Chapter 5 DSS Testing and Sensitivity Analyses

Equation Chapter (Next) Section 1

5.1 Introduction

The methodologies for evaluation and optimisation of integrated water reuse systems were implemented into a user-friendly hydroinformatics tool. Full features of the developed DSS tool for Water Treatment for Reuse with Network Distribution (WTRNet) are described in Appendix B, which also serves as the software users’ manual. WTRNet was first utilised to test the concepts embodied in the DSS and conduct the sensitivity analyses of some of the parameters using smaller test cases, prior to applying it on a larger-scale case study. The results of these efforts are presented in this Chapter. The purpose of DSS testing was to examine how the developed methodologies achieved their intended purpose, and to develop any necessary modifications, while the sensitivity analyses focused on some of the parameters and weights used as default. For both activities to take place, test case studies were needed that had features appropriate for the kind of analyses that were to be performed. Two separate test cases were developed, which are described in the remainder of this section. Two sections that follow discuss the results of testing and sensitivity analyses, respectively, and an overall summary with conclusions reached from these activities conclude this Chapter.

5.1.1 London Test Case

The first test case was developed primarily for the purpose of testing of the sequential approach for distribution system sizing. Therefore, large scale wastewater treatment plants combined with distributed potential end-users requiring seasonally varying demand were sought. After conducting various searches for appropriate area that could be used to develop a hypothetical water reuse scheme, a sewage treatment plant located in London Borough of Hounslow and several golf courses in the general vicinity were identified as fitting the desired criteria. The Mogden Sewage Treatment Works (STW) occupies almost 50ha of land, and it is one of the largest wastewater treatment plants in Europe and second largest plant run by 125 Chapter 5 - DSS Testing and Sensitivity Analyses

Thames Water (Koodie and Kirkaldy 2000). It treats effluent from areas North and West of London inhabited by 1.8 million people. First built in 1936, the Mogden STW treats an average flow of 500,000 m³/day, which is just over one half of its rated capacity of 810,000 m³/day. The plant has two parallel treatment trains which include initial screening and de-gritting of raw sewage, primary clarifiers and activated sludge process, in addition to having large volume retention tanks used for wet weather flows exceeding the plant capacity. The importance of its performance is heightened by the fact that its effluent constitutes a major portion of River Thames dry weather flows that pass through central London.

Figure 5.1 Mogden STW Serviced Area (Thames Water 2006)

The test case considers several golf courses located in the general vicinity of Mogden STW as potential end-users of reclaimed water. Their monthly irrigation demands, summarised in Table 5.1, were estimated based on assumed irrigation areas and average weather conditions for London and represent a small fraction of flows that are treated at Mogden STW.

126 Chapter 5 - DSS Testing and Sensitivity Analyses

Table 5.1 Estimated Demands of Potential End-users in London Test Case

End-user Monthly Demand (m³) Total Royal Month Wyke Demand Airlinks Mid Richmond Fulwell Green (m³) Surrey Jan 82 46 118 61 113 420 Feb 530 297 766 396 736 2,725 Mar 634 355 915 473 880 3,257 Apr 890 498 1,286 665 1,236 4,575 May 1,168 654 1,687 872 1,622 6,002 Jun 1,400 784 2,022 1,045 1,945 7,197 Jul 1,760 986 2,542 1,314 2,445 9,047 Aug 1,463 819 2,113 1,092 2,032 7,520 Sep 836 468 1,208 624 1,161 4,298 Oct 266 149 384 199 370 1,368 Nov 145 81 209 108 201 743 Dec 7 4 10 5 9 34

5.1.2 Kyjov Test Case

The second test case was developed as part of author’s involvement on the AQUAREC project, and was actually the case study used in that project to demonstrate the developed DSS methodology and the WTRNet tool. The test case involved studying industrial water reuse options in the city of Kyjov, located in the South Moravia area of the Czech Republic. The input requirements for the DSS tool were drawn by the author, who also guided the assembling of information by others (Janosova 2005; Kubik 2005) and developed the WTRNet model of the test case. The industrial zone of Kyjov is approximately 5 ha in size, within which the majority of businesses are in the metal and glass industries. The wastewater treatment plant (WWTP) in Kyjov, which is in the immediate vicinity of the industrial zone, is sized for approximately 26,000 population equivalents (PE), and currently receives an average flow of 9,500 m³/d. The WWTP is a mechanical-biological treatment plant with aerobic stabilisation of sludge. Collected sewage is pre-treated using a bar screen, mechanical fine screen and a grit chamber. Another operational complex forms a biological treatment stage, which includes a circulating activation tank, secondary settling tanks, and a pumping station for re-circulated and excess sludge.

127 Chapter 5 - DSS Testing and Sensitivity Analyses

Six industries were identified as potential end-users of upgraded wastewater from the Kyjov WWTP, whose locations relative to the WWTP are shown in Figure 5.2. Table 5.2 displays the details of these industries, along with their estimated quantity requirements for reclaimed water. The total reclaimed water demand estimated for these users represents less than 10% of the current plant average flow. Nevertheless, an assumption was made that 10% of the effluent from the WWTP would need to receive additional treatment in order to satisfy the requirements of these potential users.

MLÉKÁRNA Kyjov a.s.

VETROPACK MORAVIA GLASS a.s. ŠROUBÁRNA Kyjov spol. s r.o.

ŠEBESTA spol. s r.o.

WWTP EK OR s.r.o.

BETAS MORAVIA a.s.

Figure 5.2 Kyjov Test Case Overview (Geonardo 2005)

Table 5.2 Demand of Potential End-users of Reclaimed Water in Kyjov

Estimated Water Company Industry Type Demand (m3/d) Manufacturing of packaged Sebesta spol. s r.o. 23 wastewater treatment plants Manufacturing of building and KM Beta a.s. 35 roofing products Sroubarna Kyjov spol. s r.o Manufacturing of fasteners 122 EKOR s.r.o Waste management 9 Mlekarna Kyjov, a.s. Dairy works 74 Vetropack Moravia Glass a.s Glass manufacturer 297 Total Demand 600

128 Chapter 5 - DSS Testing and Sensitivity Analyses 5.2 Testing

5.2.1 Sequential Approach for Distribution System Sizing

The testing of the sequential approach used in the DSS for sizing of all distribution system components, conducted using the London test case, was carried out primarily to confirm that the algorithm functions as intended in a variety of situations. An overview of the location of the Mogden STW relative to the end-users considered is shown in Figure 5.3, in which the distribution system components are also indicated. The test case includes three possible storage elements (earthen basins) and three possible pumping locations. The first storage facility is located at the Mogden STW, and its maximum volume was set at 200,000 m³. Central pumping was also assumed at that location. The remaining two possible storage facilities were limited to 100,000 m³ each, and they are located near the Royal Mid Surrey and Wyke Green golf courses. Off-line pumping stations were also placed at both of these locations.

Figure 5.3 London Test Case Overview

As described in Section 3.4, the application of the sequential approach will vary according to the availability and distribution of available reclaimed water and potential 129 Chapter 5 - DSS Testing and Sensitivity Analyses end-user demands over the course of the year. The approach uses different formulations of the NLP model, and also excludes the NLP altogether in cases of adequate quantity and distribution of demand. With golf course irrigation demands fixed at their estimated values, the testing consisted of changing the volume of available reclaimed water (i.e. the size of the reclamation facility) and analysing the optimisation results related to the distribution system. Unit costs of alternate supplies, which can be used to indicate user preferences for providing reclaimed water to different end-users, were all fixed at a constant value of 0.5 €/m³ throughout the testing. The size of the reclamation facility was varied between 500 m³/day, where storage would be needed but most of demands would remain unsatisfied, and 9,500 m³/day, at which point all maximum monthly demands could be met without the need for storage. Results of the analyses described above are summarised in Figure 5.4. The graph on the left side shows the optimal sizes of storage facilities for different treatment capacities determined using the sequential approach, and the graph on the right side shows the resulting annual shortfalls and spills. The first remark made on the results is that the algorithm correctly increased the overall storage capacity of the system up to the point where only additional treatment capacity could further decrease the shortfalls. Beyond that point, the need for storage facilities diminishes but is still necessary to balance the varying monthly demands with fixed production of reclaimed water, indicating that the general performance of the approach is correct for a wide range of conditions.

2,000 Wyke Green Airlinks Wyke Green 1,800 400 Royal Mid Surrey Royal Mid Surrey Richmond ³ Fulwell Spill ) Mogden STW 1,600 300 1,400 1,200 1,000 200 800 600 Storage Size (x10³ m³

100 m (x10³ Spill or Shortfall 400 200 0 0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 Supply (x10³ m³/day) Supply (x10³ m³/day)

Figure 5.4 Storage and Shortfall Volumes - London Test Case

By further examining the displayed results, it appears that Royal Mid Surrey and Richmond golf courses were favoured as their demands were the first to be met. The main reason the algorithm favoured these locations is that conveying reclaimed water to

130 Chapter 5 - DSS Testing and Sensitivity Analyses them does not incur extensive cost, as both are at lowest elevation amongst the potential end users. Also, if one takes into consideration that the assumed water distribution system includes three main branches, this branch towards Royal Mid Surrey and Richmond golf courses has the highest demands and addressing it first results in largest possible reduction in overall shortfalls. The storage sizing by the NLP model first utilises the Mogden STW storage which, being at the source, addresses all shortfalls. As the treatment capacity is expanded to 2,500 m³/day most of demands at Royal Mid Surrey and Richmond golf courses are satisfied. The storage capacity at Mogden STW is exhausted at this treatment rate, and storage needed to reduce the shortfalls in the northern branch of the distribution system is appropriately selected at Wyke Green. With total shortfalls of the northern and southern end-users approximately equalised at this point, further expansions in the storage capacity are equally divided between the Wyke Green and Royal Mid Surrey sites. A review of optimisation results related to costing of water distribution system components was also conducted on the London test case to confirm the adequacy of the approach. The top graph included in Figure 5.5 shows the capital costs of the reclaimed water distribution system over the full range of treatment capacities. The capital costs of links (i.e. pipes) and pumping facilities increase up to the 4,000 m³/day treatment capacity, at which point the peak monthly demands for all users are satisfied, and remain constant for higher treatment capacities. The capital costs of storage facilities reach their peak at the same treatment capacity, and are gradually reduced as the need for storage becomes smaller with higher treatment capacities. The O&M costs for pipes and storage facilities are related to their capital costs, and are calculated for pumping facilities from annual pumped volumes. Again, the O&M costs of pumping rise until no shortfalls occur, and remain constant beyond that point.

131 Chapter 5 - DSS Testing and Sensitivity Analyses

a) Link Pump Storage

16.0 14.0

€) 12.0 6 10.0 8.0 6.0 4.0 Capital Cost (x10 (x10 Cost Capital 2.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 Supply (x10³ m³/day)

b)

1.8 1.6 €) 6 1.4 1.2 1.0 0.8 0.6 0.4

Annual O&M Cost (x10 (x10 Cost O&M Annual 0.2 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 Supply (x10³ m³/day)

c)

3.5

3.0 €) 6 2.5

2.0

1.5

1.0

Total Annual Cost (x10 (x10 Cost Annual Total 0.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 Supply (x10³ m³/day)

Figure 5.5 Costing Results – London Test Case: a) Capital Costs b) O&M Costs c) Total Annual Costs

132 Chapter 5 - DSS Testing and Sensitivity Analyses

5.2.2 GA Operators

5.2.2.1 Initial Population

The efficiency of the generation of the initial population was tested by creating populations containing 10,000 individuals, and analysing the composition of the generated population. The first test performed examined the impact of including the possibility of ending the formation of treatment trains early by introducing the “zero option”, as described in Section 4.6.2. The distribution of treatment train lengths generated with and without the “zero option” for raw sewage influent is shown in Figure 5.6. While the lengths of treatment trains generated using either way ranged from 2 to 11, the average treatment train length (3.7 and 4.3) and the distribution of lengths was considered to favour the exclusion of the “zero option” due to the more realistic number of unit processes included in the generation process.

With "zero option" Without "zero option"

s 30%

25%

20%

15%

10%

5% Percent of test population treatment train treatment population test of Percent 0% 234567891011 Treatment train length

Figure 5.6 Distributions of Test Treatment Train Lengths

The exclusion of the “zero option” was further verified by calculating which of the generated treatment trains meet different water reuse criteria for effluent concentration. Using typical raw wastewater concentrations as input and criteria values outlined in the previous Chapter, each generated treatment train was evaluated. Overall, the percentage of treatment trains that meet different criteria varied between 3% and 8% using the “zero option”, and was slightly higher (5 to 10%) when the option was excluded in the generation process. While relatively small, this percentage of practical treatment trains was considered appropriate, although the higher value, achieved by excluding the “zero option” could potentially lead to faster convergence of the GA to better solutions.

133 Chapter 5 - DSS Testing and Sensitivity Analyses

Additional analyses were performed to examine the lengths of those treatment trains that met different criteria, whose results are shown in Figure 5.7. The results do not indicate a large difference in the distributions of lengths of treatment trains meeting criteria between the two approaches. However, the distributions resemble the composition of the overall population generated without the “zero option” more closely, therefore favouring it over the original formulation. In addition, a review of the frequency with which each unit process in the knowledge base was selected indicated that the probability of selecting processes from tertiary and disinfection categories was higher if the “zero option” was excluded, which was also considered favourable.

