Development and verification of a decision support system for the selection of optimum water reuse...

Desalination 152 (2002) 339–352

0011-9164/02/$– See front matter © 2002 Elsevier Science B.V. All rights reserved

Presented at the EuroMed 2002 conference on Desalination Strategies in South Mediterranean Countries:Cooperation between Mediterranean Countries of Europe and the Southern Rim of the Mediterranean.Sponsored by the European Desalination Society and Alexandria University Desalination Studies and TechnologyCenter, Sharm El Sheikh, Egypt, May 4–6, 2002.

*Corresponding author.

Development and verification of a decision support system forthe selection of optimum water reuse schemes

Safaa Abdelraouf Ahmed, Shadia Ragheb Tewfik*, Hala Ahmed TalaatChemical Engineering and Pilot Plant Department, National Research Center, Dokki, Cairo, Egypt

Tel. +20 (2) 3389935; Fax +20 (2) 3370931; email: [email protected]

Received 1 April 2002; accepted 15 April 2002

Abstract

The selection of an optimum scheme for treatment of agricultural drainage water for reuse in specific applicationsis a challenging and tedious task that necessitates the evaluation of a long train of optional treatment processeswhich involve, among others, alternative desalting systems. The decision maker is requested to compromise betweenmultiple factors including technical and economic considerations. Over the last two decades, considerable advanceshave been achieved in the development of expert systems as a decision support tool for the solution of variousengineering and management problems. This paper addresses the development and verification of an expert systemfor the intelligent selection of the optimal scheme for the treatment of agricultural drainage water. As typical ofexpert systems, the adopted approach includes a knowledge base, inference engine and working memory. Thedeveloped system encompassed the formulation of the logic tree and the rule base according to the experts’ knowledgeand engineering algorithms. The system has been verified for agricultural drainage water for reuse in severalapplications requiring water of varying quality. Outputs include the selected treatment trains, intermediate and finalwater characteristics and capital and operating costs for two typical cases. It is concluded that the developed expertsystem could be used for rapid and reliable decision making on the optimum management scheme for treatment ofagricultural drainage water for reuse.

Keywords: Drainage water; Treatment; Expert system; Cost; Reuse

340 S.A. Ahmed et al. / Desalination 152 (2002) 339–352

1. Introduction

The increasing population in the universespecifically in the Middle East and North Africa(MENA) region is manifested by severe watershortage. The availability of renewable waterresources has dropped from 3,500 to 1,500 m3 percapita in 1990 and 1996 respectively. It is anticipatedthat it will drop to 700 m3per capita by the year2025. Egypt and other countries in the region arealready consuming more than 100% of their renew-able water resources. In Egypt, agriculture is pro-portionately the largest user of water (87%), whileindustry and domestic supplies consume about(7%) and (6%), respectively [1]. Thus great effortsare being directed towards desalting and treatmentof the drainage water to be reused for differentpurposes.

The drainage water is characterized by havingmedium BOD and suspended solid concentra-tions, relatively higher COD due to the presenceof residual pesticides and fertilizers in additionto the varying contents of heavy metals andbacteria depending on the extent of contaminationby industrial and municipal wastewater. Typicaltreatment processes for these pollutants includepretreatment, biological, physicochemical, desaltingand disinfection [2].

However, to recommend an optimum treatmentscheme, the designer is requested to comparebetween numerous process(es) and trains on tech-nical and economic considerations including con-trolling factors encompassing location, poweravailability and the purpose of utilization of thetreated water.

Early computer technology was used for solvingonly very specific problems, by adopting fairlyroutine computational aspects [3].

As a result of the increase of confidence inalgorithms development and optimisation tech-niques, higher sophisticated problems weredeveloped and solved [4]. With the need for rapid,logical, efficient and emergent decisions for manyproblems, there has been an increasing trendtowards the adoption of expert systems, which

are considered to be among the fasted growingbranches of artificial intelligence (AI). Expertsystems (ES) are advanced computer programs,which emulate, or try to, the human reasoning andproblem solving capacities using knowledge withina particular discipline. ES always possess certainheuristics that form the static knowledge base andthe search processes conducted by the inferenceengine (which may be forward chaining, backwardchaining or a combination of both) [5,6]. Knowledgebase was initially formulated as rule based expressedas individual sequential rules. With the progressin application of expert systems, case basedapproach, which is integrated previously developedschemes, has evolved.

