PREDICTION OF SEWAGE QUALITY BASED ON FUSION OF BPNETWORKS

Transcription

1 INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 9, NO. 2, JUNE 2016 PREDICTION OF SEWAGE QUALITY BASED ON FUSION OF BPNETWORKS Ljuan Wang School of Electroncs and Informaton Engneerng X an Technologcal Unversty X an, , Shanx, Chna Emal: wanglj1900@sna.com Submtted: Dec. 17, 2015 Accepted: Apr. 2, 2016 Publshed: June 1, 2016 Abstract-Sewage treatment system s a complcated nonlnear system wth mult-varables, chemcal reacton, bologcal process and altered loads, hard to descrbe mathematcally. Thus predcton of the effluent qualty parameters of sewage treatment plant has beng a challenge. In ths paper we adopt fuson of two BP networks to predct sewage qualty parameters wth a popular process Cyclc Actvated Sludge System (CASS). We take use of SVM (support vector machne) to classfy the nput data nto two knds, and tran the correspondng BP networks wth the two knds of data. Before usng SVM to classfy the nput data, PCA (prncple component analyss) s used to analyze the correlaton between sewage qualty parameters. Then we predct the value of sewage qualty parameters wth the fuson results of the two BP networks. Test results of the case study show that fuson of BP networks not only can mprove the stablty of BP networks but also can mprove the predcton accuracy. Index terms: sewage qualty parameters, BP networks, SVM; fuson. 909

2 Ljuan Wang, PREDICTION OF SEWAGE QUALITY BASED ON FUSION OF BP NETWORKS I. INTRODUCTION Wth the development of envronment protecton, sewage treatment s becomng a hot ssue. A great deal of methods for sewage treatments has been presented, and the CASS (Cyclc Actvated Sludge System) s used unversally. Snce sewage treatment process s closely lnked wth sewage sources, chemcal composton, flow rate, bologcal process condtons, and the recycle rate of the settled sludge, real-tme montor and control for better effluent qualty has become ncreasngly challengng. Clearly, a precson predcton model of effluent qualty s necessary for the sewage treatment system. It s also proved n some papers [3-5] that a precson predcton model of effluent qualty s helpful to control a sewage treatment plant n real-tme for better effluent qualty effectvely. For example, n [3] a predcton model of effluent flow cooperated nto the sewage treatment plant control system determned the approprate level of sewage treatment plant anaerobc storage tank, mproved the effcency of treatment. In [4] a predcton model of effluent qualty acts as the dentfcaton model of controlled object n a sewage treatment control system, make the sewage treatment system controlled wth hgh accuracy. Addtonally, as a hgh energy consumpton ndustry, the energy consumpton of sewage treatment plant s concerned especally. Accordng to sewage treatment process, energy consumpton of sewage treatment plant can be categorzed nto three knds,.e. energy consumpton of pretreatment, bologcal-chemcal treatment process, and sludge treatment. Among the three knds energy consumpton, energy consumpton of bologcal-chemcal treatment process s the major part, whch nearly accounts for 60% of the total energy consumpton[7-8]. Energy consumpton of bologcal-chemcal treatment process has a close relaton wth sewage qualty parameters. Predctng sewage qualty parameters accurate and fast help to control aeraton system automatcally and reduce the energy consumpton [9-13]. Among the effluent qualty parameters, COD (Chemcal Oxygen Demand) s mportant. Because t s a comprehensve ndcator of the total amount of organc n sewage. The content of organc matter s one of the ndcators for classfyng the envronmental qualty of the natural water. It s also the bases for judgng whether the water s polluted or not. BOD(Bochemcal Oxygen Demand)s a comprehensve ndcator about pollutants n sewage, such as organcs. It s bochemcal oxygen demand that s need when bologcal and chemcal reacton of organcs 910

