 1.6 m3/h and requires 0.20 m NPSH. Therefore, it does not give the required flow rate of 3 m3/h when the developed head is 12 m.

l Pump B: gives 3.1 m3/h and requires 1.05 m NPSH. Therefore, it gives the required flow rate of 3 m3/h, but requires more NPSH than the available of 0.81 m. If used, it will cavitate.

Developed head, m

0 0 4 8 12 16 20 0 0.5 1 1.5 2 2.5

0 4 8 12 16 20 0 0.5 1 1.5 2 2.5

0 1 2 3 4 5

Exercise 6.1

Water at 208C is flowing in a horizontal pipe 10 m long with 2 in inside diameter. Calculate the pressure drop in the pipe due to friction for a flow rate of 10 m3/h. Solution

Calculate the mean velocity in the pipe:

i) Calculate the cross section area of the pipe:

ii) Calculate the mean velocity in the pipe, v:

v¼:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::m=s

Calculate the Reynolds number (find density and viscosity of water from a table with physical properties of water):

Re¼:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

Calculate the pressure drop from the Fanning equation (since the flow is turbulent): i) Find the friction factor f from the Moody diagram or from Colebrook

f¼:::::::::::::::::::::::::::::::: ii) Calculate the pressure drop:

2 ¼:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::Pa

Exercise 6.2

You have available a 550 W pump with 70% efficiency. Is it possible to use this pump to transfer 10 m3/h of a liquid through a 4.7 cm inside diameter pipe, from one open tank to another, if the liquid is discharged at a point 10 m above the liquid level in the suction tank and the total friction losses are 50 J/kg? The density and the viscosity of the liquid are 1050 kg/m3and 2 cp respectively. Solution

Draw the process diagram. Step 2

State your assumptions.

:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

Select points 1 and 2. Step 4

Calculate the Reynolds number.

i) Calculate the cross section area of the pipe, A:

v¼::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::m=s

iii) Calculate the Reynolds number:

Re¼::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

Since the flow is turbulent,a= ... Step 5

Apply the Mechanical Energy Balance Equation between points 1 and 2.

ws¼::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

¼:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::¼149 J kg

Calculate the power.

Ws¼ wsm_ ¼::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::W

For a 70% pump efficiency, the required power (brake power) will be: WsR¼

::::::::::::::::::

::::::::::::::: ¼622 W

Since the required power is higher than the available 550 W, the pump is not suitable for this pumping job.

Exercise 6.3

A power-law fluid with consistency index K = 0.223 Pa s0.59, flow behavior index n = 0.59, and densityr= 1200 kg/m3is pumped through a sanitary pipe having an inside diameter of 0.0475 m at a rate of 5 m3/h from a tank A to a tank B. The level of the liquid in tank A is 2 m below the pump, while the discharge point is 4 m above the pump. The suction line is 3 m long with one 908elbow, while the discharge line is 6 m long with two 908elbows. Calculate the developed head and the discharge pressure of the pump. It is given that the pump is a self-priming pump.

Level of reference

Calculate the Reynolds number. i) Calculate the mass flow rate,m:_

m¼Qr¼:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::kg=s ii) Calculate the cross-section area of the pipe, A:

A¼::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::m2 iii) Calculate the mean velocity in the pipe, v :

v¼::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::m=s iv) Calculate the Generalized Reynolds number:

8n1 3nþ1 4n

ð Þ0:59ð::::::::::::::::m=sÞ20:59::::::::::::::::::kg=m3 :::::::::::::::::::::::::::::::::::::::::::::::::::::

Calculate the frictional losses in the straight pipe sections, the elbows, and the valve.

i) Find the friction factor f.

n0:75log10 ReGð Þf 1n

n1:2 ¼::::::::::::::::::::::::::::::

:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

ii) Find the equivalent length of a 908standard elbow: Le=D¼32

Equivalent length of straight pipe for 3 elbows: L e ¼:::::::::::m

iii) Find the equivalent length of the globe valve: Le=D¼300

Equivalent length of straight pipe for 1 globe valve L e ¼:::::m

iv) Calculate the frictional losses in the straight pipe sections, the elbows, and the valve:

hs¼:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: J kg

Calculate the frictional losses in the sudden contraction.Since the flow is laminar, the kinetic energy correction coefficient is:

a¼ð2nþ1Þð5nþ3Þ

3 3nð þ1Þ2 ¼:::::::::::::::::::::::::::::::::::::::::::::::

hc¼::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

Calculate the total frictional losses.

Ft¼::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: J kg

¼:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::¼

¼::::::::::::::::::::::::::::::::::::::::::::::::: J kg

Calculate the required power.

Ws¼ wsm_ ¼::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: W

For a 70% pump efficiency, the required power (brake power) will be:

Z ¼::::::::::::::::::::::::::::::::::::::::::: W Step 7

Calculate the developed head Hm.

g ¼:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::m Step 8

Calculate the discharge pressure.Apply the Mechanical Energy Balance Equa-tion (MEBE) between points 2 and 3 with v2 ¼v3, z3¼0, and ws¼0:

r þz2gþFd where Fd= the friction losses in the discharge line.

P3¼::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::¼

¼::::::::::::::::::Pa

Exercise 6.4

Study the spreadsheet program given inPump.xlsto get familiar with the way the program works. Modify the spreadsheet program given inPump.xlsto solve Example 6.1:

b) if the pump is 1 m above the liquid level in the suction tank (is this pumping possible?); and

c) If the required NPSH is 1 m and the absolute pressure in tank A is 101325 kPa, how many meters below the pump could the suction level be without having cavitation problems? If the pump was pumping water at 218C from a well, how many meters below the pump could the suction level be without having cavitation problems?

Exercise 6.5

Solving problems in food engineering

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  • Conversion of units.- Use of steam tables.-Mass balance.- Enthalpy balance.- Mechanical energy balance.- Fluid flow.- Selection of a pump.- Heat transfer by conduction (single and multiple-layer flat and cylindrical wall).- Heat transfer by convection (calculation of heat transfer coefficient, overall heat transfer coefficient, area of heat exchanger).- Heat transfer by radiation.- Unsteady state heat conduction.- Mass transfer by diffusion.- Mass transfer between phases.- Unsteady state mass transfer.- Evaporation.- Psychometrics.- Drying.- Freezing.- Microwave heating.- Separation in gravity and centrifugal fields.- APPENDICES.- Tables of physical properties.- Diagrams.- Steam tables.- Correct answers to the theoretical questions.
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Engineering solutions for food-energy-water systems: it is more than engineering

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  • Published: 27 January 2016
  • Volume 6 , pages 172–182, ( 2016 )

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  • M. L. Wolfe 1 ,
  • K. C. Ting 2 ,
  • N. Scott 3 ,
  • A. Sharpley 4 ,
  • J. W. Jones 5 &
  • L. Verma 6  

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Food, energy, and water systems interact extensively, giving rise to the term “food-energy-water (FEW) nexus,” with the term “nexus” signifying connectedness and interrelationships. A systems approach involving multidisciplinary and transdisciplinary teams and partnerships is needed to address complex challenges of the nexus. A concurrent cyber-physical framework comprised of systems informatics, information analysis methods and tools, and systems analytics and decision support could provide a viable approach for addressing FEW system challenges. A fundamental requirement for implementing the framework is data. Needed data are often difficult to obtain; for example, while much agricultural production system data are collected, the data are not generally available. A priority for addressing FEW system challenges must be development of mechanisms for widespread curation and sharing of data; a few such efforts are underway. Implementing the framework also requires many collaborations. Creating new collaborations among multiple disciplines and organizations to implement the framework could be aided by convergence thinking, which engages approaches to problem solving that transcend disciplines and integrates knowledge from the physical, biological, social, and mathematical sciences and engineering to form comprehensive and integrated thinking at the interfaces of areas. A variety of organizations, private and public, can help in facilitating collaboration and partnerships among the disciplines. Government agencies, industry, academia, and professional societies can all play significant roles in furthering collaboration to address challenges in integrated FEW systems using a systems approach.