ES technology has received renewed interestwithin the scope of environmental managementas applied for cost modeling and evaluation ofwastewater [7,8], biological treatment processcontrol [9,10], hazardous waste incineration faci-lities [10], environmental planning managementand impact assessment [11,12] ,textile wastewatertreatment [13] and membrane and disinfection[14].

This paper presents a developed decisionsupport system (DDSS) for drainage water treat-ment and reuse which is considered to be anessential step for redrawing the water resourcesmap in Egypt. The developed case based systemis first presented and then verified through solvingtypical case studies. The final section summarizesthe conclusions and recommendations.

2. The developed decision support system

The aim of this work is to develop a drainagewater treatment decision support system (DDSS).This system, when provided with drainage watercharacteristics, could deduce a recommendedtreatment scheme and corresponding costsestimates for utilization in specific purposes underprevailing technical, economic and environmentalconditions and constraints.

S.A. Ahmed et al. / Desalination 152 (2002) 339–352 341

2.1. System architecture and components

The main key components of the DDSS are:• Static knowledge base (or long time memory).• Input/output user interface (or working memory).• Inference engine (or reasoning)

2.1.1. Case based system

Typically the knowledge has been encoded inthe static knowledge base in the form of casebased and rules. The case based reasoning is acomputer based problem solver, which reuses thesolution from established cases to solve currentproblems. A set of rules have been formulated and,coded allowing the system to deduce new resultsfrom the initial set of cases (premises). Thedeveloped wastewater treatment cases and costfiles are outlined in section 3.

2.1.2. Inference engine

The inference engine (thinking machine) is thepart of the approach concerned with solving theproblem by interpreting the knowledge activatedby the case base and rules. Visual Basic interactivelywith Access has been utilized as a computinglanguage for programming. The computing flow-chart of the system is depicted in Fig. 1.

2.1.3. Working memory

It is the vital part of the system includingmodules of a natural language communicationwith the user, a reasoning explanation interface,facts entered by the users as well as results (printoutreport, see Appendix).

a) Data input: Data file in DDSS comprisesthe basic data about the site as well as theconstraints. The basic data reading includes thespecified water characteristics and is complementedby relevant information comprising land avail-ability, power con-straints, and the proposed waterreuse purposes and quantities.

b) Data output: The DDSS will assist thedecision maker in developing the optimum treat-ment train, the overall and individual mass balance

together with the estimated individual equipmentcost. Accordingly a complete cost file containingthe total capital and operating costs is deduced.

3. Drainage wastewater treatment

3.1. Technical consideration

Various processes could be adopted for treatmentof drainage wastewater. Fig. 2 presents thedeveloped integrated scheme to recover water ofsequentially improved characteristics suitable forreuse in different applications entailing water ofspecific quality. A removal efficiency matrix forindividual process modules has been also developedby statistical analysis of reported and experi-mental data [15–19] as depicted in Table 1. Treat-ment matrix, which evolved from actual cases, hasbeen compiled as presented in Table 2 to constitutethe foundation of the case based knowledge ofDDSS. Typical example of rule is as follows:

Case No.

Treatment scheme Water quality

1 PT — NS A 2 PT — Ch. T–AL B 3 PT — AS A 4 PT — Ch. T–TF–CL–DMF B 5 PT — Ch.T–CL–DMF–AC–NF C 6 PT — AL–WT–PL–CL–MDF–AC–

NF–RO–Dis D

7 PT — Ch.T–TF–CL–DMF–AC–ED–RO–Dis.

D

8 PT — Ch.T–CL–DMF–Ch.Ox–AC C 9 PT— Ch.T–CL–DMF–Ch.Ox–AC–

ED–IE D

10 PT — Ch.T–CL–DMF–AC–ED–IE D 11 PT — Ch.T–TF–CL–DMF–AC–

ED–RO–Dis D

12 PT — CL–DMF–AC–IE C 13 PT — Ch.T–CL–DMF–MF–UF–

RO–Dis. D

14 PT — Ch.T–CL–DMF–MF–NF D

Table 1Typical actual case treatment schemes

https://www.researchgate.net/publication/254984302_Electrotechnologies_for_waste_and_water_treatment_Final_report?el=1_x_8&enrichId=rgreq-ddc1b5b6b1cd447245cdf4501aba06b4-XXX&enrichSource=Y292ZXJQYWdlOzIyMjgyNjk4NztBUzo5ODYwODcxMTk5NTQwOUAxNDAwNTIxNTU4MTEx


