Multi-objective Optimization of Combined Sewer Systems Using SWMM 5.0

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1 J. Cvl Eng. Archtect. Res. Vol. 2, No. 10, 2015, pp Receved: Aprl 28, 2015; Publshed: October 25, 2015 Journal of Cvl Engneerng and Archtecture Research Mult-objectve Optmzaton of Combned Sewer Systems Usng SWMM 5.0 Upaka Rathnayake Department of Cvl Engneerng, Faculty of Engneerng, Sr Lanka Insttute of Informaton Technology, Malabe, Sr Lanka Correspondng author: Upaka Rathnayake Abstract: Combned sewer overflows (CSOs) are frequent n many ctes durng stormy weather. CSOs are not only an envronmental ssue but also nduce an adverse aesthetc vew for major ctes, worldwde. Many engneerng solutons have been proposed by researchers to reduce, f possble to avod CSOs; however, most of these solutons requre sewer network capacty enhancement. Therefore, most of the proposed engneerng solutons are based on structural measures. However, they are not the best solutons snce most of these measures requre new structural components and thus captal requrement. Therefore, f possble, control of exstng combned sewer networks to mnmze the CSOs and ther adverse envronmental effects would be an deal soluton. However, a holstc control algorthm based on envronmental concerns s yet to be tabled. Therefore, ths paper presents an mproved approach n control of exstng combned sewer systems to mnmze the adverse envronmental effects due to the combned sewer overflows. A mult-objectve optmzaton approach was developed, consderng flows and water qualty n combned sewer flows and the wastewater treatment costs. The presented mult-objectve optmzaton approach shows a consderable mprovement n controllng urban wastewater systems compared to the prevous work by the same author. The mproved algorthm has advantages n soluton space of mult-objectve optmzaton approach. Furthermore, t elmnates achevement of nfeasble solutons unlke the other constraned mult-objectve optmzaton approaches. Key words: Combned sewer overflows, combned sewer networks, genetc algorthms, mult-objectve optmzaton, Storm Water Management Model (SWMM 5.0). Nomenclature: BOD C T C1-C7 COD CSOs DO DWF EQI F 1 F 2 O h C h S h ST I NO X NSGA II Bochemcal oxygen demand (mg/l) Treatment cost at treatment plant (ϵ/year) Interceptor sewer conduts Chemcal oxygen demand (mg/l) Combned sewer overflows Dssolved oxygen (mg/l) Dry weather flow Effluent qualty ndex (kg/day) Frst objectve functon Second objectve functon Combned sewer overflows from sewer chamber (m 3 /s) Water level n sewer chamber (m) Spll level of sewer chamber (m) The water level of the storage tank Catchment nflow to node(m 3 /s) Ntrates / ntrtes (mg/l) Non sorted genetc algorthm II O1-O7 P q q max, q s q T Q S T1 -Z T1 T1-T7 T8-T9 TKN TSS WWTP Orfces Polluton load to recevng water from th sewer chamber Through flow n nterceptor sewer at node I (m 3 /s) The maxmum flow rate at th condut Flow to the storage tank from CSO chamber Wastewater volume flowrate (m 3 /s) Flow from th sewer chamber to nterceptor node I (m 3 /s) Selected optmal solutons Sewer chambers Storage tanks Total Kjeldahl ntrogen (mg/l) Total suspended solds (mg/l) Wastewater treatment plant 1. Introducton Many ctes experence CSOs durng the wet/storm weather perods. Ths s due to the capacty lmtaton of the exstng combned sewer networks. In addton,