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Introduction

Agricultural and biological engineers (ABEs) strive to ensure that the necessities of life are provided in a sustainable manner (ASABE 2015 ). They apply engineering principles to processes associated with managing natural resources and producing agriculturally based goods. Specifically, ABEs

Develop solutions for responsible, sustainable uses of natural resources (soil, water, air, and energy) and agricultural products, by-products, and wastes.

Devise practical, efficient solutions for producing, storing, transporting, processing, and packaging agricultural products.

Solve problems related to systems, processes, and machines that interact with humans, plants, animals, microorganisms, and biological materials.

While ABEs bring strong systems thinking and problem solving abilities to bear, they recognize that complex challenges, such as those in food-energy-water (FEW) systems, require involvement of many different interests and fields of expertise. While interdisciplinary (integrated concepts and methods from different disciplines) work has taken place for many years, the increasing complexity of global challenges related to food, energy, and water requires a more concerted, broader effort to develop and implement a systems approach that involves all the needed actors. Two approaches that incorporate and reach beyond interdisciplinarity are transdisciplinarity and convergence thinking. Transdisciplinarity integrates the different types of knowledge that scientists and practitioners have such that both groups benefit from a mutual learning process (Scholz and Steiner 2015 ). For example, The Global Transdisciplinary Phosphorus Management project brought together scientists from various disciplines with practitioners (producers, phosphorus users, sewage plant operators, public agencies, NGOs, etc.) to work toward the common goal of sustainable P management (Scholz et al. 2015 ). Similar to transdisciplinarity, convergence thinking involves stakeholders beyond academic disciplines and engages persons from corporate, public, and private sectors, as well as academia, in bringing different technologies and sciences together for design and development of solutions.

Members of the American Society of Agricultural and Biological Engineers (ASABE) proposed a symposium at the 16th National Conference and Global Forum on Science, Policy, and the Environment: The Food-Energy-Water Nexus ( http://foodenergywaternexus.org/ ) with the goal of initiating a conversation among ABEs, agricultural scientists, physical scientists, social scientists, and practitioners about working together to solve problems in integrated FEW systems. Specifically, we want to explore different approaches for developing and sustaining partnerships and collaborations focused on addressing challenges in the FEW nexus. While it is easy to say that partnerships and collaborations are needed, it can be difficult to initiate those partnerships and even more difficult to sustain them.

In this paper, we propose a concurrent cyber-physical framework approach to the FEW system. Data acquisition and accessibility are central to the proposed framework, and obtaining appropriate data across a variety of systems is a significant challenge in implementing such a framework. We illustrate this challenge with respect to agricultural production and natural resources systems and describe ongoing efforts to assemble research data. We believe that such an effort can be a model for data for other components of FEW systems. The proposed framework could be utilized for the overall FEW system or its specific components. As examples, we consider two specific systems of different scales: sustainable communities and the role of a particular element (phosphorus (P)) in FEW security. Lastly, we explore approaches for bringing together multiple interests and disciplines within the systems framework to address FEW challenges. We propose roles for professional societies, universities, government agencies, and industry.

Systems approach for problem solving and decision making

Systems analysis has long been used in problem solving and in providing analytical information to support decision making. One benefit of a systems approach is enabling the generation of value-added insights. A systems approach not only can be applied to provide decision support and solutions but can also be used to identify critical research questions. Successful applications often become foundations for further success in applying a systems approach; those successes provide valuable tools and information for addressing challenges in the implementation of a systems approach, such as (1) identifying systems leaders, i.e., task leaders who are familiar with the systems approach and systems experts who are able to lead; (2) abstracting systems, i.e., clearly defining and communicating the scope and objectives of the system under study; (3) handling uncertain and incomplete data and information; (4) delivering outcomes of analysis that are useful to the target audience; (5) enabling productive dynamics of contributors and participants, i.e., the ability to map the actions made at the component and subsystem level to the performance at the overall system level, as well as the distribution of responsibilities and sharing of credit; and (6) assembling and deploying human, information, fiscal, and physical resources. Past efforts have provided a wealth of information on systems theories and methodologies, and the technology readiness level is high for various systems approach tasks.

The following examples illustrate the successful use of a systems approach in a range of applications. Fang et al. ( 1990 ) applied a systems approach to develop a methodology and computational model for optimizing resource allocation in commercial greenhouse operations. The goal was to achieve annual economic returns, specifically for potted plant production systems, by considering the spatial and temporal factors. A second example focused on regenerative life support systems to satisfy the critical needs of air, water, and food supplies, as well as waste treatment and resource recovery, to support human space exploration over a long period of time. Such a life support system encompasses crew members, crop production, food preparation, waste processing, and resource recovery. A systems analysis and modeling effort were made to analyze each of the subsystems and their interactions to investigate the sustainability of life support systems (Fleisher et al. 2006 ; Ting et al. 2003 ).

The goal of a third project was to develop engineering solutions and machinery for successful production and provision of biomass feedstock using dedicated energy crops. The deliverables from the project include operating machinery design and prototypes, scientific information and engineering data, computational platforms, and decision support tools. Research was conducted on the following system tasks: (1) pre-harvest crop monitoring, (2) harvesting, (3) transportation, (4) storage, and (5) systems informatics and analysis. Models were used to investigate impact of size reduction and compression of biomass on supply chain costs and to study how long-term decisions interact with short-time decisions. Visualization tools and vehicle dispatch schemes were developed to display model results and optimize vehicle operating schedules (Shastri et al. 2011 , 2014a , b ; Lin et al. 2014 ).

Food-energy-water systems

Food, energy, and water systems are inextricably linked, giving rise to the term FEW nexus, with the term “nexus” signifying connectedness and interrelationships. Agricultural systems for food production are globally connected. Water and energy are necessary resources for food production. However, agriculture can have both beneficial and detrimental impacts on water quality and use efficiency, as well as on energy demand and supply. Global agricultural uses accounted for approximately 70 % of the world’s freshwater withdrawals in 2007 (Global Agriculture 2014 ). In 2010, agricultural use accounted for about 38 % of total freshwater withdrawals in the USA (Maupin et al. 2014 ). Some parts of the world have excess rainfall that requires agricultural fields to be drained using engineered drainage systems, while other areas require engineered irrigation methods to support crop growth. In either case, the quality of water can be altered by the use of chemicals in agricultural production. As in many other industries, energy is needed to enable various agricultural tasks. On the other hand, agriculture can also be a producer of energy, such as the conversion of dedicated energy crops or agricultural residues and wastes into liquid fuel or thermal-electric power.

If we are to increase crop yields, create new crop varieties, develop new, cost-beneficial animal production systems, reduce greenhouse gas (GHG) emissions, and use less water and energy, we need to apply holistic thinking embodied in the convergence of science and technology. This way of thinking will look at issues of both large commercial operations and smallholder farmers as well as organic and conventional farming with a partnership including the numerous players from producers, academics, government, industry, and consumers.

Convergence thinking (NRC 2014 ), the application of insights and approaches from seemingly distinctly different disciplines, could be a key in developing fundamental ways to create new solutions for “big” problems in FEW systems. Convergence thinking engages approaches to problem solving that transcend disciplines and integrates knowledge from physical, biological, social, mathematical, and engineering sciences to form comprehensive and integrated thinking at the interfaces of areas. This thinking will focus on creation of new collaborations from academia, industry, government, foundations, national laboratories, and a diverse set of stakeholders from producers to consumers. A key concept of the convergence process is not only assembling the expertise but also the formation of a web of partnerships to transform research into practice.

To put these concepts into practice and address the challenges of the very complex FEW system, we must develop a systems approach that facilitates the following: (1) understanding of the interfaces among the components and of the whole system; (2) participation of a range of disciplinary experts, practitioners, and stakeholders in transdisciplinary processes to contribute and utilize information and knowledge; (3) provision of actionable decision support for a wide range of users, including scientists, engineers, policy makers, practitioners, etc.; and (4) inclusion of feedback, feedforward mechanisms for measurement, continuous improvement, and predictive power. In the following section, we propose a framework for such an approach.