11

START

USER INTERFACE

INPUT DATA

DATA

UPDATE

CHECK

FOR MISSING

DATA

KNOWLEDGE BASE

HEURISTICS

KNOWLEDGE

REGULATORY

CONSTRAINTS DATABASE

IF – THEN RULES

FIRING RULES

AND EXECUTING SUBROUTINE

RELATED TO THE FIRED RULES

INTERMEDIATE

RESULTS

USER INTERACTIVE

DISPLAY

SEARCH AND REASONING

EVALUATION OF ALTERNATIVE

AND SELECTING THE SUITABLES

ALETERNATIVE

INTERMEDIATE

RESULTS

USER INTERACTIVE

DISPLAY

COST FILE

COST ESTIMATE OF

THE TREATMENT

PATHWAY

USER INTERFACE

PRINT OUT REPORT

END

Fig. 1. Schematic flow diagram of DDSS.

IF SS > 500OR Total heavy metal >= allowableOR Total phosphorous > allowableAND Bacteria < allowableAND BOD >200AND Power is not availableAND Land is availableAND Industrial uses = FALSEAND Potable water = = FALSEAND High quality water = = FALSE

AND cooling water = TRUETHEN The treatment train = Screen-grit removal-equalization-chemical treatment-aerated lagoon-chlorination-sand filter

3.2. Economic aspects

Capital and operating cost files have beendeveloped for the involved treatment modules.Equipment costs are expressed as functions ofessential parameters as obtained from published


A

Chemical Dosing ARaw

Water

To Dual Media

Filtration

A

Pretreatment Sedimentation Biological Treatment Disinfection

B B C D

From

Disinfection

D

C

Removal of Hazardous Dissolved Removal of Dissolved Solids and

Organics and Heavy Metals Residual Organic and Inorganic Ions

Legend for Treated Water A :Low Quality,B: Medium,C : Pure,D: Ultrapure

Screen EqualizationGrit

Removal

Natural

Sedimentation

Chemical

Treatment

Aerated

Lagoons

Trickling

Filter

Anaerobic

Lagoon

Wet Land

Activated

Sludge

Polishing

Lagoons

Disinfection

Dual Media

Filter

Chemical

Oxidation

Micro-

Filtration

Activated

Carbon

Reverse

OsmosisMembrane

Filtration

Ion

Exchange

Disinfection

Electro-

Dialysis

Fig. 2. Treatment tree of drainage water for alternative reuse.

articles. Operating costs have been estimatedaccording to actual requirements and prevailingexpenses under local conditions. Table 3 includesthe cost functions of the treatment modules andcost indicators for estimating operating expenses.

4. Case studies

To verify the developed expert system DDSS,two case studies representing the treatment of

agricultural wastewater to be recovered for reusein various applications has been investigated. Therelevant information on the specific cases and theresults are presented below.

4.1. Relevant information

It is required to treat 50,000 m3/d of drainagewater of characteristics depicted in the printoutsof the two cases. As shown, the wastewater is


Module

O

&G

S

S

DS

B

OD

C

OD

T

. phosp

h.