3 INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 9, NO. 2, JUNE 2016 occurred. In ths paper, we select COD and BOD as the sewage qualty parameters to be predcted. Methodology used n predcton of effluent qualty of sewage treatment plant nclude ANN (Artfcal Neural Network) [15-17] and SVM (Support Vector Machne) [3,4,6,18]. In [3], ANN s successfully used n the predcton of effluent flow. In [6], ANN s successfully appled n predcton of effluent qualty of sewage treatment plant of SBR process through soft-sensor modelng method. But ANN has dsadvantages of local mnmum and dependency on learnng sample numbers. SVM s a powerful machne learnng method based on small sample statstcal learnng theory [1,2]. It adopts structure rsk mnmzaton prncple whch avods local mnmum and effectve solves the over learnng and assures good generalzaton and better predct accuracy. The specal predomnance of SVM n resolve lmted samples, nonlnear functon and multdmensonal pattern recognton make t a powerful tools n predcton of effluent qualty of sewage treatment plant. In[4], SVM s adopted to predct the effluent qualty of a sewage treatment system of flocculaton process. However, the SVM wll be fal when some nput data are mssng. Except ths, SVM does not have a general soluton for nonlnear system. In ths paper we study the applcaton of SVM and Bp network n predcton effluent qualty of a sewage treatment wth a CASS process, present a predcton method based on SVM and Bp network of CASS sewage treatment process. We use fuson of two BP networks to mprove the stablty and predcton precson of BP networks. We take use of SVM to classfy the nput data of BP networks. Before classfyng, the outlers of the nput data are deleted. In order to make SVM work more effectvely, we analyze the nfluent qualty parameters and parameter to be predcted wth PCA (prncple component analyss), get prncple components, and choose the correspondng nfluent qualty parameters that affected parameters to be predcted obvously. Then, we set up fuson of BP networks based on SVM, and tran the networks wth some data, and tested the networks wth others, and fuse the results. The test result showed that the predcton method s effectve that could predct the dynamc consumpton durng control process wth hgh accuracy wth hgh accuracy and quck learnng speed. 911

4 Ljuan Wang, PREDICTION OF SEWAGE QUALITY BASED ON FUSION OF BP NETWORKS II. SEWAGE TREATMENT PROCESS AND ITS DIFFERENTIAL EQUATION MODEL The sewage treatment process as shown n Fgure1 manly adopts cyclc actve sludge to handle the nfluent sewage. The sewage frstly flows through coarse screen and fne screen, then nto aeraton tank meanwhle some actve sludge are plunged nto the tank and a blower blows oxygen necessary to reacton wth actve sludge nto the tank. After the reacton s fnshed, sewage handled flows out through contact tank. Some quantty of sludge produced by the reacton goes back nto aeraton tank for reacton contnually and others s dscharged. blower oxygen Inffluent coarse screen fne screen Aeraton tank Returen sludge contact tank Effluent Sludge condensaton Fgure 1. Block dagram of sewage treatment process In vew of the above process, accordng to the law of materal balance, the followng assumptons are made on the sewage treatment system of the actvated sludge process[14]: (1)Mcroorgansms are non-autotrophc mcroorgansms, and ther growth rate s greater than the mortalty rate, and ther growth rate satsfes the Monod equaton. (2)No bochemcal reacton occurred n the second sedmentaton tank. (3)Reflux sludge affect Influence of sludge age and yeld coeffcent. (4)Bomass of nfluent s zero. (5)Saturaton constant of organc substrate KS<<S (6)Only study the ntraton reacton of the system. Under the above assumptons, the dfferental equaton of the treatment process s shown as followed equaton. The parameters n equaton (1) see lterature [19]. Obvously, the dfferental equaton could not satsfy our requrement of predctng sewage qualty parameters. 912

5 INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 9, NO. 2, JUNE 2016 dss q ˆ F H Ss So ( S Ss) ( )( ) X (1 f ) b X dt V Y Ks Ss K So dso qf qf qr YH 1 Ss So SOF SO qr ˆ( )( ) X H dt V V Y Ks Ss K So qa b a(1 e )(S S ) O, sat dx ˆ H qf qw qf qr H Ss So X HF ( ) X H ( )( ) X H dt V V q q Y Ks Ss K So (1 f ) b X p H H SF H p H H H OH O H OH W R H OH (1) III. FUSION OF BP NETWORKS BASED ON SVM a. Classfyng of nput data based on SVM Suppose sample data x, y 1,, l nput, y R s present. Among them, n are n-dmenson sample x R are sample output, then the classfcaton s to fnd a functon f through sample tranng the x except for the tranng sample could fnd out correspondng y through f. nsenstve loss functon L ( x, y, f ) s defned as follow: 0 y f ( x ) L ( x, y, f ) y f ( x) y f ( x) else Where f s a real value functon on the feld X. The new loss functon descrbes a sort of nsenstvty model that f the dfference between predcted value and actual value s less than, and the loss equals to 0. We can search a sort of estmate regresson functon n lnear functon set, t s shown n the formula (4): n f ( x) w x b w, x R, b R (3) Where ( x1, y1),,( xl, yl) are ndependent dentcally dstrbuted data, b s deflecton value. The purpose of regresson estmate s to fnd approprate w and b to make sure that x except for sample can satsfy f ( x) w x b. For make sure the optmzaton problem have soluton, slack varable s, ˆ ntroduced. Computng the parameter n the formula(3) s equvalent to solve the optmzaton problem as follow: (2) 913