Proposed concurrent cyber-physical framework for FEW systems

A modern FEW system needs to be an intelligence-empowered system that includes capability for information collection, information processing, and decision making; mechatronics devices for sensing, controls, and actions; and synergistic integration of components into functional systems. FEW system activities incorporate actions taken by many players in physical spaces. Ideally, these actions should be supported and guided by the intelligence obtained from analyses of information in cyber spaces. A cyber system consisting of effective content and efficient delivery methods will be very valuable in empowering farmers, manufacturers, consumers, and policy makers in their decision making (Ting 1997 ; Shastri et al. 2013 ).

Intelligence-enabling information technologies that can potentially empower FEW systems analysis, planning, design, management, and operation include (1) perception using sensing and data acquisition and management technologies (e.g., internet of things); (2) reasoning and learning involving mathematical, statistical, logical, and heuristic methodologies, handling of incomplete and uncertain information, and data mining (e.g., big data); (3) communication considering the contents, sources and recipients, and delivery platforms including wired, wireless, local area networks, wide area networks, the internet, and mobile technologies and devices (e.g., the nerves of information systems); (4) task planning and execution that involves control logic, planning of physical tasks, intelligent machines, robotics, and flexible automation work cells (i.e., physical capacity); and (5) systems integration to provide computational resources and capabilities of systems informatics, modeling, analysis, decision support, design and specifications, logistics and model-based control, concurrent science, engineering, and technology, and implementation (i.e., cyber-physical systems) (Chen et al. 2015 ).

Currently, systems integration is arguably the weakest aspect when addressing the nexus of FEW systems. An FEW systems concurrent analysis platform may be created based on the above-stated concept and framework using current and emerging informatics, analytics, and computational technologies (Ting and Partlow 2015 ). This cyber platform will facilitate the necessary systems integration tasks for sharing information among interested participants, conducting efficient analyses of local and global level issues, and creating value-added information to support decisions and actions. Human users are the center of this networking platform. As alluded to above, three key dimensions in the platform are coupled to provide the users with desirable utilities:

Systems informatics —This contains the data, information, knowledge, and wisdom (i.e., intelligence) that are necessary for addressing the issues and/or deriving solutions. It also has an effective intelligence management method that enables identification of the source of intelligence, as well as the collection, sorting, storing, and retrieving of the information. Specifically, this part of the platform defines the FEW system’s scope and objectives, identifies system constraints, establishes indicators of success, conducts system abstraction, and obtains and manages data.

Information analysis methods and tools —This provides analytical capability to process information mathematically, statistically, logically, heuristically, etc. The purpose is to seek new and/or integrated meanings of the information stored within the platform and/or entered by the users. It is expected to include various forms of computer simulation and optimization models and the ability to make the models work together to solve the problems that cannot be handled by individual models. The information processing tools should ideally come with the underlying assumptions, scopes of applicability, ways of handling incomplete and uncertain information, and their verification and validation.

Systems analytics and decision support —This is the part of the platform that returns the users the deliverables of the analyses. The first two parts enable the understanding and investigation of scenarios within FEW systems. Analytics are the outcomes of analyses presented in ways that measure and compare the status and performance of the system under consideration, as well as provide insights on what - ifs. The conclusions of analyses may be useful in supporting decision making, planning and executing actions, communicating analytical outcomes, and carrying out continuous monitoring and improvement.

Clearly, an FEW system’s concurrent analysis platform will require many participants from a wide range of disciplines to make it function to its fullest extent. However, it is possible to start showing its usefulness when a critical amount of information, analytical tools, and actionable information become available and continue to evolve. The key is to systematically take necessary steps to involve key participants and configure a concurrent analysis platform structure that has the potential to be scaled up and scaled out.

We are not proposing that there will be one centrally managed entity that hosts and manages the entire system. Rather, we envision a community-wide design and development that will be distributed with connections designed for components as they are needed. The structure must be extensible, allow for innovative changes, and designed for evolution. Standards and protocols for a concurrent analysis platform structure need to be established; this could be done with the facilitation of professional organizations to work across disciplines. To some extent, this is as much a social behavior issue as a technical and scientific issue.

Although a number of examples could be cited that have some of the characteristics presented above, most decision support systems in agriculture have been rather narrow in scope, short-lived, and/or locally applicable. Here, we point out two worthy examples that are broad in their geographical coverage, include many actively contributing disciplinary scientists and partners, and are continually evolving in a distributed, participatory way. These examples could provide valuable insight for configuring a concurrent analysis platform structure that could be scaled up and scaled out.

One example is an information and decision support system in the southeast USA (including Florida, Alabama, Georgia, South Carolina, and North Carolina) that was started about 15 years ago. It integrates weather, crop, disease, water/drought, and carbon and water footprint tools using databases that were brought together from different sources with models and algorithms for providing decision support to farmers and their advisors (Fraisse et al. 2013 ; www.AgroClimate.org ). This system is now widely used in Florida by farmers, extension personnel, and water utilities. For example, it is widely used by strawberry growers in central Florida for managing fungicide applications for two major diseases in this crop. The growers have demonstrated that fungicide applications can be reduced by about 50 % during drier years, which reduces costs of production and use of chemicals ( www.usda.gov/oce/forum/2015_Speeches/CFraisse.pdf ). This integrated system was designed to be extensible and continues to serve as a platform for incorporating additional databases and decision tools. It is being adapted for use in South America and Africa in addition to other US states (e.g., www.wfo-oma.com/climate-change/case-studies/decision-support-system-for-risk-reduction-in-agriculture-agroclimate-paraguay.html ).

The Agricultural Model Intercomparison and Improvement Project (AgMIP; www.agmip.org ), described briefly later, is a second example operating globally. Its strength is that it is a community of systems scientists and engineers involved in research in climate, economics, agronomy, soil physics, hydrology, livestock, plant diseases, sociology, computer science, and other disciplines. More than 30 projects are contributing to this effort. Emerging from this initiative are harmonized models and databases, protocols for intercomparing models, assessments of impacts and trade-offs among systems, and a number of high-impact journal articles (e.g., Rosenzweig et al. 2013a , b ; Nelson et al. 2013 ; Antle et al. 2015 ).

Neither of these examples would have been successful without very strong contributions and joint development by scientists and engineers from a number of different disciplines. Whereas both of these examples demonstrate convergence of disciplines, they do not necessarily demonstrate convergence of technologies themselves, although AgroClimate.org includes a convergence of information and communications technologies and a network of distributed sensors, using the web as well as smart phones to communicate DSS information to farmers ( www.climate.gov/news-features/decision-makers-toolbox/managing-agricultural-climate-risks-us-southeast ).

Data: the foundation of systems modeling, analysis, and understanding

There is a critical unmet need regarding data for developing, evaluating, improving, and applying agricultural models to study production systems at different scales. We currently do not have capabilities for accessing and using the very best quality data across time and space that agriculture is collecting. Researchers at land grant universities, the USDA, and other institutions perform thousands of experiments every year and collect accurate data on soil, weather, management, and crop and livestock performance. These data are used by researchers to compare new management systems, to evaluate ways of more efficient resource utilization (land, water, energy), and to limit environmental (air, soil, and water) contamination. However, these data are virtually lost after researchers use them, eliminating practical discovery, access, and use for evaluating and improving agricultural system models.

There needs to be a change in the culture regarding agricultural research data (Janssen et al. 2015 ). These data should serve as the foundation for evaluating and improving models and for providing evidence on the reliability of models for applications to major society issues, such as climate change and the challenges we face in feeding over nine billion people with limited land, water, and energy resources. There is an experiment station initiative now (with 13 land grant universities) to develop a National Agricultural Research Data Network for Harmonized Data (NARDN), with contributions from Kansas State University, Cornell University, University of Florida, other major universities, and the USDA Agricultural Research Service. The goal of this effort is to develop a prototype network over the next 5 years, with the main hub in the Ag Data Commons database at the National Agricultural Library ( https://data.nal.usda.gov/ ) and with over 50,000 sets of research data. This project is also working with the CGIAR Consortium Office ( www.cgiar.org/cgiar-consortium/ ), Bioversity International ( www.bioversityinternational.org/ ), and the International Center for Tropical Agriculture (CIAT) ( www.agtrials.org/ ) to enable connectivity globally.