T. nit

rogen

T .phen

ol

T. hea

vy

met

als

Bac

teri

a

1

Scr

een

2

2

Gri

t re

mo

val

2

3

3

E

qu

aliz

atio

n

1

0

4

Gra

vit

y o

il r

emo

val

8

0

8

10

5

Nat

ura

l se

dim

enta

tio

n

5

5

3

0

40

1

5

6

Chem

ical

tre

atm

ent

wit

h

lim

e

60

35

3

8

75

9

0

7

Chem

ical

tre

atm

ent

wit

h

alu

m

60

7

0

4

0

30

1

5

2

5

84

8

Chem

ical

tre

atm

ent

wit

h

lim

e an

d a

lum

6

0

75

45

3

5

27

8

3

9

Chem

ical

tre

atm

ent

wit

h

ferr

ou

s su

lfat

e

65

44

5

0

10

Chem

ical

tre

atm

ent

wit

h

alum

and p

oly

mer

7

0

58

37

70

26

9

0

11

Chem

ical

tre

atm

ent

wit

h

ferr

ic c

hlo

ride

80

7

4

4

8

57

92

12

D

isso

lved

air

flo

tati

on

8

7

84

50

7

2

72

8

0

1

3

San

d f

ilte

r

68

28

3

4

27

16

1

4

Wet

lan

d

6

2

8

7

65

70

15

A

ctiv

ated

slu

dg

e 1

5

71

88

6

7

53

7

0

62

1

5

1

6

Tri

ckli

ng

fil

ter

80

85

6

5

20

6

0

1

7

Aer

ated

lag

oo

ns

12

6

8

8

0

62

40

18

A

nae

rob

ic l

ago

on

s 1

0

60

79

6

1

15

1

9

Act

ivat

ed c

arb

on

60

63

30

50

6

0

95

2

0

Ult

rafi

ltra

tio

n

9

6

90

9

9

21

Io

n e

xch

ang

e

97

9

5

45

5

5

50

8

0

99

2

2

Mic

rofi

ltra

tio

n

9

0

23

E

lect

rod

ialy

sis

95

4

5

55

5

0

9

9

24

R

ever

se o

smosi

s

9

5

10

32

99

Tabl

e 2

Effic

ienc

y m

atrix

of t

reat

men

t mod

ules


Table 3Basis of estimates of capital and operating costsa) Cost functions of the drainage water treatment modules

Treatment modules Equipment cost Basis

1. Screen 6.4 E – 04 × Q0.625

a) Rack bar screen b) Mechanical cleaning c) Speed up to 1 m

3/h

2. Grit removal 3.43 E – 04 × Q0.7

Including sediments removal system

3. Equalization 0.56 E – 04 Q0.7

4. Natural sedimentation 2.8 E – 05 Q + 0.4 a) Including sludge pumps b) Overflow rate 800 gallon/d c) Expected life 15 years

5. Chemical treatment 3.08 E – 05 Q+ 0.44 Including sludge pumps, dosing system and control and instrumentation system.

6. Dual media filter 4.06 E – 03 Q0.67

+ 0.44 a) Including back wash system b) Filtration cycle 12 h c) 15 min for backwash d) total height for backwash pumps 14 ft.

7. Activated sludge {6 E – 05 × Q + 0.501} × 1.5 8.4 E – 06 Q + 0.7

a) Expected life 20 years b) F/M = 0.25 c) Required oxygen 1.1 1b/1b BOD

8. Wet land {–2 E – 11 Q2 + 6 E – 06 × Q – 0.0014} × 1.4 Not including pumps

Expected life 20 years

9. Aerated lagoons {2 E – 05 Q + 0.4412) × 1.5

2.8 × Q + 0.617

a) Including aeration system and hydraulic control b) Surface aeration system

10. Anaerobic lagoons 1.5 E – 03 Q0.77

Including site preparation Not including pumps

11. Trickling filter (0.0001 × Q + 0.249) × 1.17 Including casing, media and drain network Not including pumps

12. Secondary sedimentation 6.02 E – 03 Q0.58

a) Including pumps b) Expected life 15 year.

13. Activated carbon 0.0047 × Q0.648

× 1.19 Including casing, media, pumps controlling system and waste disposal

14. Reverse osmosis 0.0041 × Q0.853 × 0.3126 Including pumping system

15. Ultrafiltration 34,050 × Q/12,000)0.6

Including pumping system

16. Electrodialysis 12.7 × (Q /50000)

^0.66

17. Chemical oxidation 3.9 E – 03 Q0.73

Including mixing system

18. Chlorination 1 E – 06 Q + 0.018 Including mixing system


Table 3Basis of estimates of capital and operating costsb) Basis of estimates of operating costs

Labor @ man h/m3 $1.25

Electricity @ kWh $0.04

Materials and chemicals Lime Alum FeCl3

Polymer Chlorine Active carbon

ton @ $22 ton @ $180 ton @ $660 ton @ $2700 ton @ $444 ton @ $1777

Membrane replacement @ 20% of capital

characterized by high concentrations of dissolvedorganics in addition to microbiological and heavymetals contaminants. The first case has relativelylower BOD and DS than the second. The treatedwater is to be reused for applications requiringlow to high quality (A to D as given in Fig. 2).