6 Ljuan Wang, PREDICTION OF SEWAGE QUALITY BASED ON FUSION OF BP NETWORKS l 1 2 ˆ 2 1 mn ( x) w C ( ) y ( w x b) subject to( w x ) ˆ b y, ˆ 0 1,, l Where C(C>0) s a constant called penalty factor whch be used to express the compromse between the smoothness of functon f and the value of that allow error s greater than. It manly react on regulate and control between how to enhance the generalzaton capacty and decrease the error. s a postve number and need enactment n advance, t s manly used to control the precson of algorthm hopng acheve. The form of shown s formula (7). We use Lagrange functon to translate the orgnal problem to ts dual problem: l 1 max Q( ˆ ) ( ˆ )( ˆ j j ) x x j 2 Subject to, j1 l ( ˆ ) y ( ˆ ) (4) nsenstve loss functon s 1 1 After solve above problems, we can get w and the estmate functon: l (5) l ( ˆ ) 0 (6) 1, ˆ 0, C l w ( ˆ ) x 1 l f ( x) ( ˆ ) x, x b 1 (7) Where x whch correspond to ( ˆ ) 0 s support vector, varable w reflects the complexty of the functon. Computaton complexty of functon estmate by support vectors s rrelevant wth dmenson of nput space and only depends on the amount of support vectors, deflecton value b can be calculated through KKT (Karush-Kuhn-Tucker)condton. We use kernel functon changes to: ' k( x, x ) to replace dot product, the regresson functon n the formula (7) l f ( x) ( ˆ ) k( x, x) b (8) 1 914

7 INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 9, NO. 2, JUNE 2016 The kernel functon ' k( x, x ) s radal bass functon. 2 x- x K( x, x ) exp - s 2 Classfcaton of effluent qualty data of the sewage treatment plant based on SVM s a black-box, whch s based only on nput-output measurements of sewage treatment process. In the classfcaton procedure, the relatonshp between nput and output of the sewage treatment plant can be emphaszed whle the sophstcated nner structure s gnored. Input and output vectors, x and y act as the learnng samples of the predcton model. By learnng the samples, the classfcaton model could provde output flag value or new nput vector x. The algorthm of classfcaton manly has two steps. (1)Set threshold of sewage qualty parameters to be predcted, and classfy and sgn the samples accordng to the threshold. (2)Tran the SVM, get a data set{ x, y }, 1,2,.., n, where x s nput data, n s numbers of samples, and y {0,1} s flag value of nput data. (9) b. Predcton of sewage qualty parameters based on BP network BP network s a knd of feed forward neural network. It ncludes nput layer, hdden layer, output layer. Assume that the nput layer of BP network has M nodes, the output layer of BP network has L nodes, and there s only one hdden layer whch has N nodes. Generally, N s larger than M and M s larger than L. Assume a ( 1,2,..., M) s output of the Neurons of nput layers, a j ( j 1,2,..., N) s output of the Neurons of nput layers, and y k ( k 1,2,..., L) s output of the Neurons of nput layers, y m s the output vector of neural network, y p s the expected output vector, followng equatons can be acqured. The th node of nput layer s M net x,where x ( 1,2,..., M) s the nput of neural network, 1 s the threshold of the th node. 915

8 Ljuan Wang, PREDICTION OF SEWAGE QUALITY BASED ON FUSION OF BP NETWORKS The jth node of hdden layer s net j N j1 wja j,where w, j s the weght of hdden layers, j s the threshold of the jth node. The kth node of output layer s net k L k1 w jk a j. The BP network for predctng sewage qualty parameters s shown n fgure2.the learnng algorthm s shown as followed. (1) Intalzaton: All weghted coeffcents are set as the smallest random numbers. (2) Provde tranng set. (3) The output of each neuron n the hdden layer and the output layer s calculated. (4) Calculate the error between the expected value and the output value. (5) Adjust the weghtng factor of the output layer. (6) Adjust the weghtng factor of the hdden layer. (7) Return (3) untl the error meets the requrements. k ph DO COD BOD SS Fgure 2. BP network for predctng sewage qualty parameters c. Fuson of BP networks based on SVM It s possble to fall nto local extremum when BP network s traned. The approxmaton capablty and generalzaton of BP network have a close relaton wth typcalty of tranng samples. The SVM are senstve to mssng samples and t cannot present a general soluton for nonlnear problem. 916