In the USA, however, this system is only a start. All land grant universities need support to enable them to create local capabilities to help researchers working on agricultural systems research store their data in this network after publication. National funding to land grant experiment stations could help create a new culture for preserving valuable data and create a virtual laboratory where researchers can conduct a variety of modeling and analytics projects using data from different locations and years within the network. This effort could help the next generation of researchers gain invaluable experience by working with the data in the NARDN that would not be possible working only with site-specific datasets. Agricultural researchers and administrators at all levels need to recognize this critical need and collectively support a coordinated effort at national, state, and local levels.

FEW systems examples

FEW systems can be defined at a wide range of scales, with an accompanying range of components. In this section, we explore the components of two very different types of systems with respect to the proposed FEW systems concurrent analysis platform. First, we consider sustainable communities, for which well-functioning FEW systems are essential, along with other related characteristics. Second, we consider the role of phosphorus in FEW system stability and security.

Sustainable communities

In the context of today’s depressed economies, aging infrastructure, shifting demographics, environmental stresses, changing climate, and uncertain energy prices and availability, the need to plan for the long-term resiliency of communities is increasingly vital to ensure future growth and success. Community health and well-being depend not only on meeting economic, social, and environmental objectives but, more importantly, on integrating them. Future community development approaches that are rooted at the neighborhood level and driven by sustainability can at once address local development needs while ensuring fair opportunities for both current and future residents.

A transdisciplinary approach will be required to successfully address the triple bottom line of economic, social, and environmental development objectives and achieve long-term results. A diverse group of stakeholders from community, public, academic, and private partners must be brought together to engage with one another and discuss the needs of the local community. Throughout this effort, sustainable options and opportunities should be evaluated with attention to maintaining balance between potential economic, social, and environmental outcomes. The following definition of sustainable development may provide guidance in these efforts:

Sustainable development is a process of change in which the direction of investment, the orientation of technology, the allocation of resources, the development and functioning of institutions, and the advancement of human and community well-being meet present needs and aspirations without compromising the ability of future generations to meet their own needs and aspirations (adapted from Brundtland 1987 ).

Thus, sustainable development is a “process” of redirection, reorientation, and reallocation, i.e., an evolving concept rather than a fixed definition. It is a fundamental design or redesign of technological, economic, and sociological processes to address change. A vision of sustainable community revitalization begins with identifying key elements for a sustainable community. For example, Cloutier et al. ( 2014 ) included nine subsystems in the Sustainable Neighborhoods for Happiness Index that they developed to assess the relative status of communities with respect to development and happiness. Stakeholders could consider those nine components and identify the characteristics of each component that would describe a sustainable community (Table 1 ).

The most effective efforts will integrate these components (Table 1 ) through a systems-based approach, beginning at the neighborhood level and using place-based strategies contextually growing from, and enhancing, each community’s identity and capacity. They will emerge from a full understanding of existing development needs and be scalable in their approach, making use of the extant and potential assets and resources of cities and regions. It is important to emphasize that this approach is scalable from a small rural community to neighborhoods within small cities or large cities.

The role of phosphorus in FEW system stability and security

Phosphorus (P) is an essential element for crop and livestock production. Over the past 50 years, global fertilizer P use has increased 350 %, and food production has more than doubled (Khan et al. 2009 ). Along with this, however, global flows of P have increased fourfold (Childers et al. 2011 ; Haygarth et al. 2014 ), with distinct areas of grain and animal production functioning in geographically disparate, yet cost-efficient systems. The main consequence of this uncoupling of production systems has been a one-way transfer of P (as feed, fertilizer, and manure) to localized grain and livestock production and human consumption and a reduction in the efficiency of P reuse. In fact, 80 % of the P mined from phosphate rock does not make it to food consumed by the global population (Neset and Cordell 2012 ), with only 10 % in human wastage recycled back to agricultural lands (Elser and Bennett 2011 ). This production system intensification and decoupling have exacerbated the risk of P loss to water and associated eutrophication (Kleinman et al. 2015 ; Sharpley and Jarvie 2012 ).

These inefficiencies of P utilization are of increasing concern for three reasons. First, unlike nitrogen (N), which is a renewable atmospheric resource, phosphate rock is a finite, non-renewable resource, with economically extractable supplies that are geographically limited (Jasinski 2015 ), that cannot be manufactured or substituted by any other element. Second, the increasing incidence and severity of surface water eutrophication and associated harmful algal toxic blooms worldwide have recently started to impact general urban populations (Carpenter 2008 ; Schindler et al. 2008 ). Third, mandates to expand biofuel production in every continent to increase future national energy security and reduce reliance on fossil fuels have added pressure on P fertilization of biofuel feedstocks (Hein and Leemans 2012 ). Biofuel feedstocks, such as sugarcane, wheat, corn, and sugar beet for bioethanol production and rapeseed, soybean, and palm oil for biodiesel production, now compete for land, water, and P use for food production. These pressures can increase the risk of P loss to surface waters (i.e., affecting water security) and grain prices with competition for food or fuel (i.e., affecting food security) (Robertson et al. 2008 ; Tilman et al. 2009 ). Clearly, P is a key element to the stability and security of the FEW nexus (Jarvie et al. 2015 ).

Efforts to stabilize the role of P in the FEW nexus will involve more than engineering. Solutions will involve embracing the “5 R’s” of P use and management: restructuring of production systems, realignment of system inputs of P to increase utilization of P sources, recovery of P from waste, reuse of P from manures and residuals, and reducing P loss through targeted precision conservation (based on Schoumans et al. 2015 ; Sharpley et al. 2015b ). This is a holistic update of the “4R” nutrient management stewardship (right form, right time, right place, and right amount) espoused by the International Fertilizer Association (IFA 2009 ) and International Plant Nutrition Institute (IPNI 2014 ).

Experience tells us that there needs to be a general restructuring and realignment of production systems to more closely connect crop and livestock operations, which includes a maximum threshold level of P use and a minimum level of land conservation that avoids risky practices on vulnerable landscapes. In extreme cases of highly vulnerable landscapes, certain production systems may be inherently unsustainable, regardless of the suite of conservation practices used or conservation measures adopted. Precision conservation and nutrient management programs can address P source realignment (e.g., rate, method, and timing of applied P) and reduce P loss through transport controls (e.g., conservation tillage, contour ploughing, cover crops, and riparian buffers) to achieve the required improvements in water quality and security.

A greater recovery and reuse of P at global, regional, local, and even farm scales can alleviate phosphate rock supply and security concerns to a certain degree. The fertilizer value of P in manures and urban and other by-products should be properly accounted for with standardized analytical methods in watershed management and strategy implementation planning. This could include development of innovative cost-effective technologies and practices for manure processing and production of higher value recycled products. However, their initial use in agricultural production systems will likely be encouraged with financial incentives, along with stricter use requirements or regulations.

In reducing P loss, lessons from the indirect consequences or trade-offs of conflicting strategies must be learned and management strategies adapted. For example, no-till conservation has dramatically decreased erosion and associated P loss, although the loss of P in dissolved, more immediately reactive form without the incorporation of applied P can reverse total P gains and be sufficient to stimulate algal blooms (Richards et al. 2010 ; Sharpley and Smith 1994 ; Tiessen et al. 2010 ). Another trade-off resulting from the cultivation of new lands fueled by corn for bioethanol that is facilitated by tile drainage will directly connect new source areas to stream and ditches, indirectly increasing the potential for P loss (Smith et al. 2014 , 2015 ). Clearly, there needs to be a more effective communication and coordination among all involved in agricultural production, policy development, and strategy implementation.

Finally, ongoing development of nutrient criteria for waters of the USA should address what is achievable and affordable, given that pristine “reference” conditions may not be achievable in some watersheds with intensive agricultural production (Scott and Haggard 2015 ; White et al. 2014 ). Concurrent with this, cost-benefit analyses of nutrient reduction strategies are necessary to determine what is achievable, affordable, and even desired by the majority of watershed stakeholders.

Facilitating and promoting partnerships and collaboration

Scott et al. ( 2016 ) suggest how convergence can be adopted and applied within agriculture, food, and natural resources systems (AFNS), including FEW systems. They contend that we need to create teams that address the complex problems of AFNS with approaches of convergence through, for example, emerging platforms of nanotechnology, biotechnology, information science, and cognitive science.