4.2. Results

The printouts of the results of the expertsystem for the two cases are presented in thefigures and tables of Case I and II respectively(see Appendix).

It is noticed that the optimum treatment schemehas been selected in view of the wastewatercharacteristics and the prevailing constraints. Inthe first case, where the BOD and DS arerelatively low and limited land is available, theselected biological treatment method is thetrickling filter while EDR has been the selecteddesalting method. Further, an RO unit has beenadded to treat the brine from the EDR thusreducing the cost of treatment of 1 m3 from $0.64to $0.52 for water of quality D.

In the second case, due to the relatively higherBOD, the method of choice for biological treatmenthas been activated sludge. Also, due to therelatively higher DS content, RO has been theselected desalting scheme. The cost of treatment

to obtain water of quality D has been estimatedto be about $0.64.

In essence, the following comments may beset forth:• The system succeeded to identify recommended

treatment trains.• The estimated capital and operating costs are

in accordance with typical prevailing cost levels.

These results indicate that the developed expertsystem provides management schemes and costestimate as a decision support tool for furtherintegrated planning.

5. Conclusions

The crucial need for reuse of agriculturaldrainage water necessitates the comparisonbetween alternative treatment schemes ontechnical, economic and environmental groundsto produce water suitable for particular purposesunder prevailing conditions and constraints.

The developed DDSS proved to be an efficientand reliable approach for identifying treatmenttrain and cost estimates when provided with thewastewater characteristics and governing require-ments and limitations as verified by typical casestudies.

It is recommended that this approach wouldbe• compemented with process design routines to

provide preliminary process designs and costestimates for a specific case under consider-ation.

• extended to optimize integrated managementand reuse schemes for a geographical area.

Abreviations

AC — Activated carbonAS — Activated sludgeAS — Aerated lagoonsChOx — Chemical oxidationCh.T — Chemical treatmentChOx — Chlorination


Dis — DisinfecationDMF — Dual media filterED — ElectrodialysisIE — Ion exchangeMF — MicrofiltrationNF — NanofiltrationNS — Natural sedimentationPL — Polishing lagoonsPT — PretreatmentRO — Reverse osmosisSec. Sed — Secondary sedimentationTF — Trickling filterWL — Wet land

References[1] The World Bank, Middle East and North Africa

Environmental Strategy, Feb. 15–17, 1995.[2] K.C. Dowflin and A.T. Lemley, J. Envir. Se. & Health.

Part B. Pesticides, Food Contamination and Agri-culture Waste, 30 (1995) 585–604.

[3] L.A. Rossman, J. Water Pollution Control Federation,52 (1980) 52–147.

[4] J.H. Ellis and G.J. Farquhar, J. Environ. Eng. (ASCE),111 (1985) 119–139.

[5] Tzafestas, Expert System in Engineering Application,Springer, Verlag, Berlin-Heidelberg, 1993.

[6] W. Wiggins and J. Kim, Expert System in Environ-

mental Planning, Springer ,Verlag Berlin Heidelberg,1993.

[7] N. Galil and Y. Levinsky, Wat. Sci. Tech., 24(6) (1991)291–298.

[8] R. Herrod, ISA, 24 (1989) 367–377.[9] M.W. Barnet and A.V. Costas, J. Env. Eng., 6 (1992)

118–125.[10] J.H. Tay and X. Zhang, J. Env. Eng., 125(12) (1999)

1149–1159.[11] Y.W. Hany, S. Shenoi and L.T. Fan, Expert System in

Civil Engineering, American Society of ChemicalEngineers, New York, 1989, pp. 145–158.

[12] S. Schibulam and P.H. Byer, Elsevier, 11 (1991) 11–27.