9 INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 9, NO. 2, JUNE 2016 Durng sewage treatment process, there s a serous couplng relaton among each parameters. Except ths, because the bologcal and chemcal reacton are nvolved n the sewage treatment process, the predcton parameter COD and BOD do not have a one-to-one correspondence relaton wth the parameters of the sewage qualty parameter. The tranng of BP network wll be faled when nput data are same or smlar whle there s a great dfference between output data. We present an fuson algorthm based on SVM and BP network. The nput data s classfed by the SVM, whch makes dfferent knd of data enter dfferent BP network. The BP networks are ph Samples A BP network ORP SS. SVM Samples B BP network Fuson COD BOD Fgure 3. Fuson of BP networks traned respectvely by dfferent samples. The output predcton values of two BP networks are fused by followng equaton. COD 2 BP wcod (10) 1 2 BOD w BOD BP 1 (11) IV. EXPERIMENT RESULTS AND ANALYSIS Accordng to above mentoned algorthm, we use fuson of two BP networks to predct sewage qualty parameters. The test results s presented and dscussed. Before classfyng and tranng and learnng of the nput data, there are data preprocessng n our work,.e. the elmnatng of outlers and PCA analyss of nput data. a. Elmnate of outlers 917

10 Ljuan Wang, PREDICTION OF SEWAGE QUALITY BASED ON FUSION OF BP NETWORKS To elmnate outlers of the data, a novel clusterng algorthm[8] s adopted n ths paper. In ths method each object s assgned to the cluster of ts nearest neghbor wthn a certan dstance. The dstance s called mmd(mnmum mean dstance). Gven a set of n objects y1, y2,, yn, n a d dmensonal space whch refers to the number of measurement varables, the mean mnmum dstance (MMD) s defned as formula 1. 1 MMD n n 1 mn j ( d k1 ( y k yjk) ) 2 1/ 2 If the dstance between an object and ts nearest pont s larger than 2*MMD, then the object s defned as an outler. The algorthm successfully dstngushed outlers from normal data as shown n fgure4,5. (12) Fgure 4. Part of Influent BOD Fgure 5. Part of Influent COD 918

11 INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 9, NO. 2, JUNE 2016 b. PCA(Prncpal Component Analyss) of sewage data In practce, COD (chemcal oxygen demand) and BOD (SS) are mportant ndces of water qualty. And they are dffcult to measure on-lne. So we selected COD and BOD as sewage qualty parameters to be predcted. Durng the process of sewage treatment, the DO (dssolved oxygen) and ORP (oxdatonreducton potental) are used to optmze adjust the aeraton qualty and the PH value s used to judge whether the systematc alkalnty be sut and be good for mcrobe s growth at the aerobc stage. At the anaerobc stage, the PH value and ORP not only provde the control nformaton of the de-ntrfcaton process, but also can act as the means of nput amount. The nterrelated experment results ndcated that the ORP, DO and PH value correlate wth COD concentraton n CASS reacton stage, especally n the stuaton that the ar quantty s nvarable and COD s hard to be degraded anymore, the ORP and DO value rse rapdly and then they wll turn to be statonary and level off n a certan hgh-value range. The PH value rses contnuously tll the COD stop degraded. Except ths, temperature and MLSS (mxed lqud suspend solds) also has co-relaton wth COD and SS. So we select the ORP, DO, PH, MLSS, temperature and COD, BOD, SS of nfluent as elements of varable x, nput of predcton model based on SVM, as shown n Fgure 2. Accordng to the correspondng responses between the sewage qualty parameters to be predcted and dfferent nfluent sewage parameters, PCA s utlzed to select the characterstc parameter that have a close relaton wth the sewage qualty to be predcted. The step s shown as followed. (1) The standardzaton of the nput data X n m. X ( X E( X )) / (var( X )) * 1/2 (2) Calculate the prncple component value by sngular value decomposton. If * X n m s the standardzed nput data, then * T X U V,where Un nand Vm mare orthogonal matrxes conssts of sngular vectors, s Dagonal matrx conssts of sngular values,.e. =dag(δ 1, δ 2,,δ n). Compute varance of each prncple component:λ =δ /(n-1). Compute Percentage of cumulatve varance : 919