We need to create teams of individuals that pursue research, education, and outreach with a lens of convergence thinking. This effort can also catalyze stakeholders to identify the emerging and most critical topics. In contrast to the Coordinated Agricultural Projects (CAPs) of the recent past funded by the USDA-NIFA Agriculture and Food Research Initiative (AFRI; http://nifa.usda.gov/afri-regional-bioenergy-system-coordinated-agricultural-projects ) that promoted large multiinstitutional projects, individual campuses need to mobilize the talent across the campus to engage scientists and engineers who commit to work as a team to study the problem as a convergence of technologies. Physicists, chemists, plant scientists, animal scientists, engineers, food scientists, computer scientists, biologists, social scientists, and economists are examples but are not meant to limit the expertise of team participants. The team will reach out to include a broad spectrum of persons from industry, governments, producers, and consumers.

While there are good examples in which teams of researchers from different disciplines have collaborated closely in empirical studies in the field, fewer examples exist in which those disciplines have worked together to develop and evaluate system models that are critical to the study of the FEW nexus. For example, engineers have cooperated with agronomists, plant physiologists, soil scientists, and economists in research on cropping system models. These collaborations have been important; however, there is at least one discipline that has not typically been cooperating with agricultural system modelers, yet it has a major role in the design of cropping systems. Plant breeders and geneticists have largely been doing their work on plant selection, genomics, and genetic engineering with little, if any, input to agricultural models. One notable exception is the Global Futures project of the CGIAR, led by the International Food Policy Research Institute (IFPRI). In that project, plant breeders have worked with agronomists, crop modelers, and economists to quantify target breeding traits for simulating “virtual genotypes” and evaluating their impacts on food production, trade, and food security globally (Rosegrant et al. 2014 ). Furthermore, a research team of crop modelers, geneticists, plant breeders, and agronomists at the University of Florida has worked with CIAT to show that it is now possible to incorporate genetic information into crop models to increase their reliability and ability to mimic variations across genetics and environments (e.g., Chenu et al. 2009 ; Boote et al. 2016 ; Messina et al. 2006 , 2011 ). This progress clearly demonstrates the need to broaden the typical disciplines working on crop and livestock models to include breeders and geneticists to create a next generation of food production models.

The AgMIP was developed to foster these types of interactions in order to greatly increase our capabilities to understand agricultural systems and to predict their performance (Rosenzweig et al. 2013a , 2014 ; Asseng et al. 2013 ). Although the main emphasis of AgMIP has been to evaluate and improve models for climate change impact and adaptation studies, it has evolved to be a more holistic community of agricultural systems modelers and now serves as a platform for many initiatives with connections to many universities globally.

Universities can play a significant role in bringing teams together both within their individual campuses and across universities and organizations, e.g., by providing facilitators, incentives, and financial support to teams. Universities also have a key role in providing curricula to produce graduates with skill competencies to address complex challenges in FEW systems. These skills need to include disciplinary skills as well as collaborative skills, which will facilitate transdisciplinarity and convergence thinking. Currently, there tends to be an emphasis on competition rather than on collaboration in advanced education, so there is a tendency for organizations to focus more on their own solutions rather than partnership solutions, even if their solutions might only be piecemeal. This behavior needs to be discouraged, whereas there needs to be more effort in encouraging broad thinking and collaborative partnerships. Such professionals are needed to fill a critical global void and will continue to be in great demand.

Funding agencies should consider allocating some funding to supporting teams that demonstrate a commitment to operating in the space of convergence in FEW systems. Agencies could develop areas where they wish to seek proposals from groups or encourage proposals from groups who will define proposed areas of research, education, and outreach. In every case, the team will be required to include matching funds to match with agency funds. This requirement is likely to ensure participation of industry and others from the beginning.

Role of professional societies

ASABE has a long history of providing resources to help its member engineers solve problems in food, agriculture, natural resources, and the environment. Recognizing the need to connect its members and partner societies to address emerging challenges as a global community, ASABE implemented an initiative, “Global Partnerships for Global Solutions: An Agricultural and Biological Engineering Global Initiative,” in 2012 toward achieving its global vision:

“ASABE will be among the global leaders that provide engineering and technological solutions toward creating a sustainable world with abundant food, water, and energy, and a healthy environment.”

ASABE has published a white paper (ASABE 2015 ) that outlines the grand challenges that the world is facing, highlights the specific needs of the three “security” themes (food security, energy security, and water security) in the context of sustainability and climate change, and discusses how ASABE, its members, and its partners will address these grand challenges as the year 2050 approaches. ASABE ( 2015 ) identified the following goals for ABEs: (1) improve food productivity; (2) reduce food losses and waste; (3) enhance energy conservation and efficiency; (4) develop adaptable renewable energy systems; (5) improve water availability, conservation, and efficient use; and (6) provide clean water for multiple uses (human consumption, agriculture, recreation, ecosystem services, biodiversity, etc.).

Other professional societies have also identified the need for and interest in partnerships. For example, the theme of the 2015 Annual Meetings of the American Society of Agronomy (ASA), Crop Science Society of America (CSSA), and Soil Science Society of America (SSSA) (the Tri-Societies) was “Synergy in Science: Partnering for Solutions,” and for 2016, the theme will be “Resilience Emerging from Scarcity and Abundance.” In 2015, ASA/CSSA/SSSA issued a call for white papers ( www.crops.org/science-policy/get-involved/infews-white-papers ) to help inform the National Science Foundation (NSF) in identifying research priorities for its Innovations at the Nexus of Food, Energy, and Water Systems (INFEWS) funding program. The resulting FEW White Paper Database ( www.crops.org/science-policy/white-papers ) includes a variety of papers, including one focused on phosphorus stewardship for resilience in FEW security (Sharpley et al. 2015a ). Clearly, issues central to FEW nexus security are a priority for the Tri-Societies now and into the future.

Professional societies could provide appropriate expertise to facilitate development of partnerships and collaborations focused on addressing FEW systems challenges. Some examples of activities that professional societies could collaboratively or individually address include the following:

Societies could host conferences or workshops designed to bring together individuals from multiple disciplines with the intention of developing partnerships or collaborations to focus on specific topics or goals.

Cross-society committees or task groups could be formed to focus on specific aspects of FEW systems, to submit proposals for funding, prepare issue papers, or meet other goals.

Leadership of multiple societies could meet with the specific objective of developing specific goals and action plans for partnerships focused on various aspects of FEW systems. Individuals who are active members across two or more societies would be valuable resources, e.g., co-author J. W. Jones is a Fellow of ASA, ASABE, and SSSA.

Societies could help lead the development of standards and protocols for interconnecting distributed components of proposed cyber-physical platforms and frameworks.

Societies could collaborate in providing information to federal agencies and legislators on the importance of funding collaborative research (convergence approach). For example, the Charles Valentine Riley Memorial Foundation ( http://rileymemorial.org/ ) and its partners are including the perspective of scientific societies in developing a unified message in support of publicly funded food, agricultural, and natural resources research. Here, 27 US scientific societies were invited to participate in a facilitated conversation in December 2015 to share their perspectives and insights.

Societies could facilitate interaction of industry, government, and academia among members within a society as well as across societies.

Food-energy-water systems are complex. Engineering and technology developed and implemented via a systems approach are critical to addressing FEW systems challenges. A concurrent cyber-physical framework comprised of systems informatics, information analysis methods and tools, and systems analytics and decision support could provide a viable approach for addressing FEW systems challenges. Many different disciplines are required to populate and implement the framework. A variety of organizations, private and public, can help facilitate collaboration and partnerships among disciplines. Government agencies, industry, academia, and professional societies can all play significant roles in furthering collaboration to address FEW systems challenges.

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Wolfe, M.L., Ting, K.C., Scott, N. et al. Engineering solutions for food-energy-water systems: it is more than engineering. J Environ Stud Sci 6 , 172–182 (2016). https://doi.org/10.1007/s13412-016-0363-z

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solving problems in food engineering solutions

Fighting Hunger

How engineers are helping to solve the global food crisis.