With "zero option" Without "zero option"

s 35%

30%

25%

20%

15%

10%

5% Percent of test population treatment train treatment population test of Percent 0% 234567891011 Treatment train length

Figure 5.7 Distributions of Lengths of Test Treatment Trains Meeting Criteria

The impact of the “zero option” was also tested by generating test populations consisting of 10,000 alternative designs using primary effluent as source. Based on the results of these analyses summarised in Table 5.3 and results presented above, the “zero option” was excluded from the procedure used to generate the initial population.

Table 5.3 Test Population Analyses Results for Primary Effluents

Zero option used Yes No Minimum treatment train length 2 2 Average treatment train length 2.9 3.3 Maximum treatment train length 7 8 Treatment trains meeting criteria 6-12% 8-14%

The maximum time taken to generate the 10,000 feasible design alternatives comprising the initial population created in testing (using raw sewage as influent and six potential

134 Chapter 5 - DSS Testing and Sensitivity Analyses end-users) was only 4.6 seconds. For comparison, (Dinesh 2002) reported that the time required by MOSTWATER to generate the initial population of 100 treatment trains that met the treatment train rules ranged from 2 to 6 minutes. The reported time varied depending on which of the several methods for random generation incorporated in the DSS was used. Although the processor used in this work is of higher capacity, a general conclusion can be made that that the process of generation of initial population developed and implemented here is more efficient by orders of magnitude.

5.2.2.2 Crossover Operator

The testing of the crossover operator described in Section 4.6.3 was conducted on a test population consisting of 10,000 individuals, and applying the operator to determine its effectiveness. It should also be mentioned that simpler versions of the crossover operator were attempted prior to deciding to follow the procedure adopted, but the testing of these earlier versions is not reported here. The effectiveness of the operator was measured mainly by determining the number of feasible crossover points and determining how often the crossover of complete treatment trains needed to be performed. Test populations of individuals were generated for two influent quality conditions, since they are used in the algorithm to define the maximum treatment train lengths (length of the chromosome) where the crossover operator was expected to perform differently. In the testing, two parents were picked at random from the test population of individuals, and they were analysed to determine the following:

• numbers of potential mating categories (PMCs),

• distribution of mating categories selected for crossover, and

• frequency of failure to perform the crossover due to presence of “complicating” unit processes or exhaustion of potential mating categories. The selection of two parent individuals from the test population was performed 10,000 times, for each inflow quality condition. For each pair of individuals selected at random, the number of potential mating categories was first determined. The results of these analyses, shown in Figure 5.8, showed that the number of PMCs ranged between one and five. The distribution of PMCs is obviously influenced by the inflow, since treatment trains for cleaner influent conditions contain unit processes from a smaller number of categories. For raw wastewater influent, the number of PMCs was between one and five, with approximately one half of tested individuals having four PMCs. Unit

135 Chapter 5 - DSS Testing and Sensitivity Analyses processes from three categories could be exchanged for primary treated influent in 84% of the cases.

100%

80%

5 60% 4 3 upulation Individual upulation 40% 2 1

20% Percent of Test P

0% Raw wastewater Primary treated

Figure 5.8 Percent of Test Population Individuals with Different Number of Potential Mating Categories

The crossover operator was then applied to the individuals selected for mating from each of the test populations. The offspring generated in this way was then analysed to determine which unit process categories, if any, were actually used in the crossover. Results of the analyses for raw sewage influent conditions, shown in Figure 5.9, show the distribution of categories used in the mating. Unit processes belonging to the disinfection treatment were used for crossover in almost one third of test individuals, approximately one fifth exchanged unit processes from the secondary and tertiary categories, and about one in ten exchanged unit processes was from the primary category. The category labelled ‘Other’ indicates the instances in which the preliminary treatment category was used, either because it was selected randomly in the crossover procedure, because the swap of treatment trains was necessary due to the presence of “complicating” unit processes, or due to infeasibility of generated offspring using any identified PMC for crossover. In 23% of test cases, the treatment train portions of parent chromosomes were swapped, and slightly more than a half of those were

136 Chapter 5 - DSS Testing and Sensitivity Analyses performed due to the latter two conditions. Although potentially good genetic material is not necessarily created through the crossover of this type, the performance of the operator was considered adequate.

Disinfection, Preliminary 32% Selected, 9%

Complicating, Tertiary, Other, 23% 3%

18% P r im a Exhausted, r Secondary, y , 11% 9 19% %

Figure 5.9 Distribution of Mating Categories – Raw Sewage Inflow

The same analyses were performed on test treatment trains mated for the primary treated influent conditions, and the results are presented in Figure 5.10. The frequency of occurrence where the treatment trains were swapped between parent chromosomes was similar to that observed for raw sewage inflow.

Complicating, 3% Disinfection, 42%

Other, 20% Exhausted, Se 18% c o n d Tertiary, 23% a r y , 1 5 %

Figure 5.10 Distribution of Mating Categories – Primary Treated Inflow

137 Chapter 5 - DSS Testing and Sensitivity Analyses

The overall conclusion reached from above analyses is that for up to one quarter of applications of the crossover operator, a swap in end-users served by the parent treatment trains will be created in the offspring. Although the treatment portions of chromosomes will remain unchanged in these situations, a potentially better offspring may be created this way. The exact implications of these findings are difficult to predict, but it can be concluded that the operator for the most part successfully performs the intended function of exchanging the genetic information (unit processes) between parent individuals.

5.2.2.3 Mutation Operator

The efficiency of the mutation operator was tested by generating a test population of 10,000 individuals, and analysing how the operator would be applied to each individual. Again, the focus of the test was on the treatment part of the chromosome, since the mutation of the end-user portion is trivial. The concern with the treatment train portion was that the mutation operator could not be performed on some chromosomes. This situation would arise if no feasible treatment trains would result from applying the mutation operator regardless of the unit process chosen (refer to Figure 4.9). To determine if and how often the condition described above occurs using the mutation operator described earlier, treatment train portions of all individuals in test population were examined. The examination was performed on a unit process level, and all possible replacements for each unit processes in each treatment train were calculated. A summary of results of this exercise is shown in Figure 5.11, in which the length of the treatment train examined is shown on the x-axis, while the y-axis indicates the position of the unit process examined. Colour is then used to indicate the average number of alternative unit processes that could replace a unit process at a particular position in a treatment train, for treatment trains of different lengths. As can be expected, the number of alternatives is reduced in longer treatment trains, but not always as a function of the position of the unit process in the treatment train. There were also a number of test chromosomes that had treatment trains that could not undergo mutation and these were also analysed. The overall results shown in Figure 5.12 indicate that longer treatment trains are more likely to exhibit this characteristic. However, with less than 3.5% of test treatment trains affected a conclusion is drawn that the effective mutation frequency of the treatment train portion of the chromosome will be only slightly smaller that the probability of mutation specified by the user (on the average). This will be offset by proportionately more mutations of the end-user portions of the chromosome. 138 Chapter 5 - DSS Testing and Sensitivity Analyses

9

26-28 8 24-26 n 22-24 7 20-22 6 18-20 16-18 5 14-16 12-14 4 10-12 8-10 3 Trai Treatment in Position 6-8 2 4-6 2-4 1 0-2 234567891011 Treatment Train Length

Figure 5.11 Possible Unit Process Replacements using Mutation Operator

9 s 8

7

3.0%-3.5% 6 2.5%-3.0% 2.0%-2.5% 5 1.5%-2.0% 4 1.0%-1.5% 0.5%-1.0% 3 0.0%-0.5% 2 No. of Unit Processes Without Alternative Without Processes Unit of No.

1 234567891011 Treatment Train Length

Figure 5.12 Percent of Treatment Trains without Mutation Alternatives

5.3 Sensitivity Analyses

5.3.1 NLP Model Parameters

The key factors in the NLP model used for determining the optimal operating strategy and sizing of storage elements are locations of source and end-users, end-user demands and costs of alternate supply. Since the locations and demands of end-users are most likely to be fixed, the only factor that influences how the algorithm distributes the available reclaimed water and sizes storage elements is the cost of alternate supply specified by the user. Due to importance of this factor, sensitivity analyses were carried

139 Chapter 5 - DSS Testing and Sensitivity Analyses out using the London test case to develop a better understanding of its impact on the optimal strategy determined by the NLP model. The sensitivity of the NLP optimisation model was conducted by varying the values used for alternate supply in the ±50% range of their original values of 0.50 €/m³, while keeping the other model parameters fixed at values shown in Table 5.4. The optimisation was repeated at three levels of treatment capacity (1,000 m³/day, 2,000 m³/day and 3,000 m³/day) to examine the impacts in different water scarcity situations. Detailed results of the sensitivity analyses are provided in Appendix C, while graphs shown in Figure 5.13 summarise the results showing the changes in end-user shortfalls (as percentage of annual demand) over a range of their unit costs of alternate supply.

Table 5.4 London Test Case Input Parameters for NLP Model Testing

Parameter Value Storage facility type (all) Earthen Basin Mogden STW maximum storage capacity (m³) 200,000 Wyke Green maximum storage capacity (m³) 100,000 Royal Mid Surrey maximum storage capacity (m³) 100,000 Pumping station types (all) Off-line Area land use Suburban Available pipe diameters (mm) 200, 300, 400, 550, 650, 800 Demand head for all end-users (m) 10 Peak hourly demand factor for all end-users 1.5 Node elevations (m) Mogden STW 10 Royal Mid Surrey golf course 5 Storage/pumping node at Royal Mid Surrey 5 Richmond golf course 14 Airlinks golf course 32 Wyke Green golf course 23 Storage/pumping node at Wyke Green 25 Fulwell golf course 21

It is evident from the results that the sensitivity of this parameter is moderate if the cost of alternate supply a single user is lowered below a level set for all other users, regardless of the scarcity level or the size of end-user demands relative to others. However, sudden major shifts in volumes of reclaimed water allocated to users were evident in most cases where the parameter was raised for a single user. The shifts

140 Chapter 5 - DSS Testing and Sensitivity Analyses occurred as a result of change in unit cost of alternate supply ranging from 10% to 40%. These shifts appear to be more gradual if reclaimed water is less scarce and also for higher demand end-users. For example, raising the unit costs of alternate supply at the Royal Mid Surrey and Fulwell golf courses up to 50% of their original values resulted in a gradual decrease in their shortfalls with the treatment capacity set at 1,000 m³/day. Contrasting that is the Wyke Green golf course, whose demand of less than half of the Royal Mid Surrey or Fulwell was fully met if the unit cost of alternate supply was raised by 20% at treatment capacities higher than 2,000 m³/day, and by only 10% with the treatment capacity set at 1,000 m³/day.

Royal Mid Surrey Richmond 100% 100% 80% 80% 60% 60% 40% 40% Shortfall Shortfall Shortfall Demand) Demand) 20% 20% (Percent ofAnnual (Percent of Annual 0% 0% -10% -8% -6% -4% -2% 0% 2% 4% 6% 8% 10% -10% -8% -6% -4% -2% 0% 2% 4% 6% 8% 10% Change in Unit Cost of Alternate Supply Change in Unit Cost of Alternate Supply

Airlinks Wyke Green 100% 100% 80% 80% 60% 60% 40% 40% Shortfall Shortfall Demand) Demand) 20% 20% (Percent of Annual Annual of (Percent (Percent of Annual 0% 0% -10% -8% -6% -4% -2% 0% 2% 4% 6% 8% 10% -10% -8% -6% -4% -2% 0% 2% 4% 6% 8% 10% Change in Unit Cost of Alternate Supply Change in Unit Cost of Alternate Supply

100% Fulwell 100% 80% 80% 60% 60% 1,000 m³/day 40% 40% 2,000 m³/day Shortfall Demand) 20% 3,000 m³/day

(Percent of Annual 20% 0% -10% -8% -6%0% -4% -2% 0% 2% 4% 6% 8% 10% -20 -16 -12 -8 Change-4 0% in 4%Unit 8%Cost 12of Alternate16 20 Supply % % % % % % % %

Figure 5.13 Sensitivity of NLP Optimisation on Unit Cost of Alternate Supply

Further examinations of complete optimisation results (see Appendix C) showed that reductions in shortage of individual users were achieved mainly by diverting reclaimed water from the Royal Mid Surrey golf course, at treatment levels of 1,000 and 2,000 m³/day. In effect, the favourable location and demand of this end-user were effectively cancelled out by increasing the importance of other demands in the system. At treatment capacity of 3,000 m³/day, the demand at the Royal Mid Surrey golf course is 141 Chapter 5 - DSS Testing and Sensitivity Analyses already fully satisfied, and shortfalls are introduced mainly at Richmond golf course to satisfy demands of other users. Lowering the importance of any end-user, by reducing its cost of alternate supply, results in a more diverse re-distribution of available water to other users without any patterns that could lead to further conclusions.