2 986 Mult-objectve Optmzaton of Combned Sewer Systems Usng SWMM 5.0 nflows to these combned sewer networks have ncreased n recent days. Rapd urbanzaton has led to an ncrease of the populaton n ctes and that causes addtonal nflows to the sewer systems. In addton, clmate change effects n some parts of the world have drected more nflows to these combned sewers. CSOs when drectly dscharged to the natural water bodes cause many envronmental problems. Concern on aquatc lfe s at a great threat. However, the aesthetc damage s not secondary to the aquatc lfe threat. Many proposed engneerng solutons for CSOs are based on structural measures. However, they are not among the best solutons, snce most of these measures requre new structural components. Therefore, control of exstng combned sewer networks show a great potental n mnmzng the adverse effects of the CSOs. A holstc optmal control strategy s stll a challenge, when consderng the water qualty effects and computaton dffcultes. Most of the lterature on controllng combned sewer systems s based on volumetrc measures [1-4]. However, they have faled to address the ssue of water qualty n both combned sewers and recevng waters. In addton, generally, economc measures such as treatment cost at downstream wastewater treatment plant are not consdered. Rathnayake and Tanymboh [5-8] have successfully addressed these ssues n ther prevous work. Mnmzng the polluton load from CSOs and mnmzng the cost of wastewater treatment were the two objectves n ther earler research. Apart from Rathnayake and Tanymboh [5-8], Fu et al. [9-11] have ncorporated some water qualty parameters to ensure the recevng water qualtes from CSOs. Concentratons of BOD, total ammona and DO n recevng water were consdered at an ndvdual level. Nevertheless, they were unable to utlze the other mportant water qualty parameters. More mportantly, the water qualty parameters were not aggregated to develop a sngle ndex to gve the water qualty. Real-tme control s another aspect of control of combned sewer systems. A recent research publcaton by Vezzaro et al. [12] showed some new technques n real-tme control of combned sewer systems based on water qualty. However, t was a prelmnary study and a holstc soluton s yet to be presented. Not only controllng of combned sewer networks, but also desgnng a proper combned sewer network s a challengng task. Ths s due to the transent dynamcs of water flow and stochastc nature of ranfall [13]. Maurco-Iglesas et al. [13] have presented a novel generc method to desgn a self-optmzng controllers system for combned sewers. In addton, Baek et al. [14] have presented an optmal approach n desgnng the mult-storage facltes n a combned sewer network to mtgate the combned sewer overflows. Moreover, Cozzolno et al. [15] have presented an nnovatve approach for urban dranage szng usng optmal desgn of network systems. They have used genetc algorthms coupled wth a steady and unform hydraulc model to dentfy the optmum sze of the ppe network for rural dranage network. Furthermore, Noojen and Kolechkna [16] have presented an approach to control the sewer systems n low-lyng areas of Netherlands. Ths research nvolves control of pumpng systems to reduce the CSOs.To the Netherlands, t s very crtcal to control any CSOs especally n low-lyng areas. Therefore, the above research emphaszes the usage of optmzaton strateges n the related research felds. However, ths paper presents an mproved approach n controllng combned sewer networks for that of presented n Rathnayake and Tanymboh [8]. Storage tanks proposed n the combned sewer network n Rathnayake and Tanymboh [8] were off-lne storage tanks. However, on-lne storage tanks are proposed n ths paper as an alternatve and more mportantly, the flow control n these on-lne storage tanks has taken nto the account. A mult-objectve optmzaton approach based on the polluton load to the recevng water from CSOs and the cost of wastewater treatment s proposed. The performance of the optmzaton

3 Mult-objectve Optmzaton of Combned Sewer Systems Usng SWMM approach developed s demonstrated here on an nterceptor sewer system wth promsng results. 2. Problem Formulaton Fg. 1 shows a schematc dagram of a typcal nterceptor sewer. Dependng on the capacty of the sewer chambers and the nterceptor sewer, CSOs (O ) occur. The frst objectve functon (F 1 ) was formulated to mnmze the polluton load from CSOs to the recevng water. Polluton load to the recevng water was formulated usng the effluent qualty ndex (EQI). The EQI s an ndex to represent fve mportant water qualty parameters, ncludng total suspended solds (TSS), chemcal oxygen demand (COD), fve-day bochemcal oxygen demand (BOD), total Kjeldahl ntrogen (TKN) and ntrates/ntrtes (NO X ). A detaled explanaton of ths EQI can be found n Rathnayake and Tanymboh [5-7] and n Rathnayake [17]. Eq. 1 gves the formulaton of the frst objectve functon. Mnmze F 1 n P (1) 1 where n and P are the number of nterceptor nodes or CSO chamber ponts and the polluton load to the recevng water from the th CSO chamber respectvely. P can be expressed as: P EQI (2) where EQI s the effluent qualty ndex at node. Eq. 3 shows the second objectve functon and t was formulated to mnmze the wastewater treatment cost at downstream wastewater treatment plant. Mnmze F C (3) 2 T where C T ( /year) s the treatment cost at treatment plant. Ths C T s expressed as a functon of the wastewater volume flow rate (q T ) to wastewater treatment plant. More nformaton on the dervaton of ths generc cost functon s gven n Rathnayake and Tanymboh [5-8] and n Rathnayake [17]. The followng contnuty equatons can be formulated wth reference to Fg. 1. Eq. (4) shows the contnuty equaton for the th nterceptor node. Q q q (4) 1 0 Eqs. (5) and (6) are the condtonal contnuty equatons for the th sewer chamber. When the water Fg. 1 Schematc dagram of nterceptor sewer system.