Read a summary or generate practice questions using the INOMICS AI tool

The world is beset by intertwined crises: the climate, pandemic, and, increasingly, a crisis in the global food supply. It’s prosaic to say, but as the world’s population increases, more food is needed to sustain it. And regardless of the population size, humanity will only ever have the same amount of land on which to produce its food. This, as one may expect, is a growing tension. Luckily, engineers of all stripes are busy using their expertise, conjuring up innovative solutions to address the issue. Here we take a look at some of the most impressive.

Vertical farming

So, because the planet has a finite amount of land, we need to get the most yield out of what we have. One way of doing this is building upwards – building vertical farms. A vertical farm means that more food can be grown in the same amount of land because crops are being grown in layered structures. Obviously vertical farming is much different than conventional farming . It is heavily reliant on technology rather than natural elements like the Sun and wind, which means that if a vertical farm loses power there is a huge loss of production. Also, because some crops need much more light and water than a vertical farm can provide, only certain crops, such as lettuce, can be grown this way. 

Unfortunately, vertical farming is not yet a sustainable way to produce food. Growing crops in large fields and in other conventional locations is still more affordable and accessible. That being said, with the work that engineers are doing, it is only a matter of time before vertical farming is a viable solution to help solve the global food crisis. Watch this space.

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Biological inoculants

Besides coming up with new ways to grow crops, another angle engineers have taken to help solve the global food crisis is targeting the crops themselves. One such example is biological inoculants. These inoculants consist of bacteria, algae, or fungi that are applied to the crops or  soil. They are used to mutually benefit both the crops they treat and the soil they grow in. For example, a fungus applied to a soybean plant strengthens its root system, allowing it to take in more nutrients. By doing so, less fertilizer and water is wasted, saving farmers money they can spend elsewhere.

How engineers are helping to solve the global food crisis

A further benefit of biological inoculants is that they are entirely organic. This characteristic means that farmers can start to reduce their use of chemical-based products, so there are fewer chemicals being added to the soil, water, and air. While microbiologists and other scientists helped to develop the inoculants, the world has engineers to thank for both scaling up the production process and cutting its costs. Thanks to engineers’ ingenuity, the world has more food to eat.

Research and development in space

In response to the dwindling resources on Earth, Humans are attempting to visit and build settlements on different celestial bodies like the Moon and on Mars. Naturally, if a settlement is eventually built, food will be needed to keep the settlement going. Sending food shipments to these faraway places is unsustainable, meaning researchers are trying to find ways to grow food on these settlements, and make them self-sustaining.

Unfortunately, a self-sustaining settlement in space is a long way off. There are many challenges to growing food in space, including water, energy, and nutrients. That has not put engineers off the challenge. The Advanced Plant Habitat aboard the International Space Station, for example, is a growth chamber used to test which growth conditions plants prefer in space. Engineers were needed to ensure that this system held up and they designed key features, such as the carbon dioxide scrubber that removes carbon dioxide from the chamber.

The lessons learned from this research and development can be applied on Earth as well. Growing food in space is all about efficiency, meaning that if we master growing food in space, our ability to grow food more efficiently on Earth will be greatly increased. It sounds mad, but its true.

Mastering resource management

The world has a finite amount of resources. Once they’re used up, they’re gone. That means that we must make the most of them while we can. Water is one of those resources. The more water that is wasted, the less food that can be grown. Engineers, though, are on hand, and have begun working to minimise water wastage by designing effective water management and irrigation systems. If these systems can be optimized, most of the water used for irrigation will be used rather than wasted. This optimization is particularly important in areas with limited water resources that are already suffering from desertification.

Engineers can also help by minimizing the amount of food waste that is produced. This could mean, for example, designing containers that slow the rate at which food spoils, or improving roads and railways so that food can get from fields to people’s mouths more quickly. When it comes to mastering resource management in terms of the global food crisis, engineers have a lot to offer.

Bottom line

Engineers are using their expertise to tackle the global food crisis in a number of ways. Whether coming up with new technologies to make food easier to grow, designing infrastructure to limit waste, or doing research to find ways to make growing food more effective, engineers have a big hand in dealing with the global food crisis. As the global population grows and the problem worsens, we will need to lean more on engineers to be even more innovative.

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How engineering students are seeking to solve major food and water security problems

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Kevin Patrick Simon (left) sets solar panels in place with villagers at a pilot site in Jharkhand, India. Simon, an MIT PhD candidate in mechanical engineering, is studying solar irrigation.

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Food and water are two necessities for survival, but what happens when a changing climate in key agricultural regions threatens crop production? Or when the quality of milk cannot be ensured as it is exchanged between producer and seller?

Seven MIT graduate students studying food and water security issues presented their research and preliminary findings on issues such as these during the MIT Water and Food Security Student Symposium held on Nov. 21. Hosted by the MIT Department of Civil and Environmental Engineering (CEE) and the MIT Abdul Latif Jameel World Water and Food Security Lab (J-WAFS), the event brought together professors and students to discuss food and water challenges and opportunities to address these through research.

Chandra Madramootoo, CEE visiting professor and J-WAFS visiting scholar, curated the event and spoke briefly of the importance of water and food security. “The withdrawal of water varies in different parts of the world. Much larger amounts of water are withdrawn for agriculture compared to industry and domestic uses in South Asia, Middle East, North Africa, Sub-Saharan Africa, Latin America, and East Asia Pacific. This puts stresses on water resources available for food production and our ability to achieve food security. It also puts stress on a very finite resource amongst the other economic and environmental sectors that are competing for that water supply,” Madramootoo highlighted in his opening remarks. 

During the event, each student presenter was tasked with conveying a broad overview of his or her research in a five minute presentation. There was a wide range of topics, but each student sought to solve a problem relating to food or water. Presentations addressed research as varied as understanding the impact of an environmental threat to agriculture to developing improved irrigation technology to help smallholder farmers around the world.

Understanding food and water through an environmental lens

Paige Midstokke, a master’s candidate in CEE and a Tata Fellow in Technology and Policy, kicked off the student component of the symposium. Through her research, Midstokke is seeking to improve the drought planning process by researching water security for the state of Maharashtra in India. Since most water planning and management is done at the district level, Midstokke is conducting a case study, focusing on the Aurangabad district.

For her thesis, Midstokke is developing a “vulnerability-scarcity index” that integrates socioeconomic data, pumping rates from observation wells, and geographic information system data. “If you work with the government of India, they have a ton of data and they are often willing to give it to you; you get to decide how to put it all together,” she said. Her index is intended to improve early indicators of drought and thus improve water scarcity planning at the district and local levels.

Anjuli Jain Figueroa, a PhD candidate in CEE, introduced her research by asking if the question of “sustainable agriculture” is an oxymoron. She noted that historically, increased food production has had negative environmental impacts. Her research, entitled “Sustainable agriculture – quantifying the trade-offs between food security and environmental impacts,” uses case studies from India to describe this trend.

During her presentation, Jain Figueroa provided an example of a trade-off she is studying, wherein profit increased for farmers, but there was an unforeseen negative impact. “We see this in a shift that happened with rice. India is growing a lot of rice but it has come at a cost; the cost was nutritional value. Even though families are making more money, the nutritional value for that household decreased. That’s one of the unintended consequences we’re only realizing now,” Jain Figueroa said. She is now working to solve problems like these in her thesis by using a systems approach to study how farmers could increase crop production to meet 2050 population needs, while limiting the negative environmental impacts.

Luke Schiferl, a PhD candidate in CEE, added a global perspective to food and water security. Schiferl looks at how air quality affects crop productivity on a global scale and how these effects can be quantified. In his research project “Contrasting particulate matter and ozone effects on crop production,” he uses crop production simulations and chemistry models to quantify the offsetting effects of ozone and particulate matter on crop productivity. This research can suggest how crop production losses can be properly mitigated by air quality improvements once the effects are understood.

“We can relate ozone and particulate matter effects with known relationships to crop production and basically plot the different effects,” he said. In the future, Schiferl hopes to apply similar research to simulate the effects water and nutrient restrictions have on these air quality effects and predicting crop productivity.

Creating tools and technology to solve food and water security issues

Four student contributors to the symposium presented on technological solutions to various environmental issues. Many of these innovative tools are already in use and tested, ready to make positive changes for food and water security around the world. 