5.3.2 GA Population Size and Operator Values

The effects of population size and values of the crossover and mutation operators were conducted on the Kyjov case study described earlier. Prior to conducting the optimisation runs with different EA parameters (population size, crossover rate, mutation rate), enumeration of all design alternatives was conducted. The purpose of conducting enumeration was two-fold: to determine the global optimal value of the fitness function of the design problem, and to use the results for evaluation of EA parameters. The total number of design alternatives evaluated for the Kyjov case study is 1.88x108, which is determined by multiplying all user combinations (six users can be combined in 64 different ways) with the number of possible treatment trains with no existing treatment assumed (2,942,221). In order to expedite the computation, optimal sizing of the distribution system for each user combination was determined in advance using the LP model (no seasonal storage was required in this case study). These results were then combined with the results of evaluation of each treatment train arrangement, to determine the fitness value of each design alternative. Once the fitness of each design alternative was calculated, post- processing of results involved extracting the design alternatives with highest fitness values for each end-user combination, and sorting the results in a decreasing order according to fitness values. Twenty design alternatives with highest (and positive) fitness values were then extracted for each combination of end-users, to determine the cumulative distribution of their fitness functions shown in Figure 5.14. It is emphasised that the distribution shown does not represent the distribution of the fitness function for all possible design alternatives, which would be much more skewed to the lower values.

142 Chapter 5 - DSS Testing and Sensitivity Analyses

100%

y 80%

60%

40%

Cumulative Frequenc 20%

0% 0.00 0.40 0.80 1.20 1.60 2.00 2.40

Fitness (x106)

Figure 5.14 Cumulative Distribution of Top Design Alternatives

With the global optima known for the case study, the GA optimisation was applied. Fitness function indicated by Equation 4.1 was used both in enumeration of design alternatives and for determination of the best GA parameters, rather than the multi- objective formulation that would complicate the investigation substantially. After some preliminary investigations, the values of GA parameters shown in Table 5.5 were selected for detailed testing. The testing itself involved conducting 40 GA model runs for each combination of parameters (252 combinations in total), and determining the success rate of GA finding the known optimal solution to the test case study. The stopping criterion used in all runs was the maximum number of generations, which was set at 100.

Table 5.5 GA Parameter Values Tested

GA Parameter Values Used in Testing Population size 100, 150, 200, 300 Crossover probability 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99 Mutation probability 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99

A summary of the GA parameter testing results is shown in Figure 5.15, which gives an overview of the success rate in reaching the optimal solution with all combinations of parameters tested. It is immediately evident that an increase in population size has dramatic effects on the success of finding the optimal solution. While the rate never exceeded 30% for the population size of 100, when the population size was raised to just 150 resulted in the success rate reaching 70%. Doubling the first tested population

143 Chapter 5 - DSS Testing and Sensitivity Analyses size to 200 resulted in a slight increase of the maximum success rate in fairly narrow regions of crossover and mutation probabilities, although the overall spread of the success rate was much better for a wide number of combinations. The maximum success rate was reached using the population size of 300, where a success rate of up to 90% was achieved using several combinations of crossover and mutation probabilities, and a rate of 70% was achieved using a majority of probability combinations in the tested range. A general observation is made that the crossover probability ranging of 0.80 to 0.99, in combination with probabilities of an individual being mutated between 0.30 and 0.70, resulted in the highest success rate of finding the optimal solution for the case study used.

Population Size = 100 Population Size = 150

0.50 0.50 0.60 0.60 0.70 0.70 0.80 0.80 0.90 0.90 Crossover Rate Crossover 0.95 0.95 Crossover Rate 0.99 0.99 0.30 0.40 0.50 0.60 0.70 0.80 0.90 0.95 0.99 0.30 0.40 0.50 0.60 0.70 0.80 0.90 0.95 0.99 Mutation Rate Mutation Rate

0.50

Population Size = 200 Population Size 0.80= 300

0.99 0.50 0.50 Mutation Rat e 0.60 0.60 0.70 0.70 0.80 0.80 0.90 0.90

0.95 Rate Crossover 0.95 Rate Crossover 0.99 0.99 0.30 0.40 0.50 0.60 0.70 0.80 0.90 0.95 0.99 0.30 0.40 0.50 0.60 0.70 0.80 0.90 0.95 0.99 Mutation Rate Mutation Rate

0%-10% 10%-20% 20%-30% 30%-40% 40%-50% 50%-60% 60%-70% 70%-80% 80%-90%

Figure 5.15 GA Parameter Testing Results – Success Rate

The higher success rate achieved with larger population size also comes with increased computational cost, since a larger number of evaluations of design alternatives need to be performed. To investigate this further, data recorded during repeated optimisation runs was further analysed. The fitness of the best performing individual in each generation was recorded during all optimisation runs, and used to determine the average number of fitness function evaluations performed in determination the global optimum.

144 Chapter 5 - DSS Testing and Sensitivity Analyses

The results of these analyses, shown in Figure 5.16, indicate that the number of fitness function evaluations needed to find the optimal solution was generally lower than 7,000 in the most promising regions regardless of the population size. The computational effort exerted using the population size that showed the best results in terms of success rate (300), was not much higher than that required for lower population sizes, which required up to 5,000 fitness function evaluations.

Population Size = 100 Population Size = 150

0.50 0.50 0.60 0.60 0.70 0.70 0.80 0.80 0.90 0.90

0.95 Rate Crossover 0.95 Rate Crossover 0.99 0.99 0.30 0.40 0.50 0.60 0.70 0.80 0.90 0.95 0.99 0.30 0.40 0.50 0.60 0.70 0.80 0.90 0.95 0.99 Mutation Rate Mutation Rate

Population Size = 200 Population Size = 300

0.5 0 0.50 0.50 0.9 0 0.60 0.60

Mu tation Rate 0.70 0.70 0.80 0.80 0.90 0.90

0.95 Rate Crossover 0.95 Rate Crossover 0.99 0.99 0.30 0.40 0.50 0.60 0.70 0.80 0.90 0.95 0.99 0.30 0.40 0.50 0.60 0.70 0.80 0.90 0.95 0.99 Mutation Rate Mutation Rate

1,000-3,000 3,000-5,000 5,000-7,000 7,000-9,000 9,000-11,000 11,000-13,000

Figure 5.16 GA Parameter Testing Results – Computational Effort

Based on the results of testing presented above, the population size of 300 was selected as the most promising and used in optimisation runs conducted in this research. While single best values for the crossover and mutation probabilities are difficult to ascertain from the results, the widest region of most successful optimisation runs is around the crossover probability of 0.80 and mutation probability of 0.40. Therefore, these were the parameter values used in further optimisation runs. The results presented in Figure 5.16 were derived by multiplying the population size with the generation number on which the optimal solutions were found. In order to determine the GA stopping criteria for further optimisation runs, distributions of generation numbers in which the optimal solution was found were plotted for different 145 Chapter 5 - DSS Testing and Sensitivity Analyses population sizes used in sensitivity analyses. This plot, shown in Figure 5.17, indicates that the optimal solutions were found by running the optimisation for less than 50 generations in more than 90% of cases. Therefore, the stopping criteria for GA optimisations used henceforth is 50 generations.

100 150 200 300

100% 80% 60% 40% Solutions 20% Percent of Optimal 0% 0 102030405060708090100 Generation Number

Figure 5.17 GA Parameter Testing Results – Number of Generations

5.3.3 Objective Function Weights in Single-objective Optimisation

The sensitivity of weights used for different objectives included in the fitness function used in single-objective optimisation was examined on the Kyjov test case. The sensitivity analyses entailed performing 30 optimisations runs for different combinations of weights, and identifying the impact that different weights had on the results. The objective in the fitness function that captures the financial viability of the water reuse scheme is its NPV. This objective can take a very broad range of values, compared with the other objectives that form the fitness function. Both percent demand satisfied and treatment train qualitative criteria score can take values ranging from 0 to 1, while the penalty term heavily penalises the treatment trains that produce the effluent with quality substantially poorer than that required. The impact of the weight of each of the objectives was analysed by examining the treatment trains generated from the optimisation where the weight for the objective, alone or in combination with weights for other objectives, was set to one with others set at 0.001. The reason for setting the weights of other objectives to such a small number was to cancel their influence in the optimisation, as weights are used either as exponents or multipliers in the objective

146 Chapter 5 - DSS Testing and Sensitivity Analyses function formulation. The combination tested and the significant results obtained are summarised in Table 5.6. The results showed that two treatment trains (BScr.DAF.SPFac.WetPOL and SPAnbc.SPAer.WetPOL.ClG) were selected in nearly all optimisation runs conducted with weights for all objectives set at one. The same two treatment trains were also found to be optimal in vast majority of optimisation runs in which the NPV weight was used, with the second treatment train selected more frequently regardless of the weights used for other objectives. Optimisation runs in which the weight of the NPV objective was set to 0.001 resulted in the selection of a much larger variety of treatment trains. With cost being of no importance, the treatment trains with highest fitness values typically included some form of activated sludge process and tertiary treatment by a membrane filtration process. The overall conclusion drawn from the sensitivity analyses of objective weights in the single-objective GA optimisation is that certain treatment trains are found to be favourable regardless of the weights used. In the Kyjov test case, the optimisation produced essentially one of two types of treatment trains: one that included natural treatment processes (stabilisation ponds and wetlands) followed by chlorination, or a treatment train consisting of a more conventional bar screen and DAF in combination with a facultative pond and a wetland for polishing of effluents. The results also emphasise the need to include a multi-objective GA formulation in the DSS that can be used to more accurately capture the tradeoffs in objectives that may exist between treatment trains, and which are not captured using the fitness function formulation used in single-objective optimisation.

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Table 5.6 Summary of Objective Function Weights Sensitivity Analyses

Percent of Runs Treatment Trains Selected as Optimal**

Criteria Used* BScr.DAF.SPFac.WetPOL. SPAnbc.SPAer.WetPOL.ClG. SPAnbc.Ppre.SurF.UF. SPFac.MedF.NF. LLAs.Ppre.Floc.SurF.MF. LLAs.Ppre.Floc.SurF.UF. LLAs.SurF.SAT.O3. LLAsN.Ppre.Floc.SurF.MF. LLAsN.Ppre.Floc.SurF.UF. HLAs.Ppre.Floc.SurF.UF.

All 47 50 NPV only 37 60 NPV+Demand 33 57 NPV+Qual.Crit. 23 70 NPV+Penalty 23 77 NPV Demand+Penalty 17 70 NPV+Qual.Crit.+Penalty 37 53 Demand+Qual.Crit. 10 13 20 20 Demand+Penalty 27 13 Demand+Qual.Crit.+Penalty 13 27 10 Qual.Crit+Penalty 17 23 20 13 * Objectives for which weights were set to 1, with all others set to 0.001 ** Refer to Table 3.1 for unit process abbreviations (p. 64)

5.3.4 Effluent Quality Tolerance in Multi-objective Optimisation

As previously stated, the multi-objective optimisation of integrated water reuse schemes is conducted using the NSGA-II algorithm, where the choice of objectives to be optimised is left to the user. Although the user can choose from a number of objectives, Hughes (2005) has shown that NSGA-II looses its effectiveness as the number of objectives increase, and that algorithms such as Multiple Single Objective Pareto Sampling (MSOPS) and Repeated Single Objective (RSO) are more effective on problems involving four and more objectives. Furthermore, conducting optimisation involving more than three objectives might require that other post processing techniques be used for meaningful interpretation of results.

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Regardless of the objectives used in the optimisation, the basic requirement of treatment trains that form a design alternative is that they meet the effluent quality criteria. This constraint is handled in the NSGA-II by specifying the degree of tolerance with which it is achieved as described in Section 4.6.6, and the scale of its impact on the optimisation results is examined in this section. The sensitivity analyses were performed on the Kyjov test case by specifying the lifecycle cost and demand satisfaction as the two objectives to be optimised, and analysing how the lifecycle cost of the optimal alternative changes as a function of the tolerance used. Ten optimisation runs were then conducted with different levels of tolerance specified, ranging between values of 0.001 (i.e. effluent quality criteria essentially has to be met) and 5 (i.e. the water quality parameters exceeded by more than 500% of their limit values in total, see Equation 4.3). The wide range is used for demonstrating the sensitivity, and does not represent values that would realistically be used. Results of the optimisation runs are summarised in Figure 5.18, which displays the median optimal costs determined at different levels of demand satisfaction. As expected, lowering the effluent quality tolerance level resulted in selection of less costly treatment options overall. For smaller facilities, used in treating reclaimed water for smaller number of end-users, the only discernible impact on the cost was observed when the tolerance limit was raised from 0.001, since lifecycle costs for all other tolerance limits were the same. At higher treatment capacities, the same general trend is observed, however, with a greater degree of variance in the results attributed to the optimisation results not including certain combinations of end-users in the Pareto set. Overall, the results are understandable as many combinations of unit processes can produce effluent that does not quite meet the effluent quality requirements, and the optimisation is simply trying to identify the least costly option. The impact is more pronounced at higher treatment capacities

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9,000 0.001 ) 8,000 1 7,000 2 6,000 3 5,000 4 4,000 5 3,000 2,000

Scheme Lifecycle Cost (x10³ € 1,000 0 0% 20% 40% 60% 80% 100% Percent Demand Satisfied

Figure 5.18 Effluent Quality Tolerance Sensitivity Analyses Results

5.3.5 Pollutant Removal Efficiency of Unit Processes

The pollutant removal efficiency information on most of the unit processes contained in the knowledge base includes minimum, average and maximum values. Average values are used as default in the optimisation of design alternatives, and this section presents the results of analyses conducted to determine the effects of altering the default values to minimum and maximum efficiencies. Ten optimisation runs were carried out on the Kyjov test case using the multi-objective algorithm, where the two objectives to be optimised were set as lifecycle cost and percent demand satisfied. Results of the sensitivity analyses are shown in Figure 5.19, which displays the median optimal costs determined at different levels of demand satisfaction. It is immediately apparent that significantly higher costs resulted from setting the unit process removal efficiencies at their minimum values. The costs of satisfying different levels of demand, however, do not differ significantly when average and maximum removal efficiencies are used. The results are somewhat surprising at first, since one would expect that a smaller number of unit processes (i.e. less expensive solutions) would be needed to achieve the required effluent quality if each process operated at its maximum efficiency. One of the reasons that this is not the case is because the removal efficiency of some processes is indicated by a single value, rather than a range of values. Further

150 Chapter 5 - DSS Testing and Sensitivity Analyses examination of optimisation results was also performed in an attempt to provide further explanations, which involved analysing pollutant removals at each step of the treatment.