[13] S.R. Ahmed, M.H. Sorour, S.R. Tewfik and M.M.Fahmy, 5th International Conference on ArtificialIntelligence Application, Cairo, Egypt, 1997, pp. 156–174.

[14] S. Gillot, M.N. Pons and H. Spanjers, Report of the6th Working Meeting. 5, Vinno, Austria (2001) 1–19.

[15] G.L. Harris and Sc. Rose, Aspects of ApplicationBiology 30, 199 (1992) 199–202.

[16] Degremont Water Treatment Handbook, 6th ed.,Degremont, France, 1991.

[17] A.P. Jackman and R.L. Powell, Hazardous WasteTreatment Technology, Noyes Data Corporation, ParkRidge, NJ, 1991.

[18] S.R. Qusim, Wastewater Treatment Plants, CBSCollege Publishing, New York 1985.

[19] Novel Technologies for Drainage Water Treatment,Final Report, National Research Centre, Cairo, Egypt,2001.





Screen

Grit Removal

Equalization

Chemical Treatment

Tricking Filter

Secondary Sedimentation

Chlorination

Sand Filter

Activated Carbon

Electro Dialyses

Reverse Osmosis

Previous

Next Print screen

Quality A

Quality B

Quality C

Quality D

Appendix

Case I

Fig. A1. Treatment scheme for Case I.


Trickling Filter

Indicators Value Unit

O&G ppm

SS 9.8 ppm

DS 850 ppm

BOD 6.4 pm

COD 10.4 ppm

T.Phosp. 0.07 ppm

T.Nitrogen 0.87 ppm

T.Phenol ppm

T.H.Metals 0.5E-03 ppm

Bacteria 330445 V/100 ml

Pesticide ppm

Unit Cost

$ Million

Total

Operating Cost

Cost/m3 ($/m3)

6.2

6.6E-03

9.1

Previous ModuleBack Next ModulePRINT SCREEN

Sand Filter


O&G ppm

SS 1.4 ppm

DS 824 ppm

BOD 4.6 pm

COD 7.6 ppm

T.Phosp. 0.07 ppm

T.Nitrogen 0.8 ppm

T.Phenol ppm


Bacteria 826.1 V/100 ml

Pesticide ppm

Unit Cost

$ Million

Total

Operating Cost

Cost/m3 ($/m3)

4.5

4.8E-03

16.4

PRINT SCREEN Previous ModuleBack Next Module

Electrodialysis


O&G ppm

SS 0.1686 ppm

DS 285.8 ppm

BOD 0.34 pm

COD 0.55 ppm

T.Phosp. 0.04 ppm

T.Nitrogen 0.7 ppm

T.Phenol ppm


Bacteria 24.6 V/100 ml

Pesticide ppm

Unit Cost

$ Million

Total

Operating Cost

Cost/m3 ($/m3)

10.26

8.88E-02

32.96


Treatment train: screen–grit removal–equalization–chemical treatment–trickling filter. Case I.

Treatment train: screen–grit removal–equalization–chemical treatment–trickling filter–chlorinating–sand filter. Case I.

Treatment train: screen–grit removal–equalization–chemical treatment–trickling filter–chlorinating–sand filter–activatedcarbon–electrodialysis. Case I.


Screen

Grit Removal

Equalization

Chemical Treatment

Activated Sludge

Chlorination

Sand Filter

Activated Carbon

Reverse Osmosis

Sec Reverse

Osmosis

Previous

Next

Print screen

Quality A

Quality B

Quality C

Brine

Quality D

Case II

Table A1Cost indicators of the proposed treatment scheme Case I

Item Capital cost, million $

Annual depreciation,$1000/y

Annual operating cost,$1000/y

Total annual cost, $1000/y

Cost/M3,

$/m

3

Water quality

Suction pumps 1.1 113.8 113.8 0.0062 Screen 0.71 70.7 9.8 80.5 0.0044 Grit removal 0.38 19.0 11.6 30.6 0.0017 Equalization 0.14 6.9 8.9 15.8 0.0009 Chemical treatment 2.53 168.9 355.1 524.0 0.0287 Trickling filter 7.81 520.5 120.4 640.9 0.0351 Secondary sedimentation 2.30 153.6 151.2 304.8 0.0167 Subtotal 15.01 1053.5 657.0 1710.5 0.0937 A