Kevin Patrick Simon, a PhD candidate in mechanical engineering, lent the energy perspective to the water and agriculture discussion. Energy is needed to access water sources with pumps, but energy supply from electricity grids and water sources are distributed unequally across India. In rural India, small farmers still use diesel to run their water pumps. Unfortunately, the cost of diesel discourages year-round cultivation in favor of lower-paying jobs during the winter and dry season. Simon is seeking a solution with his research on “High efficiency, low-cost positive displacement pumps for solar irrigation.”

The central question to Simon’s research — “How can we enable people to have better access to water in order to irrigate their land?” — is addressed in part by solar irrigation. He explained the numerous benefits of solar irrigation, including its potential for cost-savings, independence from the electricity grid and environmental sustainability. “It gives small farmers an unprecedented amount of independence and an ability to draw income from their land,” he said.

Pulkit Shamshery, also a PhD candidate in mechanical engineering, is taking a different approach to irrigation by making drip irrigation systems more energy-efficient and more accessible to small farmers. Shamshery noted that 15 percent of India’s food production is dependent on over-exploited water resources; “there is an extreme need for more food with less water, and that’s the motivation for drip irrigation.” Advantages of drip irrigation are increased water savings and higher yields. Additionally, farmers can use less fertilizer by only applying it where it is needed. However, drip irrigation is typically a costly endeavor, which led Shamshery to his project “Low cost, energy-efficient drip irrigation system.”

Along with a research team, Shamshery looked at where the most pressure was being lost in irrigation systems when using them to retrieve water from surface sources, and then figured out how to reduce that pressure loss. The team created an off-grid product to make irrigation more efficient and available at a lower cost. Shamshery’s component is patent-pending and will be licensed, and the pressure loss results in a cut of cost of an off-grid drip system by 50 percent for an acre of land.

“In the United States you can buy milk off the shelf and not worry about any contamination, but that’s not true for developing countries,” said Pranay Jain, Legatum Fellow and PhD candidate in mechanical engineering. Jain highlighted the public health and economic consequences of milk contamination in India, noting that when people don’t trust the quality of milk that is sold to them, the milk industry suffers. 

Jain’s research project, “Milk quality analysis for villages in India,” addresses the question: “If milk changes hands so many times before it reaches the consumer, how can these buyers and sellers trust each other?” Jain’s solution was to create an affordable, portable instrument to quickly and accurately determine the quality of milk as it moves through the supply chain from farm to grocery store, and to help improve payment mechanisms. By testing the quality of milk on the spot, “the supply chain simplifies; more farmers opt to sell their milk to processing plants, these plants can get more milk, they will pay the farmer more accurately and both sides benefit.” The device is also powered by mobile phones and collects data; the data collected by the device will be saved in the cloud, allowing for researchers to observe trends and monitor the health of their livestock. 

In India, approximately one out of every five crates of produce is lost due to spoilage, but Kendall Nowocin, Legatum Fellow and PhD candidate in electrical engineering and computer science, is tackling that problem with CoolCrop, a storage apparatus for small farmers. The storage unit is about the size of a walk-in closet and extends the freshness of their produce.

CoolCrop is already being piloted through cooperatives involving non-governmental organizations, and their use is augmented with market analytics. Following supply-and-demand, farmers get the most value for their crops if they are in markets with fewer competitors. CoolCrop fills this void by providing market analytics to small farmers, so they know which markets will increase their profit. Explaining their business model, he said that they “can extract an initial profit that pays for the cold storage and increases the value for the farmer.” 

CEE Professor Dennis McLaughlin noted in the closing remarks that although most of the research shared at the symposium was centered on India, other developing areas around the world are dealing with similar issues.

The MIT Water and Food Security Student Symposium was the final component of a seminar series hosted by Chandra Madramootoo. Commenting that on the importance of water security, he said “I think it’s important when we think about water in the broader context, to think about the competition that’s placed between agricultural water and other sources of water. It’s this competing pressure for resources that we need to think about.”

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Solving Problems in Food Engineering

Solving Problems in Food Engineering is a step by step workbook intended to enhance students' understanding of complicated concepts and to help them practice solving food engineering problems. The book covers problems in fluid flow, heat transfer, mass tr

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Stavros Yanniotis, Ph.D. Author Solving Problems in Food Engineering Stavros Yanniotis, Ph.D. Department of Food Science and Technology Agricultural University of Athens Athens, Greece ISBN: 978-0-387-73513-9 eISBN: 978-0-387-73514-6 Library of Congress Control Number: 2007939831 # 2008 Springer Science+Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC., 233 Spring Street, New York, NY10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper 9 8 7 6 5 4 3 2 1 springer.com ‘‘Tell me and I will listen, Show me and I will understand Involve me and I will learn’’ Ancient Chinese Proverb Preface Food engineering is usually a difficult discipline for food science students because they are more used to qualitative rather than to quantitative descriptions of food processing operations. Food engineering requires understanding of the basic principles of fluid flow, heat transfer, and mass transfer phenomena and application of these principles to unit operations which are frequently used in food processing, e.g., evaporation, drying, thermal processing, cooling and freezing, etc. The most difficult part of a course in food engineering is often considered the solution of problems. This book is intended to be a step-by-step workbook that will help the students to practice solving food engineering problems. It presumes that the students have already studied the theory of each subject from their textbook. The book deals with problems in fluid flow, heat transfer, mass transfer, and the most common unit operations that find applications in food processing, i.e., thermal processing, cooling and freezing, evaporation, psychometrics, and drying. The book includes 1) theoretical questions in the form ‘‘true’’ or ‘‘false’’ which will help the students quickly review the subject that follows (the answers to these questions are given in the Appendix); 2) solved problems; 3) semisolved problems; and 4) problems solved using a computer. With the semisolved problems the students are guided through the solution. The main steps are given, but the students will have to fill in the blank points. With this technique, food science students can practice on and solve relatively difficult food engineering problems. Some of the problems are elementary, but problems of increasing difficulty follow, so that the book will be useful to food science students and even to food engineering students. A CD is supplied with th

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Heavy Machinery Meets AI

  • Vijay Govindarajan
  • Venkat Venkatraman

solving problems in food engineering solutions

Until recently most incumbent industrial companies didn’t use highly advanced software in their products. But now the sector’s leaders have begun applying generative AI and machine learning to all kinds of data—including text, 3D images, video, and sound—to create complex, innovative designs and solve customer problems with unprecedented speed.

Success involves much more than installing computers in products, however. It requires fusion strategies, which join what manufacturers do best—creating physical products—with what digital firms do best: mining giant data sets for critical insights. There are four kinds of fusion strategies: Fusion products, like smart glass, are designed from scratch to collect and leverage information on product use in real time. Fusion services, like Rolls-Royce’s service for increasing the fuel efficiency of aircraft, deliver immediate customized recommendations from AI. Fusion systems, like Honeywell’s for building management, integrate machines from multiple suppliers in ways that enhance them all. And fusion solutions, such as Deere’s for increasing yields for farmers, combine products, services, and systems with partner companies’ innovations in ways that greatly improve customers’ performance.

Combining digital and analog machines will upend industrial companies.

Idea in Brief

The problem.

Until recently most incumbent industrial companies didn’t use the most advanced software in their products. But competitors that can extract complex designs, insights, and trends using generative AI have emerged to challenge them.

The Solution

Industrial companies must develop strategies that fuse what they do best—creating physical products—with what digital companies do best: using data and AI to parse enormous, interconnected data sets and develop innovative insights.

The Changes Required

Companies will have to reimagine analog products and services as digitally enabled offerings, learn to create new value from data generated by the combination of physical and digital assets, and partner with other companies to create ecosystems with an unwavering focus on helping customers solve problems.

For more than 187 years, Deere & Company has simplified farmwork. From the advent of the first self-scouring plow, in 1837, to the launch of its first fully self-driving tractor, in 2022, the company has built advanced industrial technology. The See & Spray is an excellent contemporary example. The automated weed killer features a self-propelled, 120-foot carbon-fiber boom lined with 36 cameras capable of scanning 2,100 square feet per second. Powered by 10 onboard vision-processing units handling almost four gigabytes of data per second, the system uses AI and deep learning to distinguish crops from weeds. Once a weed is identified, a command is sent to spray and kill it. The machine moves through a field at 12 miles per hour without stopping. Manual labor would be more expensive, more time-consuming, and less reliable than the See & Spray. By fusing computer hardware and software with industrial machinery, it has helped farmers decrease their use of herbicide by more than two-thirds and exponentially increase productivity.