16,000 ) 14,000 Minimum Average Maximum 12,000

10,000 8,000 6,000

4,000

Scheme Lifecycle Cost (x10³ € (x10³ Cost Lifecycle Scheme 2,000

0 0% 20% 40% 60% 80% 100% Percent Demand Satisfied

Figure 5.19 Pollutant Removal Efficiency Sensitivity Analyses Results

When either average or maximum pollutant removal efficiencies were used, two treatment trains were most frequently selected in the optimisation process. The first of these (SPAnbc.SPAer.WetPOL.UF) was determined as optimal for satisfying up to approximately one-third of demand, and the second (BScr.DAF.SPFac.WetPOL) was typically selected for higher capacities. Pollutant concentrations were examined at each step of treatment for both pollutant removal efficiencies, with results summarised in Table 5.7. For the first treatment train, increasing pollutant removal efficiency of unit processes resulted in effluent criteria being met with smaller number of processes (e.g. TSS, COD). For pollutants such as BOD and TN, the same processes were required to meet effluent quality, but raising the removal efficiency resulted in lower effluent concentrations. However, UF was added as the final treatment step in the optimisation to remove FC to sufficiently low levels, regardless of the efficiency level used. Similar observations can be made on the second treatment train, which actually required wetlands as the final polishing step to achieve the required turbidity and TSS concentrations.

151 Chapter 5 - DSS Testing and Sensitivity Analyses

Table 5.7 Pollutant Concentrations in Treatment Steps

Pollutant Concentrations* (Average Efficiency /Maximum Efficiency) INEggs Turb TSS BOD COD TN TP FC (No/100m (NTU) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) L) Influent 225 155 133 600 19 4 1E+06 800 Required 10 10 10 70 10 0.2 1E+04 0.1 SPAnbc.SPAer.WetPOL.UF 68/ 120/ 40/ 255/ 10/ 3.7/ 2E+05/ 16.8/ SPAnbc 56 80 40 90 5.7 3.6 2E+05 16.8 20/ 120/ 5/ 89/ 5.2/ 2.0/ 6E+04/ 0.01 SPAer 14 10 2 9 1.7 1.8 6E+04 /0 5/ 10/ 3.2/ 2.6/ 0.1/ 0.2/ 6E+04/ 0.01/ WetPOL 5 3 1.3 1.1 0.1 0.1 6E+04 0 UF 0.25/ 0.2/ 0.62/ 1.1/ 0.1/ 0.15/ 2E+03/ 0/ 0.05 0.01 0.25 0.3 0.1 0.08 0.0 0 BScr.DAF.SPFac.WetPOL 225/ 155/ 130/ 591/ 4/ 1E+06/ 800/ 19/19 BScr 225 155 126 582 4 1E+06 800 68/ 47/ 65/ 296/ 16.2/ 0.8/ 316/ 0.08/ DAF 45 31 51 233 13.3 0 100 0.01 20.3/ 30/ 13/ 37 / 11.1/ 0.4/ 77/ 0.01/ SPFac 11.3 15 2.5 12 9.2 0 24 0 5/ 10/ 8.4/ 6.8/ 5.8/ 0.04/ 77/ 0.01/ WetPOL 5 3 1.6 1.3 0.1 0 24 0 * Unit process effluent values shown in bold indicate when the required pollutant concentration is reached.

The overall conclusion reached from analyses presented above is that optimisation results conducted using average removal efficiencies can be quite robust, since different processes are used as primary removal mechanisms for certain types of pollutants. Therefore, although they are capable of removing a range of pollutants to different levels which would lead to perhaps cleaner effluents, their inclusion in the treatment train might be required due to a single pollutant. On the other hand, conservative estimates of unit process pollutant removal efficiencies lead to significantly higher costs of treatment, as more processes are required to meet the water quality criteria.

5.4 Summary and Conclusions

Testing of the DSS for integrated water reuse systems and sensitivity analyses with respect to a number of options used in evaluation and optimisation methodologies are presented in this Chapter. Both testing and sensitivity analyses were conducted on two test cases, developed specifically to examine various DSS features, and verify the working of the WTRNet tool developed to apply the methodologies efficiently. The London test case is a hypothetical water reuse scheme involving a large wastewater

152 Chapter 5 - DSS Testing and Sensitivity Analyses treatment facility, several irrigation end-users, and the potential need for seasonal storage. It was used to test the sequential approach to distribution system sizing and evaluate the sensitivity of optimisation results to changes in the NLP model parameters. The Kyjov test case, developed with resources made available through the AQUAREC project, is of smaller scale. It was used to determine the most appropriate values for optimisation using GAs, and for determining the sensitivity of optimisation results with respect to changes in single-objective function weights, tolerance levels used in multi- objective optimisation and unit process pollutant removal efficiencies. Testing of the sequential approach for sizing of reclaimed water distribution systems was conducted by performing optimisation runs for a wide range of conditions that would investigate the entire approach. Both the LP and NLP models, incorporated in WTRNet and used sequentially in the optimisation process, proved to function correctly when applied to the test case for allocation of reclaimed water and sizing of distribution system facilities. Moreover, the London test case optimisation results indicated almost linear increase in lifecycle cost of distribution to the point where all demands are satisfied, indicating that economies of scale typical of wastewater treatment systems may result in favouring treatment over seasonal storage. The second part of DSS testing focused on GA operators used to generate the initial populations of design alternatives, and to perform the crossover and mutation. The initial population procedure was modified slightly based on results of testing, which indicated that excluding the “zero alternative” in the generation of treatment trains resulted in treatment trains lengths that closer resembled the practical treatment trains. In addition, the modification is thought to improve the search process. A comparison of computational efficiency in generating initial populations with the approach used in MOSTWATER shows that the present approach was more efficient by several orders of magnitude. Testing of the crossover operator showed that the number of potential crossover categories ranged from one to five. Distribution of mating categories selected or crossover was determined using test populations, which showed that the exchange of complete treatment trains between parents occurs in up to one quarter of crossovers performed using raw sewage as influent. This condition results from either the selection of the preliminary processes as the mating category, presence of “complicating” unit processes or lack of adequate mating categories. Although this does not ensure that

153 Chapter 5 - DSS Testing and Sensitivity Analyses potentially good genetic material is created, the performance of the operator was considered adequate. The testing of the mutation operator focused on identifying the number of possible mutation alternatives and the occurrences of treatment trains for which the treatment portion of the chromosome could no be mutated. Results of tests indicated that sufficient numbers of mutation options are available for treatment trains of different lengths. In a small number of cases, the mutation could not be performed on the treatment part of the chromosome, resulting in the end-user part of the chromosome being used. Therefore, the effective rate of treatment train mutation is slightly lower from that specified. Sensitivity analysis of the NLP model parameters was performed by varying the unit costs of alternate water supply by ±50% of original values, one at the time, and analysing the changes in the distribution of available reclaimed water. The results indicated that the sensitivity of this parameter is moderate if the cost of alternate supply to a single user is lowered below a level set for all other users, and high if the parameter for a single user is raised. Also, the re-distribution of available water between end-users quickly cancelled out the favouring of particular locations if the unit cost of alternate supply was raised elsewhere. The selection of appropriate population size and rates for application of GA operators was determined concurrently through sensitivity analyses, which consisted of determining the success rate of finding the global optima for a wide range of parameter combinations. Using the Kyjov test case, the results indicated that the crossover probability ranging of 0.80 to 0.99, in combination with probabilities for mutation of an individual in a population ranging between 0.30 and 0.70 and the population size of 300 resulted in the highest success rate of finding the optimal solution. The relatively high population size required for good success rates might be indicative of a lack of the mutation operator effectiveness in promoting the diversity of solutions. The sensitivity of single-objective optimisation results to different weights used in the fitness function was evaluated next. The optimisation results indicated that certain types of treatment trains were favoured regardless of the weights used, and also if any weight was assigned to project NPV. Therefore, it is concluded that the current fitness function formulation weight heavily on the financial performance of a water reuse scheme, and that the need for multi-objective optimisation is thus emphasised.

154 Chapter 5 - DSS Testing and Sensitivity Analyses

The final sensitivity analysis performed assessed the impacts of using different unit process removal levels on the treatment options and costs produced by the optimisation. Using the multi-objective algorithm with lifecycle costs and percent demand satisfied as the two objectives to be optimised, repeated runs were performed with all unit process removal efficiencies set at low, average and high levels. With respect to the selection of unit processes that formed optimal treatment trains, the results showed no significant difference when average or high removal efficiencies were used. The conclusion reached from a closer examination of intermediate water quality results is that the presence of some processes is necessary in optimal treatment trains since they are primary removal mechanisms for certain types of pollutants, for both the average and high pollutant removal levels. Results of the optimisation runs where low pollutant removal levels were assumed, however, indicated that vastly different (and more expensive) treatment trains containing larger number of processes were needed.

155

Chapter 6 Case Study

Equation Chapter (Next) Section 1

6.1 Introduction

With the knowledge gained from testing and sensitivity analyses of methods encapsulated in the developed DSS tool, the WTRNet software was used to investigate water reuse options for the City of Waterloo, Ontario, Canada. Most of the data used in the case study was reported in (Zhang 2004), who also conducted an analysis of water reuse options. That study, however, differed from this research in several ways, since the treatment options were considered in a very simplified manner and water exchange between potential users was included in optimisation of alternatives. Therefore, additional data required for implementing the DSS was collected and further assumptions were made, as stated further in this Chapter. Even though the case study is hypothetical and of relatively small size, it is considered appropriate for demonstrating the methodology developed in this research. In addition, it is hoped that results of this study could potentially be useful, should the municipality wish to pursue water reuse more aggressively in the future.

6.2 Study Area

The Region of Waterloo (RoW) is located in south-western part of the province of Ontario in Canada, as shown in Figure 6.1. The overall population of RoW is approximately 450,000, and it is one of the fastest growing regions in Ontario. The City of Waterloo is the smallest of three cites located in the RoW, with current population of 113,000 that has been projected to experience tremendous growth over the next three decades. The integrated urban water supply system provides an average of 171,500 m³/day (MLD), using groundwater as the main source (80%) as well as surface water for the remaining 20% (RoW 2006). The main source of groundwater is the Waterloo Moraine, and it provides over 300,000 people in the region with drinking water, making this the largest region dependant on ground source water in North America. The largest source of surface water is Grand River, whose quality has been affected by loadings 156 Chapter 6 - Case Study from wastewater treatment plants and agricultural practices (Zhang 2004). The region is thus faced with a scenario not uncommon with many other municipalities around the world, of large future growth in water demand, limited sources of water and increasing pollution of water bodies.

Figure 6.1 Study Area Location (NRC 2006)

To address the future growth in demand for water, the RoW completed a decade-long Long Term Water Strategy (LTWS) in 2000, which examined opportunities to satisfy the water needs of the Region for a 40 year time period and identified the following three projects of increasing complexity and cost (RoW 2006):

• Aquifer storage and recovery (ASR), that will be used to inject the water treated and the Mannheim Water Treatment Plant in periods of low demand (autumn, winter, early spring) for use during periods of high demand (summer),

• Additional ground water, where a number of potential locations to supply additional water have been identified, and

• Supply of lake water via new pipelines, in which Lake Huron, located approximately 100km away, has been identified as the preferred source.

157 Chapter 6 - Case Study

The first stage of the ASR project (19,000 m³) was completed in early 2005 at an estimated cost of $7 million, and 700,000 m³ of treated water were stored in the period of low demand of the first year (RoW 2005). Due to perceived success of the project, the RoW plans to double the ASR capacity by the end of the decade. To expand groundwater a water supply by 91,000 m³/day, a study is currently under way that will identify preferred well locations, determine possible improvements in current supplies and confirm the implementation dates. The last project, involving conveying water from Lake Huron, has an estimated capital cost of $500 million, and its implementation is expected by year 2035. In reviewing the projects identified by the LTWS, it is interesting to note that the ASR essentially balances seasonal water demands and the second project only increases the capacity at which the existing (limited) groundwater supplies can be exploited. Only the last project will provide additional water resources to the area, however, at an enormous cost. In addition, none of the projects address the issue of degrading water quality in Grand River, the single largest water course in the region. At the same time, the RoW operates eleven wastewater treatment plants that produce 164,000 m³/day of secondary treated wastewater. Therefore, wastewater reclamation could potentially be used as a major new water resource capable of addressing multiple issues in RoW: balancing of water supply and demand, additional source and way of improving the quality of Grand River. The potential source considered here is the City of Waterloo WWTP, which has a design capacity of approximately 73,000 m³/day. It is a conventional activated sludge plant, with the following treatment train: bar screen, vortex grit remover (future), primary clarifier, aeration tank, secondary clarifier and chlorination (Liu 2005).