4 988 Mult-objectve Optmzaton of Combned Sewer Systems Usng SWMM 5.0 level n the sewer chamber (h c ) s less than the splllevel of the chamber (h s ), Eq. (5) governs thecontnuty, whereas Eq. (6) s for when the water level of sewer chamber s greater than the spll level. hc AC I Q h t ; C hs (5) hc AC I Q O ; h C hs (6) t A C s the surface area of the th CSO chamber. Except to the above gven contnuty equatons, the followng Eq. 7 shows the flow constrants n the nterceptor sewer system. 0 q qmax, (7) where q max, s the maxmum flow rate at th condut. Fg. 2 shows the schematc dagram of an on-lne storage tank. q s and h ST are flow to the storage tank from CSO chamber and the water level of the storage tank, respectvely. When the water level of the sewer chamber (h c ) reaches the spll level of the chamber (h s ), the storage tank starts fllng. Flow to the storage tank (q s ) stops when the storage tank reaches ts maxmum capacty. Ths wll then lead to CSOs through the correspondng CSO chamber. These controls are formulated nsde the hydraulc smulaton model by usng the control rules. 3. Solutons to the Mult-objectve Optmzaton Approach The hydraulc model SWMM 5.0 [18] was lnked wth the mult-objectve optmzaton module, NSGA II [19] usng C programmng language. SWMM 5.0 s a powerful hydraulc model. It s capable of smulatng water qualty n the combned sewer systems. On the other hand, NSGA II s a wdely used mult-objectve optmzaton module. NSGA has been successfully appled to many real world mult-objectve optmzaton problems n varous dscplnes ncludng n urban wastewater systems [11, 20-21]. Rectangular orfces at the bottom of each CSO chamber have been used to control the wastewater to the nterceptor sewer from the correspondng CSO chamber. The openngs of the orfces were randomly generated as the decson varables of the mult-objectve optmzaton problem. Next, a full hydraulc smulaton, ncludng water qualty routng was carred out usng SWMM 5.0. The results from the smulaton were used to calculate the polluton load F 1 and the wastewater treatment cost F 2. Then, the NSGA II optmzaton module was run to obtan the optmal solutons. Fg. 2 Schematc dagram of sewer chamber wth on-lne storage tank.

5 Mult-objectve Optmzaton of Combned Sewer Systems Usng SWMM Mass balance and conservaton of energy were automatcally satsfed by the hydraulc model. Maxmum flow rates allowed through conduts (Eq. 7) were formulated nsde the hydraulc model. SWMM 5.0 condut features n defnng the maxmum flow rates were used n formulatng the maxmum flow rates allowed through these conduts. By contrast, Deb s bnary tournament selecton technque was used to handle constrants n Rathnayake and Tanymboh [8]. A detaled explanaton about Deb s constrant handlng technque can be found n Deb et al. [19] and Rathnayake and Tanymboh [17]. The obtaned optmal solutons were plotted as a Pareto optmal front. Dependng on the sewer network controller s aspratons, optmal solutons can be selected from the Pareto optmal front. Then, the optmal control settngs for the correspondng optmal solutons can be obtaned. 4. Results and Dscusson The performance of the mult-objectve optmzaton model was tested on a smple nterceptor sewer system. The nterceptor sewer system found n Thomas [22] was modfed and used to analyze the performance of the developed mult-objectve optmzaton approach. A detaled descrpton of ths nterceptor sewer system, ncludng the modfcatons, can be found n Rathnayake and Tanymboh [5-8]. Fg. 3 shows the modfed nterceptor sewer system. Durnal effects of the DWF were not consdered n ths study; however, average flow rates were fed to the T1-T7 CSO chambers. In addton, flow hydrographs from sngle storms were fed to the CSO chambers. More nformaton of these hydrographs can be found n Rathnayake and Tanymboh [5-7] and n Rathnayake [17]. Fve dfferent land-uses, ncludng resdental, ndustral, commercal, agrcultural and md urban were assumed when generatng the pollutographs for fve dfferent water qualty consttuents. These pollutographs of fve dfferent water consttuents (TSS, COD, BOD, NO X, and TKN) can be found n Rathnayake [17]. Real-coded NSGA II program was used to obtan the optmzaton solutons. The optmzaton process was done wth a populaton of 100, 100 generatons and a smulated bnary crossover probablty of 1. Many optmzaton runs wth dfferent random seeds were conducted. Dfferent mutaton probabltes were tred n dfferent runs. However, t was found out that, 0.4 mutaton probablty gves the best optmal results for ths optmzaton problem. Fg. 3 Interceptor sewer system.