  • Vijay Govindarajan is the Coxe Distinguished Professor at Dartmouth College’s Tuck School of Business, an executive fellow at Harvard Business School, and faculty partner at the Silicon Valley incubator Mach 49. He is a New York Times and Wall Street Journal bestselling author. His latest book is Fusion Strategy: How Real-Time Data and AI Will Power the Industrial Future . His Harvard Business Review articles “ Engineering Reverse Innovations ” and “ Stop the Innovation Wars ” won McKinsey Awards for best article published in HBR. His HBR articles “ How GE Is Disrupting Itself ” and “ The CEO’s Role in Business Model Reinvention ” are HBR all-time top-50 bestsellers. Follow him on LinkedIn . vgovindarajan
  • Venkat Venkatraman is the David J. McGrath Professor at Boston University’s Questrom School of Business, where he is a member of both the information systems and strategy and innovation departments. His current research focuses on how companies develop winning digital strategies. His latest book is Fusion Strategy: How Real-Time Data and AI Will Power the Industrial Future.  Follow him on LinkedIn . NVenkatraman

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COMMENTS

  1. PDF Solving Problems in Food Engineering

    in food processing, e.g., evaporation, drying, thermal processing, cooling and freezing, etc. The most difficult part of a course in food engineering is often considered the solution of problems. This book is intended to be a step-by-step workbook that will help the students to practice solving food engineering problems.

  2. Solving Problems in Food Engineering

    Solving Problems in Food Engineering Home Textbook Authors: Stavros Yanniotis Introduction to food engineering problems for those who have very little to no background in engineering Supplemental text that covers the basics of food engineering problem solving A progressive degree of difficulty in the questions

  3. (PDF) Solving Problems in Food Engineering

    Food processing is unique from other material processing, as it includes complex multiphase transport and change in material properties during processing. It poses a great challenge in food process engineering. Now a day's, consumers are taking more precautions before eating something. The way of food processing effectively impacts food quality.

  4. PDF Arguing to Solve Food Engineering Problems

    Problem solving is an essential 21st century skill, specifically the ability to solve different kinds of problems and to identify and ask significant questions1. The cognitive processes that enable learners to solve problems are the construction of problem schemas, analogical reasoning, causal reasoning, and argumentation2.

  5. Solving Problems in Food Engineering

    Solving Problems in Food Engineering is a step by step workbook intended to enhance students' understanding of complicated concepts and to help them practice solving food engineering problems. The book covers problems in fluid flow, heat transfer, mass transfer, and the most common unit operations that have applications in food processing, such as thermal processing, cooling and freezing ...

  6. [PDF] Solving problems in food engineering

    Solving problems in food engineering S. Yanniotis Published 2008 Engineering, Agricultural and Food Sciences Conversion of Units.- Use of Steam Tables.- Mass Balance.- Energy Balance.- Fluid Flow.- Pumps.- Heat Transfer By Conduction.- Heat Transfer By Convection.- Heat Transfer By Radiation.- Unsteady State Heat Transfer.-

  7. (PDF) Arguing to solve food engineering problems

    Our results validate that argumentation is an essential skill in learning to solve studied food engineering problems as well as a powerful method for assessing problem-solving ability...

  8. Solving Problems in Food Engineering (Food Engineering Series)

    Solving Problems in Food Engineering is a step by step workbook intended to enhance students' understanding of complicated concepts and to help them practice solving food engineering problems. ... The semi-solved problems guide students through the solution. Some of the problems progress from elementary level increasing difficulty so that the ...

  9. Solving Problems in Food Engineering

    Solving Problems in Food Engineering January 2008 Publisher: Springer ISBN: 978--387-73513-9 Authors: Stavros Yanniotis Agricultural University of Athens Download citation Abstract

  10. Solving Problems in Food Engineering

    Solving Problems in Food Engineering is written by Stavros Yanniotis and published by Springer. The Digital and eTextbook ISBNs for Solving Problems in Food Engineering are 9780387735146, 0387735143 and the print ISBNs are 9780387735139, 0387735135. Save up to 80% versus print by going digital with VitalSource.

  11. Inverse problems in food engineering: A review

    Inverse problems are usually performed when direct measurements of heat and mass transfer properties and boundary conditions, are not feasible. They are very sensitive to measurement errors; require optimization methods to tackle the inverse problem. It is necessary to consider the coupling heat and mass transfer for the solution of inverse ...

  12. Inverse problems in food engineering: A review

    Inverse problems in food engineering: A review. Direct problems are solved by FDM, FEM, FVM or analytical method. The inverse problems are used for the estimation of unknown quantities from the indirect measurements. Statistical concepts in inverse problems are crucial and must be performed.

  13. Solving Problems in Food Engineering

    1. Conversion of Units . . . 1 Examples Exercises 2. Use of Steam Tables. . . 5 Review Questions Examples Exercises 3. Mass Balance. . . 11 Review Questions Examples Exercises 4. Energy Balance . . . 21 Theory Review Questions Examples Exercises

  14. Solving Problems in Food Engineering

    Solving Problems in Food Engineering is a step by step workbook intended to enhance students' understanding of complicated concepts and to help them practice solving food engineering...

  15. How to Boost Your Food Engineering Problem-Solving Skills

    1 Define the problem The first step to solving any problem is to define it clearly and precisely. You need to identify the goal, the constraints, the assumptions, and the criteria for...

  16. Solving problems in food engineering

    Solving problems in food engineering Responsibility Stavros Yanniotis, author. Digital text file; PDF Imprint New York : Springer Science + Business Media, ©2008. Physical description 1 online resource (xi, 297 pages) : illustrations Series Food engineering series. Online Available online SpringerLink Report a connection problem More options

  17. Engineering solutions for food-energy-water systems: it is ...

    Food, energy, and water systems interact extensively, giving rise to the term "food-energy-water (FEW) nexus," with the term "nexus" signifying connectedness and interrelationships. A systems approach involving multidisciplinary and transdisciplinary teams and partnerships is needed to address complex challenges of the nexus. A concurrent cyber-physical framework comprised of systems ...

  18. Problem-solving learning environments for an introduction to food

    Workplace problems are ill-structured and complex because they possess conflicting goals, multiple solution methods, non-engineering success standards, non-engineering constraints, unanticipated ...

  19. Solving Material Balance Problems

    Solving Material Balance Problems | Food Engineering | Food Technology | Food Technology Lecture | Food Engineering Lecture | GATE Food Technology Questions ...

  20. How Engineers are Helping to Solve the Global Food Crisis

    (Online) Data Science Projects and Applications at FutureLearn Biological inoculants Besides coming up with new ways to grow crops, another angle engineers have taken to help solve the global food crisis is targeting the crops themselves. One such example is biological inoculants.

  21. How engineering students are seeking to solve major food and water

    How engineering students are seeking to solve major food and water security problems Students with a common passion for food and water security share their research at the MIT Water and Food Security Symposium. Carolyn Schmitt | Department of Civil and Environmental Engineering Publication Date January 9, 2017 Press Inquiries Caption

  22. Solving Problems in Food Engineering

    Solving Problems in Food Engineering Stavros Yanniotis, Ph.D. Department of Food Science and Technology Agricultural University of Athens Athens, Greece ISBN: 978--387-73513-9 eISBN: 978--387-73514-6 Library of Congress Control Number: 2007939831 # 2008 Springer Science+Business Media, LLC All rights reserved.

  23. Solving Problems in Food Engineering

    The book includes 1) theoretical questions in the form ''true'' or ''false'' which will help the students quickly review the subject that follows (the answers to these questions are given in the Appendix); 2) solved problems; 3) semisolved problems; and 4) problems solved using a computer.

  24. Heavy Machinery Meets AI

    The automated weed killer features a self-propelled, 120-foot carbon-fiber boom lined with 36 cameras capable of scanning 2,100 square feet per second. Powered by 10 onboard vision-processing ...