6.3 WTRNet Model of the Study Area

In determining optimal water reuse options in the City of Waterloo, Zhang (2004) identified 16 potential end-users shown in Figure 6.2. The demand of potential industrial (ICI) end-users is considered constant throughout the year, with estimated flows shown in Table 6.1. Demand estimates for residential (RE) and golf course irrigation (GF) uses were made based on average values reported by Zhang (2004), and assumptions made with respect to time of the year that no water required for golf course irrigation or toilet flushing. The seasonal variations in demand were adjusted according to monthly distribution of average daily water consumption in the City of Waterloo. Two options were modelled using WTRNet: constructing a new water reclamation 158 Chapter 6 - Case Study facility for treatment of raw sewage specifically for reuse, and upgrading the existing treatment to provide upgraded effluent suitable for reuse.

Figure 6.2 Potential Reclaimed Water End-users in the Waterloo Study Area (Zhang 2004)

Table 6.1 Estimated Constant Demands of Industrial End-users

End-user Estimated Demand (m³/day) ICI-1 1,605 ICI-2 481 ICI-3 462 ICI-4 382 ICI-5 299 ICI-6 606 ICI-7 602 ICI-8 950 ICI-9 519 ICI-10 507 ICI-11 507

159 Chapter 6 - Case Study

Estimated monthly demands for residential and irrigation end-users are shown in Table 6.2, and Table 6.3 shows assumed water quality requirements for each potential end- user considered in the study. Characteristics of nodes used in WTRNet to represent end-users, WWTP and intermediate distribution system connections are provided in Appendix D , which also includes node elevations estimated using topographical maps (NRC 2006). A distribution system connecting the potential end-users was represented in WTRNet, consisting of two main branches, with the first servicing users located approximately southwest west of the WWTP (ICI-1,2,3,4,5,11; RE-3; GF-2) and the second for the remaining end-users (ICI-6,7,8,9,10; RE-1,2; GF-1). Map of the distribution system and information on links used to connect the potential end-users is included in Appendix D. Ten pipe sizes were considered for all links initially, ranging from 100mm to 1,000mm in diameter, and these were later adjusted to accelerate the sizing of the pipes using the LP component of the sequential approach.

Table 6.2 Estimated Variable Demands of Residential and Irrigation End-users

End-user Monthly Demand (m³/day) Total Month Demand RE-1 RE-2 RE-3 GF-1 GF-2 (m³/day)

Jan 1,292 1,780 2,492 0 0 5,565 Feb 1,292 1,780 2,492 0 0 5,565 Mar 1,292 1,780 2,492 0 0 5,565 Apr 2,345 3,229 4,521 1,062 531 11,689 May 2,330 3,208 4,492 1,047 523 11,600 Jun 2,575 3,547 4,966 1,295 647 13,030 Jul 2,684 3,697 5,175 1,405 702 13,662 Aug 2,608 3,592 5,028 1,328 664 13,220 Sep 2,597 3,577 5,007 1,317 658 13,156 Oct 1,292 1,780 2,492 0 0 5,565 Nov 1,292 1,780 2,492 0 0 5,565 Dec 1,292 1,780 2,492 0 0 5,565

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Table 6.3 Assumed Water Quality Requirements of Potential End-users

Maximum Allowed Pollutant Concentrations

End-user COD (mg/L) INEggs INEggs TP (mg/L) FC (mg/L) TN (mg/L) TSS (mg/L) TSS (mg/L) (No/100mL) Turb (NTU) BOD (mg/L) BOD (mg/L)

ICI-1 10 100 20 20 10 0.2 1000 0.1 ICI-2 10 100 20 20 10 0.2 1000 0.1 ICI-3 10 100 30 30 10 0.2 1000 0.1 ICI-4 10 50 20 20 10 0.2 1000 0.1 ICI-5 10 10 10 20 10 0.2 1000 0.1 ICI-6 10 100 30 50 10 0.2 1000 0.1 ICI-7 10 100 80 75 10 0.2 1000 0.1 ICI-8 10 100 30 100 10 0.2 1000 0.1 ICI-9 10 180 60 75 10 0.2 1000 0.1 ICI-10 10 180 60 75 10 0.2 1000 0.1 ICI-11 10 180 60 75 10 0.2 1000 0.1 RE-1, RE-2, RE-3 2 10 10 70 15 2 0 0 GF-1, GF-2 10 10 20 70 10 0.2 0 0.1

6.4 Analyses of Reuse Options using WTRNet

The analyses of reuse options for the City of Waterloo were conducted using the WTRNet model developed for the study. Multi-objective optimisation was carried out for a number of different optimisation objectives. The optimisations focused on financial performance initially, and considered different levels of demand satisfied (first objective) in conjunction with project lifecycle cost as the second objective. Two options for provision of reclaimed water (new water reclamation facility and upgrade to the existing treatment) were analysed using the demand satisfaction and financial indicator as objectives. The second part of analyses considered each of the remaining objectives (see Table 4.3) in the optimisation, in combination with demand satisfaction and lifecycle objectives (i.e. a three-objective optimisation problem was solved in each instance). Results of all these analyses are presented in this section, which represent the median values achieved from repeating each optimisation ten times. The optimisation runs were performed using a fixed population size of 300 and the stopping criteria of maximum number of generations fixed at 50 (see Figure 5.18). 161 Chapter 6 - Case Study

6.4.1 Least-cost Optimisation

The least-cost optimisation was carried out using WTRNet by specifying the percent demand satisfied and lifecycle cost as the two objectives to be maximised and minimised, respectively. An assumption used in evaluating the first option, the construction of a new water reclamation facility, is that its maximum capacity would be such that peak monthly demand could be satisfied regardless of the combination of end- users selected. Therefore, the optimisation runs did not entail utilisation of the sequential approach and the distribution system sizing was simply carried out using the LP method for sizing of pipes and pumps. The upgrade option was modelled by artificially neutralising the implications of selecting processes already used at the City of Waterloo WWTP. This was accomplished by eliminating all resources (i.e. cost, land, labour, etc.) required for using these processes in the knowledge base. The optimisation was then carried out in the hope that the comprehensive GA optimisation algorithm would include these processes in optimal design alternatives. One additional modification that was performed on the knowledge base was to exclude SAT as a treatment option from all analyses, which was found to be favoured in test optimisation runs. This was done to reflect the fact that the study area depends on groundwater as the key source of water supply, and SAT is not seen as a prospective option due to potential for contamination of this source and lack of public acceptance. The results of optimisation carried out using the demand satisfied and lifecycle cost are shown in Figure 6.3. The lifecycle cost for upgrading the existing WWTP ranges from approximately 4.5 million € for satisfying the demand of a single end-user to 80 million €, required to satisfy 100% of demand for reclaimed water. If a new facility is to be provided, dedicated for reclamation of raw sewage, the lifecycle costs are much higher, ranging from 7.6 million € for satisfying a single end-user to 98 million € to completely satisfy the demand. Although there is a fair amount in variability in optimal lifecycle costs (intrinsic in using random search optimisation), the results indicate that upgrading the existing WWTP becomes more attractive economically at higher levels of demand satisfaction. The variability in results in more pronounced for the upgrade option, indicating that the existing treatment processes, which were assumed not to require any resources, were not always included in optimal design alternatives.

162 Chapter 6 - Case Study

100,000 ) 90,000 New Existing 80,000 70,000 60,000 50,000 40,000 30,000 20,000

Scheme Lifecycle Cost (x10³ € 10,000 - 0% 20% 40% 60% 80% 100% Percent Demand Satisfied

Figure 6.3 Demand Satisfied – Lifecycle Cost Optimisation Results

6.4.2 Optimisation Using Three Objectives

In addition to optimising the financial objective (minimal lifecycle cost) at various levels of demand satisfaction, each of the other objectives available for multi-objective optimisation were added. The purpose of adding the third objective is threefold:

• to determine design alternatives for different levels of demand satisfaction that are both financially optimal and desirable from the perspective of other criteria that may be specified by the decision maker,

• to explore potential tradeoffs that may exist between the objectives, and

• to derive design principles through examination of patterns in optimisation results. The first two are addressed in the remainder of this section, while the design principles are addressed in the two sections that follow, which deal with the selection of treatment processes and end-users respectively.

6.4.2.1 Qualitative Criteria Score

The results of the optimisation carried with the qualitative criteria score as the third objective, which was maximised, are shown in Figure 6.4. The value of the third objective is indicated using colour coding, while the optimisation results for the first two objectives are shown on the axis. 163 Chapter 6 - Case Study

Figure 6.4 Demand Satisfied – Lifecycle Cost – Qualitative Criteria Score Optimisation Results

Overall, the cost of optimal water reuse schemes does not differ significantly from the schemes determined by optimising with respect to lifecycle cost and demand satisfied where a new treatment facility was considered. However, most of the schemes identified as having the least lifecycle cost do not score well on the qualitative criteria. In fact, most of the optimal schemes that have the highest qualitative scores are not the least-cost solutions for various levels of demand satisfied. Although it cannot be generalised that the two objectives (lifecycle cost and qualitative criteria score) are conflicting, it is evident that if a scheme is selected purely on its least-cost it will most likely not be the most desirable when qualitative criteria are considered.

6.4.2.2 Land Required

The results of optimisation runs conducted with the land required used as the third objective are shown in Figure 6.5, again with symbols of different colour indicating the value of the third objective in hectares. Immediately it is apparent in the results that including the land required as the third objective to be minimised resulted in generally higher scheme costs, compared to two-objective optimisation. Furthermore, the optimal solutions identified as belonging to the Pareto set are quite spread, with most schemes 164 Chapter 6 - Case Study

(87% of all solutions) requiring less than 1 ha for the treatment facility. A much smaller number of optimal schemes ranged widely in terms of land required for the treatment facility. In most of these cases, the savings achieved in lifecycle cost are accompanied by a drastically larger footprint of the treatment facility.

Figure 6.5 Demand Satisfied – Lifecycle Cost – Land Optimisation Results

Overall, the optimisation results show that limited land availability can have significant impact on the lifecycle cost of the water reuse scheme considered in this case study. However, the land required to construct a facility that would generate any level of end- user demand is generally less than 1 ha. Having larger area available could result in significant savings in lifecycle cost of providing reclaimed water, particularly for high levels of demand satisfaction.

6.4.2.3 Sludge Production

Minimising the sludge production was also considered as the third objective, with the optimisation results summarised in Figure 6.6 indicating the annual sludge production (tonnes) with symbols of different colour. It should be pointed out that the production of sludge is indirectly included even if only lifecycle cost and demand satisfaction are used as objectives, since the costs of treatment include the costs associated with sludge

165 Chapter 6 - Case Study treatment and disposal. The sludge production can be a separate objective in cases where there is a specific desire on the part of the decision maker to minimise it due to environmental or other constraints or requirements.

Figure 6.6 Demand Satisfied – Lifecycle Cost – Sludge Optimisation Results

The sludge production is generally proportional to the volume of wastewater treated, regardless of the treatment process used, and this is also evident in the results. However, there is also a fair degree of variability in sludge produced by schemes identified as optimal due to inclusion of different treatment processes. Limiting the sludge production is again seen to have potentially significant effect on the lifecycle cost of a scheme. This is particularly true for higher levels of demand satisfaction, where lower sludge production inevitably leads to higher lifecycle cost.

6.4.2.4 Energy Consumption

Energy consumption was next included as the third objective to be minimised in the optimisation, with results shown in Figure 6.7 in which the energy required for treatment (kWh/year) is indicated by symbols of different colour. Similarly to sludge production, the energy consumption is already accounted for when optimising with respect to lifecycle cost. However, since the energy costs represent a large portion of

166 Chapter 6 - Case Study any water reuse scheme, this objective is addressed much more effectively when optimisation is carried out to minimise the lifecycle cost of reuse schemes of different sizes.

Figure 6.7 Demand Satisfied – Lifecycle Cost - Energy Optimisation Results

The optimisation results reflect the significance of energy costs in water reuse, as they are not drastically different from the optimisation results that considered only lifecycle cost and demand satisfaction.

6.4.2.5 Labour Required

The labour required to operate a water reclamation facility is another factor included in the lifecycle cost of treatment. It was the last quantitative criteria considered as the third objective in optimisation of water reuse options for the case study, with results of optimisation provided in Figure 6.8. The colour coding in this instance is used to indicate the number of person-hours per month required for the treatment component of the scheme.

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Figure 6.8 Demand Satisfied – Lifecycle Cost - Labour Optimisation Results

The optimisation results shown are again not significantly different form those determined through optimisation with respect to cost and level of demand satisfaction. Labour required to operate a treatment facility is generally proportional to the size of the facility, and the overall optimisation results reflect this fact. The tradeoffs between the cost and labour apparent in the upper range of demand satisfaction are attributed to the random nature of the optimisation algorithm used.