Chlorination 1.22 121.9 116.0 237.9 0.0130 Sand filter 5.76 576.0 87.4 663.4 0.0364 Subtotal 21.99 1751.4 860.4 2611.8 0.1431 B

Activated carbon 7.94 793.6 626.3 1419.9 0.0778 Subtotal 29.92 2544.98 1486.70 4031.68 0.22 C

Electrodialysis 12.80 1280.0 1621.7 2901.7 0.2271 Subtotal 42.72 3824.98 3108.40 6933.38 0.64 D

Reverse osmosis (9000 m3/d brine) 4.20 419.8 929.4 1349.2 0.4107

Total 46.92 4244.82 4037.79 8282.6 0.52

Fig. A2. Treatment scheme for Case II.


Case II

Activated Sludge


O&G ppm

SS 21.8 ppm

DS 2541 ppm

BOD 21.15 pm

COD 32.55 ppm

T.Phosp. 0.06 ppm

T.Nitrogen 0.55 ppm

T.Phenol ppm

T.H.Metals 0.2 ppm

Bacteria 165225 V/100 ml

Pesticide ppm

Unit Cost

$ Million

Total

Operating Cost

Cost/m3 ($/m3)

4.9

3.2E-02

7.84


Treatment train: screen–grit removal–equalization–chemical treatment–activated sludge. Case II.

Activated Carbon


O&G ppm

SS 4 ppm

DS 2466 ppm

BOD 3.3 pm

COD 5.1 ppm

T.Phosp. 0.18 ppm

T.Nitrogen 0.53 ppm

T.Phenol ppm

T.H.Metals 0.012 ppm

Bacteria V/100 ml

Pesticide ppm

Unit Cost

$ Million

Total

Operating Cost

Cost/m3 ($/m3)

6.2

0.034

19.6


Treatment train: screen–grit removal–equalization–chemical treatment–trickling filter–chlorinating–sand filter–activated carbon.Case II.


Table A2Cost indicators of the proposed treatment scheme Case II

Item Capital cost, million $

Annual depreciation,$1000/y

Annual operating cost,$1000/y

Total annual cost, $1000/y

Cost/M3,

$/m

3

Water quality

Suction pumps 1.1 113.8 113.8 0.0062 Screen 0.71 70.7 9.8 80.5 0.0044 Grit removal 0.38 19.0 11.6 30.6 0.0017 Equalization 0.14 6.9 8.9 15.8 0.0009 Chemical treatment 2.53 168.9 355.1 524.0 0.0287 Activated sludge 6.27 418.1 180.0 598.1 0.0328 Subtotal 11.17 797.5 565.4 1362.9 0.0747 A

Chlorination 1.22 121.9 116.0 237.9 0.0130 Sand filter 5.76 576.0 87.4 663.4 0.0364 Subtotal 18.15 1495.4 768.8 2264.2 0.1241 B

Activated carbon 7.94 793.6 626.3 1419.9 0.0778 Subtotal 26.08 2288.98 1395.10 3684.08 0.20 C

Microfiltration 2.66 266.2 261.4 527.6 0.0289 Reverse osmosis (3000 m

3/d) 8.52 852.2 3000.0 3852.2 0.3518

Subtotal 37.27 3407.44 4656.50 8063.94 0.7364 D

Sec. reverse osmosis (brine 12 m3/d) 4.98 497.9 1200.00 1697.9 0.3877

Total 42.25 3905.36 5856.50 9761.86 0.637

Reverse Osmosis


O&G ppm

SS 0.015 ppm

DS 122.1 ppm

BOD 2.9 pm

COD 4.49 ppm

T.Phosp. 0.2 ppm

T.Nitrogen 0.5 ppm

T.Phenol ppm


Bacteria V/100 ml

Pesticide ppm

Unit Cost

$ Million

Total

Unit Operating Cost

Cost/m3 ($/m3)

6.66

0.3

28.3


Treatment train: screen–grit removal–equalization–chemical treatment–trickling filter–chlorinating–sand filter–activatedcarbon–electrodialysis. Case II.

Development and verification of a decision support system for the selection of optimum water reuse...

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