6 990 Mult-objectve Optmzaton of Combned Sewer Systems Usng SWMM 5.0 Routng tme-step n SWMM 5.0 was kept at 30 seconds, and the results were obtaned n 15 mnutes. Then, the NSGA II optmzaton module was run usng the obtaned results. Fg. 4 shows the best Pareto optmal front that was obtaned under 0.4 mutaton rate. The Pareto optmal front s a set of optmal solutons, whch are obtaned after 100 teratons. These solutons present the trade off between the two objectve functons (F 1 and F 2 ). Each GA run took about 8 mnutes on a Pentum 4 desktop personal computer wth a Core 2 Duo processor and 4 GB of RAM. Optmal solutons S T1 to Z T1 were selected for further assessment. Soluton S T1 gves the mnmum polluton load to recevng water, whereas soluton Z T1 that shows the mnmum wastewater treatment cost. Control settngs for these optmal solutons (S T1 to Z T1 ) were obtaned and the hydraulc smulatons were carred out (Tables 1-3). Fg. 5 gves the progress of the GA for the treatment cost objectve functon. Mnmum values of the objectve n several generatons show the convergence of the mult-objectve optmzaton approach. As t s expected n GAs, t can be clearly seen that a rapd convergence happened durng the ntal generatons. However, durng the later generatons convergence rate keeps steady. Ths convergence s very common for the better soluton strateges n mult-objectve optmzaton problems. Therefore, the proposed mult-objectve soluton strategy s n ts acceptable condton for producng better results. Fg. 4 Best Pareto optmal front acheved for 15 mnutes. Fg. 5 Functon evaluatons for mnmum treatment cost for 15 mnutes.

7 Mult-objectve Optmzaton of Combned Sewer Systems Usng SWMM Table 1 gves the flow rates through the nterceptor sewer sectons at 15 mnutes for solutons S T1 to Z T1. As stated n the problem formulaton secton (Eq. 7) the flow rates through sewer conduts were constraned to the respectve maxmum flow rates. Maxmum flow rate allowed through C1 to C3 s 3.26 m 3 /s and that of C4 to C7 s 7.72 m 3 /s. It can be clearly seen n Table 1 that the flow rates through these conduts are less than or equal to the maxmum allowed flow rates for all the tabulated cases. Ths observaton presents that the developed mult-objectve optmzaton approach produces feasble solutons. In addton, t shows that the constrant handlng approach that was used n ths study works well. Obtanng feasble solutons s an mportant feature of ths soluton approach. The feasblty solutons tell how relevant the obtaned the results n the real world envronment s? Table 2 shows the polluton loads for solutons S T1 to Z T1. Soluton S T1 gves the mnmum polluton load to recevng water whereas the Soluton Z T1 gves the mnmum wastewater treatment cost whch has the largest polluton load. The mnmum polluton load soluton (S T1 ) shows zero polluton load to the recevng water from CSOs. Ths s very nterestng. Even at a hgher correspondng wastewater treatment cost, the approach shows zero polluton loads to recevng water, thus to enhance the water qualty standards n the recevng water. Smlarly, the mnmum treatment cost soluton s an nterestng soluton. There may be cases wth lower budgets at local governments to treat the wastewater, specally, towards the end of the fscal year. Therefore, t may be a better dea to save some money at a cost to the envronment by allowng some CSOs (whch s a current practce n some ctes). Therefore, Table 2 reveals a consstent pattern that suggests the proposed optmzaton model yelds satsfactory results. Table 1 Flow rates through nterceptor sewer sectons at t = 15 mnutes for selected solutons. Soluton Interceptor sewer flow rates(m 3 /s) C1 C2 C3 C4 C5 C6 C7 S T T T U T V T W T X T Y T Z T Table 2 Polluton loads at CSO chambers at t = 15 mnutes for selected solutons. Soluton Polluton loads (kt/day) T1 T2 T3 T4 T5 T6 T7 S T T T U T V T W T X T Y T Z T