6.4.3 Selection of Treatment Processes

As stated earlier, one of the purposes for conducting the optimisation using three objectives was to examine any patterns in the selection of treatment processes that may be evident in the optimisation results, and how they may differ from the ones where only the lifecycle cost and demand satisfaction are used as objectives. This was performed by first looking at the representation of each unit process in treatment schemes selected by the optimisation, as well as examining the treatment trains that were most frequently included. All optimisation results were compiled, and processed to identify the number of times each unit process included in the knowledge base formed part of optimal treatment

168 Chapter 6 - Case Study scheme. These figures were then used to calculate the percent of optimal treatment trains that included each unit process, when different objectives were specified for the optimisation. These results are summarised in Figure 6.9, in which unit processes that did not appear in any of the optimal treatment trains are omitted (Acti, SPAnbc, SPFac, WetFWS, WetSUB, AOO3, AOH2O2, SAT, O3 and UV). Notable differences in the selection of the remaining unit processes using different third objectives in the optimisation are discussed below, by comparing the results with those determined using lifecycle cost and demand satisfaction only. Including the qualitative criteria score as the third objective subject to maximisation resulted in certain processes being included in the optimal treatment trains more frequently. In particular, treatment trains that included the LLAS (with and without de- Nitrification) for secondary treatment appear to have been selected much more frequently, along with the selection of PPre, MF and Floc. Unit processes that were selected in smaller proportion of optimal treatment schemes include DAF, WetPOL and IE, and also disinfection processes (PA and ClG). In cases where land required was considered as the third objective to be minimised, tertiary treatment processes such as DAF, MedF and MF appear to have been selected less frequently. Instead, processes with potentially smaller footprint (UF and particularly NF) were included more often in optimal treatment trains. Natural treatment processes (MP and WetPOL) were also included less frequently but IE was used in more optimal treatment trains. Similarly, disinfection using PA was included much more frequently while the presence of ClG in optimal treatment trains declined. Furthermore, MBR was included in nearly 20% of treatment trains, and missing entirely in all treatment schemes identified without using land required explicitly in the optimisation.

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Percentage of Optimal Treatment Trains 0% 20% 40% 60% 80% 100%

BScr GrCh CScr FScr Sed SedC DAF DMF HLAs LLAs LLAsN TF RBC SAF SPAbc SPAer MBR EBPR Ppre MedF SurF MF UF NF RO GAC PAC IE MP WetPOL Floc PA ClO2 ClG

Cost Qualitative Criteria Land Sludge Energy Labour

Figure 6.9 Summary of Unit Processes Included in Optimal Schemes

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In optimisation results that included the minimisation of sludge as the third objective, CScr appears to have been included more frequently as the preliminary treatment step in optimal treatment trains. For primary treatment, the frequency in which Sed was included is more than doubled, while the SedC is used in just half of optimal treatment trains. For secondary treatment, LLAs and LLAsN are included much less frequently in optimal treatment trains, while the percent of treatment trains that included stabilisation ponds (SPAbc and SPAer) is much higher. The percent of optimal treatment trains that used SurF for tertiary treatment declined, while UF and NF were used much more frequently. Although the latter processes do produce concentrates, these are treated separately from sludge treatment and disposal, so their inclusion in favour of more sludge-producing process in reasonable. Consequently, WelPOL was used in a smaller portion of optimal treatment trains as the polishing step. Disinfection using PA was included more frequently, while the inclusion of ClG in optimal treatment trains dropped significantly. When energy minimisation was considered as the third objective to be minimised, GrCh was used less frequently as preliminary treatment step, in favour of CSrc which was included much more frequently in optimal treatment trains. This is understandable, since the latter does not use any energy while the former does. For primary treatment, Sed and SedC were used much more frequently, while DAF inclusion in optimal treatment trains dropped significantly. Activated sludge processes were also used much less frequently in secondary treatment, in favour of less energy-intensive processes (TF and RBC). For tertiary treatment, SurF and WetPOL were included in a vast majority of optimal treatment trains, while PA was not used for disinfection at all in favour of ClG. Using the labour required as the third objective in the optimisation did not result in any significant changes to the selection of preliminary treatment processes. However, primary treatment processes were employed more frequently (Sed and SedC), whereas the inclusion of activated sludge processes (LLAs and LLAsN) for secondary treatment was reduced. Some secondary processes, such as RBC, SAD and SPAbc, however, were include in optimal treatment trains more frequently. For tertiary treatment, a notable change was an increase in optimal treatment trains that included NF, while the use of WetPOL was somewhat declined even though this process does not have particularly high labour requirements. The results presented above are encouraging, as they confirm that the WTRNet model, and particularly optimisation methodology employed to analyse treatment options for

171 Chapter 6 - Case Study the City of Waterloo, correctly include unit processes in optimal design alternatives that could intuitively be favoured by a planner. Depending on the objectives selected for optimisation, different unit processes are included more or less frequently in design alternatives identified as optimal. The results indicate that considering only the lifecycle cost of a water reuse scheme in the optimisation, in conjunction with demand satisfaction, results in the selection of processes that are far from optimal if other criteria are used. In order to better appreciate the optimisation results in the sense of the overall treatment train selection, a list was compiled of treatment trains most often identified as optimal. Indicated in Table 6.4 are the five treatment trains present most frequently in the repeated optimisation runs for each combination of objectives used, as well as the percentage of the overall optimal treatment trains they represent. With lifecycle cost used as the primary optimisation criteria (in addition to percent demand satisfied), three actual treatment trains can be identified of the top five:

• DAF.MedF.SurF.MF.IE, preceded either by BScr or GrCh,

• DAF.TF.WetPOL.SurF.ClG, preceded either by BScr or GrCh, and

• BScr.SedC.DAF.EBPR.MP.SurF.MF. In all of these treatment trains DAF is used as the primary treatment component and they all include SurF and MF as the tertiary treatment component, while the other unit processes needed to upgrade the effluent to required quality are different between the three. Together, they were included in more than one third of optimal design alternatives. When qualitative criteria score was used as the third alternative, LLAsN.Ppre.SurF.MF was present in almost one quarter of optimal design alternatives. The same treatment train with Floc or BScr.DAF added was present in further 18% of optimal treatment trains, although these processes appear to be redundant and should logically be excluded. The top five treatment trains identified with land requirement as the third alternative are quite different, however, none include any of the natural treatment processes and all include some form of membrane filtration. The MBR was included in only one of the treatment trains, even though it offers significantly smaller footprint than activated sludge processes combined with membrane filtration, possibly due to cost effects.

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Table 6.4 Summary of Treatment Trains Included Most Frequently in Optimal Schemes

Optimisation Percent of All Optimal Treatment Train Criteria Treatment Trains BScr.DAF.MedF.SurF.MF.IE. 9% BScr.DAF.TF.WetPOL.SurF.ClG. 9% Lifecycle Cost GrCh.DAF.MedF.SurF.MF.IE. 8% GrCh.DAF.TF.WetPOL.SurF.ClG. 5% BScr.SedC.DAF.EBPR.MP.SurF.MF. 4% LLAsN.Ppre.SurF.MF. 24% LLAsN.Ppre.Floc.SurF.MF. 14% Qualitative Criteria LLAsN.Ppre.MedF.SurF.ClG. 8% Score BScr.DAF.TF.WetPOL.SurF.ClG. 6% BScr.DAF.LLAsN.Ppre.SurF.MF. 4% LLAsN.Ppre.SurF.MF. 8% LLAsN.PAC.NF. 6% Land Required GrCh.SedC.DAF.MBR.PAC.NF. 6% GrCh.DAF.UF.NF.IE. 5% BScr.SedC.UF.NF.IE. 5% BScr.Sed.DAF.SPAer.UF.NF. 9% BScr.DAF.MF.NF.IE. 6% Sludge Production BScr.Sed.DAF.SPAbc.UF.NF. 6% GrCh.Sed.DAF.SPAer.UF.NF. 5% CScr.Sed.DAF.SPAer.UF.NF. 4% BScr.DAF.TF.WetPOL.SurF.ClG. 15% BScr.SedC.TF.WetPOL.SurF.ClG. 15% Energy CScr.SedC.TF.WetPOL.SurF.ClG. 7% Consumption BScr.DAF.RBC.WetPOL.SurF.ClG. 7% CScr.DAF.TF. WetPOL.SurF.ClG. 7% BScr.SedC.DAF.MedF.SurF.NF. 11% BScr.SedC.DAF.EBPR.MP.SurF.MF. 9% Labour Required BScr.SedC.DAF.SurF.MF.IE. 7% BScr.Sed.DAF.SurF.MF.IE. 6% BScr.Sed.RBC.WetPOL.SurF.ClG. 5%

Inclusion of sludge production as the third objective in the optimisation resulted in effective elimination of activated sludge processes for secondary treatment in most frequently identified optimal treatment trains. DAF and membrane filtration processes are included in all of these treatment trains, as well as the natural treatment in the form of stabilisation ponds. The inclusion of these processes led to the lack of need for disinfection, and none of the unit processes from this category are present in the top five optimal treatment trains. 173 Chapter 6 - Case Study

With energy consumption was used as the third objectives, DAF and SedC combined with either TF or RBC for primary and secondary treatment are included in the five treatment trains most frequently identified as being optimal. Notably, no activated sludge processes appear in any of the most frequently selected treatment trains. The effluent polishing steps included in all cases were the same, consisting of WetPOL, SurF and ClG. When minimisation of labour required for treatment operations was included as the third objective, BScr and Sed (SedC) were used for preliminary and primary treatment in most frequently selected treatment trains. These were followed by DAF in most cases, several different unit processes that preceded SurF, which was included in all cases. The membrane filtration was included in four of the five treatment trains, supplemented by IE in two of them, while the fifth most frequently selected treatment train used ClG for disinfection. As the results of the analyses of inclusion of individual unit processes suggested, the treatment trains most frequently selected through optimisation varied depending on the objectives used. Although this variation can be quite marked with different objectives used in the optimisation, the results do point that the variation in optimal treatment trains is not as drastic for each individual case of the third objective, since some unit processes tend to be included in most of the top five treatment trains. These patterns, identified here on a specific case study, form the design principles that can be used for further and more detailed investigations of the most promising treatment alternatives. A final comment is made on the selection of unit processes by the optimisation of the WTRNet model that considered upgrades to the existing WWTP. As mentioned previously, this model was created by modifying the knowledge base to eliminate all resources required for implementation of processes that are already present (BScr, GrCh, Sed, LLAs and ClO2). In this way, it was anticipated that these processes would be included in most (if not all) optimal treatment trains, and that the figures obtained from the model would reflect only the resources needed for upgrading of the portion of the existing WWTP effluent. Figure 6.10 shows the percent of optimal schemes that included each of the unit processes, with those that were never included omitted for clarity. The results, based on optimisation using lifecycle cost and demand satisfied as objectives, indicate that LLAs was indeed included in all treatment trains identified as optimal. The other existing processes, however, were substituted by others (BScr, GrCh, Sed with DAF; ClO2 with WetPOL and ClG) even though this resulted in

174 Chapter 6 - Case Study additional cost. Therefore, it is concluded that optimising upgrades to existing facilities simply by reducing the resources required for existing processes is not appropriate using the current methodology.

100%

s 80%

60%

40% Percentage of Optimal Treatment Train 20%

0% IE UF NF Sed MP UV MF ClG Ppre BScr CScr DAF SurF SedC GrCh LLAs ClO2 MedF LLAsN WetPOL

Figure 6.10 Unit Processes Included in Optimal Upgrade Schemes

6.4.4 Selection of End-users

In addition to examining patterns in the selection of treatment processes, the optimisation results were analysed to try and identify end-users that might appear to be more favoured. The analyses involved determining the number of times each end-user was included in the optimal scheme, for different ranges of overall demand satisfaction, and expressing it as a percentage of the total number of optimal schemes in that range. The same analyses were performed on the optimisation results involving different objectives. This was performed since the end-users have different quality requirements, and their inclusion in the scheme has implications on the selection of optimal treatment trains. A preliminary examination of this hypothesis was conducted by plotting the percent of optimal design alternatives that included different end-users, using different objectives in the optimisation as shown in Figure 6.11.