8 992 Mult-objectve Optmzaton of Combned Sewer Systems Usng SWMM 5.0 Table 3 Wastewater depths at CSO chambers and storage tanks at t = 15 mnutes for selected solutons. Soluton Wastewater depths (m) T1 T2 T3 T4 T5 T6 T7 T8 T9 S T T T U T V T W T X T Y T Z T Hghlghted values presents the exstence of CSOs. Table 3 presents the wastewater depths at CSO chambers and storage tanks for Solutons S T1 to Z T1. Geometrc depth of these chambers and tanks for T1 to T9 are 5.42, 6.91, 7.95, 8.04, 8.18, 8.47, 9.26, 6.91 and 8.18 m, respectvely. Wastewater depths hghlghted n grey n Table 3 have more than the geometrc capacty of the correspondng CSO chambers. In other words, they represent the combned sewer overflows. These hghlghted wastewater depths are entrely consstent wth the polluton load dscharges seen prevously n Table 2. For example, chamber T3 s full for solutons U T1 to Z T1. Accordngly, Table 2 shows that polluton loads to the recevng water occur at chamber T3 for same U T1 to Z T1 solutons. Another nterestng observaton can be seen for chamber T5 for solutons X T1 to Z T1. Table 2 shows solutons X T1 to Z T1 have no polluton load dscharges at chamber T5 that s full as Table T3 shows. In fact, ths s due to the T9 on-lne storage tank that s not full. In addton, t can be seen n Table 3 that there s wastewater n the T8storage tank and, moreover, tank T2 s not full. Consequently there are no polluton load dscharges at the T2 chamber as Table 2 shows, for all solutons X T1 to Z T1. These observatons justfy the role of on-lne storage tanks. 5. Conclusons A consderable mprovement n controllng urban sewer systems can be seen compared to the work presented n Rathnayake and Tanymboh [8]. There s no ssue n extractng the feasble solutons among nfeasble solutons from the optmzaton module, snce constrant handlng was conducted external to the mult-objectve optmzaton approach. However, the proposed model gves the optmal CSO control settngs where a sngle set of statc control settngs s used throughout the storm duratons. Further research s requred to develop an optmzaton model whch can cater the dynamc control of urban sewer systems. References [1] G. Cembrano, J. Quevedo, M. Salamero, V. Pug, J. Fgueras, J. Mart, Optmal control of urban dranage systems-a case study, Control Engneerng Practce 12 (2004) 1-9. [2] S. Darsono, J.W. Labade, Neural-optmal control algorthm for real-tme regulaton of n-lne storage n combned sewer systems, Envronmental Modellng & Software 22 (2007) [3] B.Beraud, M. Mourad, E. Soyeux, C. Lemone, M. Lovera, Optmsaton of sewer networks hydraulc behavour durng wet weather: couplng genetc algorthms wth two sewer networks modellng tools,n: Proceedngs of 7th Internatonal Conference on Sustanable Technques and Strateges for Urban Water Management, 2010, pp [4] B.J. Duran, C.O. Martnez, G. Cembrano, Hybrd modelng and recedng horzon control of sewer networks, Water Resources Research 50 (2014) [5] U.S. Rathnayake, T.T. Tanymboh, Mult-objectve optmzaton of urban wastewater systems, n: Proceedngs of 10th Internatonal Conference on Hydronformatcs, 2012, pp [6] U.S. Rathnayake, T.T. Tanymboh, Integrated Optmal

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