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Cost Qualitative Criteria Land Sludge Energy Labour

ICI-1 100% GF-2 ICI-2 80% GF-1 ICI-3 60%

RE-3 40% ICI-4

20%

RE-2 0% ICI-5

RE-1 ICI-6

ICI-11 ICI-7

ICI-10 ICI-8 ICI-9

Figure 6.11 Overall Inclusion of End-users in Optimal Treatment Schemes

The results shown above suggest that some end-users are clearly represented more often than others in optimal water reuse schemes, regardless of the objectives used. The industrial end-users (ICI-1 to ICI-11) are included in at least 40% of all optimal schemes with ICI-1 being included in excess of 80% of the optimal schemes that considered lifecycle cost and demand satisfaction as objectives. The inclusion rate of residential end-users (RE-1 to RE-3) in optimal schemes varied, with RE-2 being included in excess of 65% of schemes, RE-1 in 20% to 35% of schemes and RE-3 in 25% to 40% of schemes. It is worth noting that out of the three, RE-2 is located nearest to the WWTP which could have been a major influencing factor. Golf course end-users appeared to be less represented in optimal water reuse schemes, with GF-1 present in approximately 15% to 35% of schemes and GF-2 included in 10% to 40% of schemes. This seems even more prominent if the lifecycle cost is considered alone with maximising end-user demand, where golf courses appeared in less than 15% of optimal schemes. Two factors most probably contributed to this: both golf courses are located at far ends of the distribution network and both were assumed to have highly seasonal demands. The combination of these factors means that larger and longer pipes are required to convey the reclaimed water to these users (compared, for example to users that are nearer to the treatment facility and have constant demands). This also means

176 Chapter 6 - Case Study that the pumping (energy) costs incurred over the project lifecycle are significant. In addition, the peak to average demand flow rate is quite high for these users, which means that expensive infrastructure is required compared with lower revenues. To further explore the presence of additional patterns in the selection of end-users, two relatively simple indicators of end-user preference were compared with optimisation results. The first step in the selection of potential reclaimed water end-users considered by many researchers and planners is to identify those located within a certain distance from the water reclamation facility. The logic used is that it is more economical to provide reclaimed water to the end-users located nearest to the plant, since the pumping and piping costs would not be extensive. Following this logic, it would then be expected that the inclusion rate of potential end-users located closest to the WWTP in the City of Waterloo would be the highest, particularly for schemes that satisfy less than 100% of end-user demand. The actual inclusion rates of individual end-users in optimal schemes identified using different objectives and at different levels of demand satisfaction are plotted as a function of the length of pipe required for their supply. The results are shown in Figure 6.12, in which individual graphs are used for different ranges of end-user demand satisfaction (as percent of the total potential demand). Although the data is quite scattered, some general trends can be observed by fitting straight lines to the observed points (not shown here). For cases where the satisfaction of potential end-user demand is on the extreme (i.e. 0-10% or 90-100% of total), the end-users appear to be included in optimal schemes unrelated to their distance from the reclamation facility. For all other cases, where the potential end-user demand is satisfied to various degrees, there are some indications of higher inclusion rates of end-users closer to the WWTP. However, the results also clearly demonstrate that this cannot be used as a general rule, since some users are undoubtedly not preferred, even though their location may lead to inclination to automatically include them in water reuse scheme. Conversely, certain end-users located further away from the reclamation plant appear to be favoured, due to a combination of a number of other possible factors such as their elevation, required water quality, seasonality of demand, location in relation to other end-users, level of demand relative to other end-users, etc. An additional factor, not accounted for in this case study but which could have significant implications in the end-user selection, are the revenues that could be expected from the end-user through a combination of various charge for provision of reclaimed water.

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0-10 50-60

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Inclusion Rate 20% Inclusion Rate 20% 0% 0% 246810 246810 Distance to End-user (km) Distance to End-user (km)

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Inclusion Rate 20% Inclusion Rate 20% 0% 0% 246810 246810 Distance to End-user (km) Distance to End-user (km)

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Inclusion Rate Inclusion 20% Rate Inclusion 20% 0% 0% 246810 246810 Distance to End-user (km) Distance to End-user (km)

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Inclusion Rate 20% Inclusion Rate 20% 0% 0% 246810 246810 Distance to End-user (km) Distance to End-user (km)

40-50 90-100 100% 0% 100% 100% 23456789 80% 80% Distance to End-user (km) 60% 60%

Inclusion Rate Inclusion 40% 40%

Inclusion Rate 20% Inclusion Rate 20% 0% 0% 246810 246810 Distance to End-user (km) Distance to End-user (km)

Cost Qual. Crit. Land Sludge Energy Labour

Figure 6.12 Inclusion of End-users in Optimal Treatment Schemes as Function of Their Distance from Water Reclamation Facility and Demand Satisfaction 178 Chapter 6 - Case Study

The second criteria potentially useful in identifying optimal allocation of reclaimed water to end-users is the piping efficiency ratio (PER), introduced by Zhang (2004). The author suggested that this ratio, defined in Equation 6.1, reflects the economy of scale which is important in infrastructural planning and investment. Therefore, smaller PER values would identify the more economically suitable options for the potential reuse water supply. (Piping Length) PER =∀k , water reuse arcs k (6.1) (Flow Rate)k

Using the PER as defined above, the location of end-users relative to the supply as well as their demand are taken into account. Therefore, it is presumed that end-users with high demand and near to the water reclamation facility would be more favoured in the end-user selection. This hypothesis was tested on the results of optimisation runs carried out all combinations of objectives, by plotting the inclusion rates of individual end-users in optimal schemes at different levels of demand satisfaction against the calculated PER values for each end-user. These results are shown in Figure 6.12, in which individual graphs are used for different ranges of end-user demand satisfaction (as a percent of the total potential demand). The scatter and the abundance of data points resulting plotting PER values versus end- user inclusion rates do not allow formulation of any clear relationship between the two values. However, the results do indicate that the PER might not be very useful parameter in determining the optimal allocation of reclaimed water amongst the end- users. Some end-users that have lower PER values, theoretically making them the most attractive for provision of reclaimed water, were not included in optimal treatment schemes unless the scarcity of reclaimed water relative to overall demand was minimised. At the same time, certain end-users with high PER values tended to be included much more frequently in optimal reuse schemes. Therefore, it is concluded that the PER does not on its own represent adequately the attractiveness of potential end-users of reclaimed water in situations of scarcity, since it omits other factors that might be of higher importance outlined previously.

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0-10 50-60

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Inclusion Rate 20% Inclusion Rate 20% 0% 0% 0 5 10 15 051015 PER PER

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Inclusion Rate Inclusion 40% 40%

Inclusion Rate Inclusion 20% Rate Inclusion 20% 0% 0% 0 5 10 15 051015 PER PER

Cost Qual. Crit. Land Sludge Energy Labour

Figure 6.13 Inclusion of End-users in Optimal Treatment Schemes as Function of Their PER and Demand Satisfaction

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A final observation, made on results dealing with preference of end-users based both on their distance from the reclamation facility and the PER, is that the choice of objectives in the optimisation appears to influence the inclusion of end-users in the scheme. This observation is made on the fact that different end-users appear to be more favoured when their inclusion rates in optimal schemes are considered in various ranges shown in Figure 6.12 and Figure 6.13, as opposed to the aggregated results provided in Figure 6.11.

6.5 Summary and Conclusions

In this Chapter, the methodology developed for modelling and optimisation of integrated water reuse systems and incorporated in WTRNet software was demonstrated on a case study. The case study is concerned with analysing water reuse options for the City of Waterloo (Ontario, Canada), located in a region expecting to undergo large population increase in the coming decades. Although a major study has been completed, which analysed opportunities for long term water supply, water reuse was not considered as an opportunity to offset future expansion programs. Since the reuse option is considered to potentially offer multiple benefits, including balancing of water supply and demand, additional source and way of improving the river water quality, it was analysed to demonstrate the methodology developed in this thesis and provide some insight that could be useful should the City decide to consider water reuse in the future. The data used in the case study came primarily from another research study (Zhang 2004) that was concerned with looking at the possible water exchange scenarios. This study, however, examined the possibility of providing upgraded wastewater to potential end-users identified, which is considered both more probable and acceptable to the stakeholders. Two WTRNet models of the study area were developed: the first considered provision of a dedicated water reclamation facility using raw wastewater as a source, and the second evaluated options for upgrading of the existing WWTP to provide effluent of satisfactory quality. The optimisation using the lifecycle cost and percent demand satisfied as the two objectives to be minimised and maximised, respectively. The results showed that the lifecycle cost for upgrading the existing WWTP ranges from approximately 4.5 million € for satisfying the demand of a single end-user to 80 million €, required to satisfy 100% of demand for reclaimed water. If a new facility is to be provided, dedicated for reclamation of raw sewage, the lifecycle costs are much higher, ranging from 7.6

181 Chapter 6 - Case Study million € for satisfying a single end-user to 98 million € to completely satisfy the demand. However, both the variability of results determined using the upgrade model and analyses of treatment trains selected for upgrade showed that existing unit processes were not present in the schemes identified as belonging to the Pareto set. Other quantitative criteria computed by the treatment performance module were also used as the third objective in the optimisation. The purposes of using additional objectives were to: (i) determine financially optimal design alternatives for different levels of demand satisfaction that are also desirable from the perspective of other criteria that may be specified by the decision maker, (ii) explore the potential tradeoffs that may exist between the objectives and (iii) derive design principles through examination of patterns in optimisation results. Using the treatment train qualitative criteria score as the third criteria showed that most of the optimal schemes that have the highest qualitative scores are not the least-cost solutions for various levels of demand satisfied. Including the land required as the third objective to be minimised resulted in generally higher scheme costs, compared to two- objective optimisation. The optimisation results also showed that limited land availability can have significant impact on the lifecycle cost of the water reuse scheme considered in the case study. The optimisation runs that included minimisation of sludge production showed to have potentially significant effect on the lifecycle cost of a scheme. This is particularly true for higher levels of demand satisfaction, where lower sludge production inevitably leads to higher lifecycle cost. The optimisation results where minimisation of energy was included as the third objective reflected the significance of energy costs in water reuse, as they were not drastically different from the optimisation results that considered only lifecycle cost and demand satisfaction. Similarly, including the labour required labour required to operate a treatment facility, which is generally proportional to the size of the facility, resulted in optimisation results not significantly different form those determined through optimisation with respect to cost and level of demand satisfaction. The inclusion of individual unit processes in optimal treatment trains was examined, by analysing the percentages of optimal treatment trains that included each process. Patterns in the selection of unit processes were examined when different criteria were used as a third objective in the optimisation. Generally, the results confirmed that the WTRNet model and particularly optimisation methodology employed to analyse treatment options for the City of Waterloo, correctly included unit processes in optimal

182 Chapter 6 - Case Study design alternatives that could intuitively be favoured by a planner. In addition, considering only the lifecycle cost of a water reuse scheme in the optimisation, in conjunction with demand satisfaction, was shown to result in the selection of processes that are far from optimal if other criteria are used. Analyses of optimisation results also extended to examining the treatment trains most frequently included in optimal treatment trains. While the treatment trains selected with different objectives considered varied considerably, it was also found that the variation in optimal treatment trains is not as drastic for each individual case of the third objective, since some unit processes tend to be included in most of the top five treatment trains. These patterns, identified here on a specific case study, formed the design principles that can be used for further and more detailed investigations of the most promising treatment alternatives. Analyses of inclusion of end-users in optimal treatment schemes were also conducted, which demonstrated that the optimal selection of potential end-users is influenced by a number of factors. Two criteria that have been suggested or used in the past to select end-users of reclaimed water were analysed: distance from the reclamation facility and the PER. Although there were some indications of higher inclusion rates of end-users closer to the WWTP in the case study, the results showed that distance alone cannot be used as a general rule. In the case of the PER, no correlations with this value and the rate of inclusion of potential end-users in the case study was found, indicating some limitations in it being able to represent adequately the attractiveness of potential end- users of reclaimed water in situations of scarcity.

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Chapter 7 Summary and Conclusions

7.1 Thesis Summary

The practice of water reclamation and reuse, which has a long history in human settlements, has grown tremendously in the last century. In Chapter 1, the evidence of this growth and its acceleration in recent decades is presented, along with some contributing factors. With the growth of water reuse capacity worldwide projected to accelerate even further, resulting in schemes of larger size, and the number of technological, environmental, social and financial considerations on the rise, planning of water reuse projects is becoming ever more complex and challenging task. Therefore, decision support tools are acutely needed to assist the planners of water reuse schemes in providing this valuable resource more efficiently. Chapter 1 concludes by outlining the objectives of research presented in this thesis, which can be summarised as follows:

• develop a decision support tool for integrated water reuse systems, and

• gain knowledge by establishing design principles for integrated water reuse systems derived from applications of the developed tool. A comprehensive review of existing decision support tools designed to provide assistance in addressing issues relevant to planning of integrated water reuse projects was conducted, with the results summarised in Chapter 2. The review concentrated on both the treatment and distribution aspects of water reuse, in addition to the integrated approaches. With regards to water treatment for reuse, a number of tools developed in the last three decades were identified and their approaches for: synthesis of treatment trains, criteria used to evaluate them and methods used to identify optimal treatment alternatives were examined. The vast majority of approaches were found to be quite inflexible in allowing the user to both include new treatment processes in evaluation and specify how they can be combined in the course of assembling treatment trains. The review also showed that treatment train evaluation criteria used in the past varied tremendously,

184 Chapter 7 - Conclusions and Recommendations depending on the intended purpose of the DSS. A short list of criteria considered in this research was compiled that represents some of the most frequently used (important) quantitative and qualitative decisive factors in treatment train selection. A variety of approaches for optimising treatment train selection were used in the past, and the review focused on those that dealt with assembly of treatment trains from a large number of unit processes. The GA-based approach used in MOSTWATER was thought to offer advantages that could be used in the development of a DSS for integrated water reuse schemes. Several issues were addressed in the review of topics relevant to distribution of reclaimed water: system layout, sizing of components and changes in water quality. A number of methods of optimal layout (or simultaneous layout and design) of distribution systems were reviewed. However, at the preliminary planning level DSS developed here it was considered adequate for the user to provide preliminary layouts and include only branched networks. These simplifications resulted in focusing the review on LP-based methods for optimal sizing of distribution components, and advantages and limitations of this approach were outlined. A comprehensive review of investigations dealing with changes in quality of reclaimed water concluded that they are highly dependant on the configuration of the distribution system, its operation, and management and maintenance activities employed. Therefore, the DSS developed here did not explicitly account for changes in reclaimed water quality in the distribution system. The review of integrated approaches that account for both the treatment and distribution aspects of water reuse showed that they either do not incorporate provisions for the assembly of treatment trains, or consider the treatment train components in a simplified manner by considering only general levels of treatment. In addition, both the layout of the distribution system and the optimal design of its components (pipes, pumping stations and storage reservoirs) are in most cases greatly simplified. Also noted was the absence of evolution-based methods for optimisation of the integrated planning of water treatment and distribution, while a number of GAs-based applications in related fields were identified. The key components of the methodology developed for evaluation of integrated water reuse schemes are presented in Chapter 3, covering the knowledge base included in the DSS and computational modules for evaluation of treatment train performance and sizing of the distribution system. The knowledge includes the information needed for

185 Chapter 7 - Conclusions and Recommendations assessing the wastewater treatment for reuse, sizing and costing of the distribution system components and calculating potential revenues from the provision of reclaimed water. In addition to providing design and costing information for 44 unit processes, the knowledge base incorporates a method for specifying feasible ways of combining them in a treatment train. The method, inspired by that used for the Assembly Sequence Planning Problem, includes a matrix in which five types of operators are used to fully encapsulate the rules for treatment train assembly. The sizing of the distribution system is carried out in the DSS using a sequential approach, in which the operational policy and storage sizing are carried out concurrently by an NLP model and used iteratively with an LP-based procedure for sizing of system pumps and pipes. As a result, the approach produces the least-cost design in an efficient manner that can then be used in the optimisation of integrated water system. The approach developed for determination of optimal water reuse schemes and presented in Chapter 4 is hierarchical, in that it detaches the optimal sizing of the distribution system from the optimisation of the overall water reuse scheme. The “inner” problem of optimal distribution sizing is considered linear and solved using the sequential approach, while the solution of the “outer”, non-linear problem of selecting treatment options and end-users is addressed using different methodologies appropriate for the size of the scheme under consideration. The following three methods are used in the DSS: 1. If the secondary effluent is to be reclaimed and the number of potential customers is not large (e.g. less than five), enumeration is used to determine the best design alternatives for all combinations of potential end-users, 2. For schemes where the secondary effluent is to be reclaimed for a large number of potential end-users (e.g. tens or even hundreds), a simple GA is used for optimal user selection, and 3. If the source of water is raw sewage or primary effluent, the GA optimisation algorithm was developed that conducts a simultaneous search of least-cost design alternatives and the selection of end-users. The last approach required the development of a novel chromosome representation of design alternatives. In addition, the generation of initial population as well as the crossover and mutation operators were designed to limit the search to treatment alternatives that are considered feasible, thus improving the efficiency of the optimisation. 186 Chapter 7 - Conclusions and Recommendations

Both single and multiobjective formulations were implemented in the DSS. In the single-objective GA, the fitness function was defined by combining the project net present value, percent of demand satisfied, treatment qualitative criteria score and the effluent quality achieved by assigning individual weights to each of them. The multi- objective GA included further objectives related to the utilisation of resources. The widely used NSGA-II algorithm was implemented in the DSS for multi-objective optimisation. The developed DSS was implemented in a user-friendly software tool named WTRNet, described in Appendix B, which was then used to conduct the testing and sensitivity analyses of methodologies on two test cases, as described in Chapter 5. The results of testing of the sequential approach for sizing of reclaimed water distribution systems showed that both the LP and NLP models, used sequentially in the optimisation process, function correctly when applied to allocation of reclaimed water and sizing of distribution system facilities. Testing of GA operators used to generate the initial populations of design alternatives and to perform the crossover and mutation resulted in slight changes being made to the mutation operator, while the operators proved to function adequately despite some complications that arise from their implementation. The results of NLP algorithm sensitivity analysis indicated that the sensitivity of this parameter is moderate if the cost of alternate supply to a single user is lowered below a level set for all other users, and high if the parameter for a single user is raised. The selection of appropriate population size and rates for application of GA operators was determined concurrently through sensitivity analyses, which consisted of determining the success rate of finding the known global optima for a wide range of parameter combinations. The relatively high population size required for good success rates was thought to be indicative of a lack of the mutation operator effectiveness in promoting the diversity of solutions. Sensitivity analyses of the single-objective GA formulation concluded that the current fitness function formulation weight heavily on the financial performance of a water reuse scheme, and emphasised the need for multi-objective optimisation. The effects of uncertainties in pollutant removal efficiencies used in the knowledge base on the treatment options and costs produced by the optimisation were also examined through sensitivity analyses. It was concluded that the presence of some processes is necessary in optimal treatment trains since they are primary removal mechanisms for certain types of pollutants, for both the average and high pollutant removal levels.

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Results of the optimisation runs where low pollutant removal levels were assumed, however, indicated that vastly different (and more expensive) treatment trains containing larger number of processes were needed. Chapter 6 presents the results of application of WTRNet to a case study in the City of Waterloo (Ontario, Canada). The study area and the rationale for water reuse are described, followed by a detailed investigation of water reuse options for sixteen potential end-users of different type. The results of the least-cost optimisation showed that there is a fair amount in variability in optimal lifecycle costs, and that upgrading the existing WWTP becomes more attractive economically at higher levels of demand satisfaction. Each quantitative criteria was also added in turn as the third objective in the optimisation to: (i) determine financially optimal design alternatives for different levels of demand satisfaction that are also desirable from the perspective of other criteria that may be specified by the decision maker, (ii) explore the potential tradeoffs that may exist between the objectives and (iii) derive design principles through examination of patterns in optimisation results. Using the treatment train qualitative criteria score as the third objective showed that most of the optimal schemes that have the highest qualitative scores are not the least- cost solutions for various levels of demand satisfied. Including the land required as the third objective to be minimised resulted in generally higher scheme costs, compared to two-objective optimisation. The optimisation results also showed that limited land availability can have significant impact on the lifecycle cost of the water reuse scheme considered in the case study. The optimisation runs that included minimisation of sludge production showed to have potentially significant effect on the lifecycle cost of a scheme. This is particularly true for higher levels of demand satisfaction, where lower sludge production inevitably leads to higher lifecycle cost. The optimisation results where minimisation of energy was included as the third objective reflected the significance of energy costs in water reuse, as they were not drastically different from the optimisation results that considered only lifecycle cost and demand satisfaction. Similarly, including the labour required to operate a treatment facility, which is generally proportional to the size of the facility, resulted in optimisation results not significantly different form those determined through optimisation with respect to cost and level of demand satisfaction. Patterns in the inclusion of unit processes in optimal treatment trains, determined using a selection of qualitative criteria as the third objectives, showed that processes that

188 Chapter 7 - Conclusions and Recommendations could intuitively be favoured by a planner for any given objective were indeed included in optimal design alternatives. Conversely, considering only the lifecycle cost of a water reuse scheme in the optimisation, in conjunction with demand satisfaction, was shown to result in the selection of processes that are far from optimal if other criteria are used. Examinations of treatment trains most frequently included in optimal design alternatives using different objectives showed that the variation in their inclusion is not as drastic for each individual case of the third objective, since some unit processes tended to be included in most of the top five treatment trains. Examination of inclusion of potential end-users in optimal design alternatives indicated that a number of factors may be important in the selection process, such as their elevation, required water quality, seasonality of demand, location in relation to other end-users, level of demand relative to other end-users, etc. The appropriateness of two suggested criteria, distance of end-user from the water reclamation facility and Piping Efficiency Ratio, for selection of end-users was conducted. The results demonstrated higher inclusion rates of end-users closer to the WWTP in the case study, but also indicated that distance alone cannot be used as a general rule. In the case of the PER, no correlations with this value and the rate of inclusion of potential end-users in the case study was found.

7.2 Conclusions and Recommendations for Further Research

A review of current practices and future trends in water reuse worldwide leads to a conclusion that the planning of water reuse schemes of increasing scale and complexity will require a structured approach for their evaluation. One such approach is proposed here, which takes into account the interactions that exist between the individual scheme components in evaluation and selection of most promising design alternatives through optimisation. The conclusions reached from the development and application of the approach can be summarised as follows:

• Despite the fact that a number of decision support methodologies have been developed in the past that deal with various aspects relevant to reclaimed water treatment and distribution, no single tool currently exists that addresses the issues of treatment, distribution and customer selection concurrently.

• The methodology developed in this research for synthesis and evaluation of treatment trains provides a generic framework that includes guidance in the selection process, provides flexibility for capturing individual designer preferences

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and incorporation of any number of unit processes, and evaluates the generated treatment trains on a number of criteria.

• An efficient approach was developed for sizing the components of a reclaimed water distribution system that, although limited to branched network topology, addresses issues of seasonal distribution of reclaimed water and determines least- cost distribution system rapidly.

• The size of the search space in optimisation of integrated water reuse systems was examined, and drastically reduced through application of treatment train assembly rules which prohibit illogical sequences of unit processes.

• The dimensionality of the search space of the GA is reduced by using a hierarchical approach, in which the “inner” water distribution system optimisation problem is solved separately from the outer”(non-linear) problem of selecting treatment options and end-users.

• Optimisation methodologies were developed that are appropriate for integrated water reuse schemes of different size and complexity, including an enumeration approach for schemes of smaller complexity, a simple GA for the selection of most promising end-users and a comprehensive GA for simultaneous selection of treatment trains and customers.

• Custom crossover and mutation operators were developed for the comprehensive GA, which improve the efficiency of the search by limiting it to only those treatment options that meet pre-specified rules for their assembly.

• The comprehensive GA optimisation methodology incorporates a single-objective formulation, as well as a multi-objective formulation that allows the inclusion of objectives related to the utilisation of resources and user preferences, and which is solved using the NSGA-II algorithm.

• The methodologies for assessment and optimisation of integrated water reuse schemes were incorporated into hydroinformatics software tool named WTRNet, which provided a user-friendly interface for testing and application of the approach on case studies.

• Analyses of water reuse options for the City of Waterloo (Ontario, Canada), conducted using the WTRNet software, demonstrated that the objectives used in the optimisation influence the selection of optimal schemes and result in

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identifiable patterns in the selection of treatment options leading to the development of case-specific design principles.

• An additional conclusion reached from the application of WTRNet on the City of Waterloo case study is that the optimal selection of potential end-users is influenced by a number of factors, not accounted for in the criteria suggested or used in the past to perform the selection. It is hoped that the methodologies developed and incorporated in WTRNet will provide planners of future water reuse schemes with a useful tool for an efficient identification of reuse options. The methodology developed and tested in this research presents, to author’s knowledge, a first attempt to integrate the selection of treatment trains, optimal design of a distribution system and selection of end-users of reclaimed water comprehensively in a DSS. Although the integration of these components has been demonstrated to have benefits in deriving optimal water reuse schemes, it also required that some simplifying assumptions be made in the development of methodologies. Possible future improvements to the methodology are presented here, which can serve as a potentially useful guidance for further expansion. The DSS considers a single treatment facility reclaiming water for a large number of users. The methodology could be expanded to include cases where multiple treatment facilities are used (e.g. two WWTPs at either end of a city), to simultaneously consider treatment options at all facilities, selection of end-users and allocation of reclaimed water. An additional enhancement of the methodology would be to allow distributed treatment facilities to be considered, where reclaimed water of different quality is provided to different users (e.g. “designer” water produced at the West Basin Municipal Water District in California (Miller 2003)). The modular architecture of the software should allow these improvements without extensive modifications, while broader testing would be required for the optimisation methodology. The sensitivity analyses conducted as part of the DSS testing addressed the issues of uncertainties briefly. These could be addressed directly in the DSS by including, for example, uncertainties that exist with regard to information contained in the knowledge base (unit process performances and resource requirements), quality of wastewater used as a source, as well as the demand and expected revenues from potential end-users. With regards to the optimisation methodology employed in the DSS, there are a number of issues that could be explored. A potentially more robust mutation operator could be 191 Chapter 7 - Conclusions and Recommendations constructed through further investigations, which would replace single unit processes with a combination of processes, in an attempt to improve the diversity propagation in the optimisation process. Locations of pumping and storage facilities could be included in the optimisation, by specifying the nodes where facilities could be located in the appropriate part of the chromosome and modifying the operators accordingly. A more elaborate approach would also address the optimal layout of the distribution system. Also, the current limitation in the number of objectives that can be considered concurrently in the optimisation using the NSGA-II algorithm could be removed by employing algorithms capable of including more objectives in the optimisation. Finally, the development of the DSS was conducted under the assumption of perfect knowledge of the ultimate demand for reclaimed water at the end of the planning period, which is used as input to the model. An improvement could be made to incorporate the staging of water reclamation projects into the decision making process, either by repetitive running of the optimisation problem as it is currently formulated, or by explicitly incorporating the decisions taken at each stage of project development.

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