12 Ljuan Wang, PREDICTION OF SEWAGE QUALITY BASED ON FUSION OF BP NETWORKS CPV ( k) k 1 n 1 / ( n 1) 2 / ( n 1) (3) Fnd the all of the percentage of cumulatve varance that s larger than a gven value, determne the prncpal component and reduce the dmenson of nput data. The analyss results of PCA of sewage data s shown n table 1. From table 1, t can be seen that percentage nformaton of nfluent quantty, PH, SS, DO, ORP can be represent by three prncple components. Accordng to table2, DO, ORP, and PH that respectvely has strong relatons wth prncple component1, 2, 3 have been selected as nput as BP network. Table 1: Results of PCA 2 (13) Comp onent Intal Egenvalues Total % of Varance Cumulatve % Table 2: Correlaton between each varables and prncpal component Component V V V V V

13 INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 9, NO. 2, JUNE 2016 c. Predcton by BP networks Bology-chemcal energy consumpton process ncludng bologcal and chemcal reacton s a severe nonlnear process. As a knd of artfcal neural network, back propagaton network has good ftness for nonlnear functon. Here, we used BP network to set the model. As shown n fgure 2, the model has one hde layers. The hdden layer has 15 cells. We traned the model wth 70 samples, test the model wth other sets of datum. COD tranng samples Table3: Tranng results of Bp network test samples tranng samples MSE Test samples 2 c BOD tranng samples test sample tranng samples MSE test sample 2 c From Table3, we can see the mean squared errors of test samples of the predcton model are small. It shows that the predcton model based on BP networks make good performance n effluent qualty predcton for sewage treatment plant of CASS process. When the numbers of tranng samples s changng, the vared range of mean squared errors s small. It shows that the degree of dependng on the sample of the predcton model s small,.e., the predcton model based on SVM has good generalzaton performance and small samples learnng ablty. It s also concluded that energy costng of a sewage treatment plant wth CASS process manly s dependent on nfluent n aeraton tank. COD and BOD of the sewage treatment plant could be predcted acutely by nfluent PH, ORP and DO n aeraton through a BP network model. 921

14 COD(mg/l) COD(mg/L) Ljuan Wang, PREDICTION OF SEWAGE QUALITY BASED ON FUSION OF BP NETWORKS d. Fuson of BP networks based on SVM In ths part, we predcted COD and BOD durng certan perod respectvely wth fuson model of BP networks and sngle BP networks. The predcton results of COD by fuson BP networks are shown n fgure 7. The predcton results of COD by sngle BP networks are shown n fgure 8. The predcton results of BOD by fuson BP networks are shown n fgure 10. The predcton results of BOD by sngle BP networks are shown n fgure 11. Fgure 6 shows the measured value of COD. Fgure 9 shows the measured value of BOD. From the results, t can be seen that BP networks have a good capablty n predcton of the sewage qualty parameters. It also can be concluded that durng predcton of sewage qualty parameters, fuson of BP networks mprove the stablty and learnng precson of the BP networks tme/hour Fgure 6. Measured value of COD tme/hour Fgure 7. Predcton of COD by fuson of BP networks 922

15 BOD(mg/L) BOD(mg/L) INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 9, NO. 2, JUNE 2016 Fgure 8. Predcton of COD by sngle BP network tme/hours Fgure 9. Measured value of BOD tme/hours Fgure 10. Predcton of BOD by fuson of BP networks 923

16 BOD(mg/L) Ljuan Wang, PREDICTION OF SEWAGE QUALITY BASED ON FUSION OF BP NETWORKS tme/hours Fgure 11. Predcton of BOD by BP network V. CONCLUSIONS In ths paper, a predcton method of sewage qualty parameter COD and BOD based on BP network fuson s presented. The BP network fuson s based on SVM. The BP network fuson mproves the stablty and precson of the tranng, and overcome the dsadvantages of sngle BP network. Durng the predcton process, the nput data frstly are classfed nto two knds by SVM accordng to ther characterstcs. Before classfyng, the outlers of the nput data has been deleted by a clusterng method; and the correlaton among parameters are analyzed whch s used to reduce the dmenson of the nput data, acqurng the parameter that have a close relaton wth the parameter to be predcted. After classfyng, the nput data are classfed nto two knds, and the BP network are traned by the correspondng data. The predcton value of the parameter COD and BOD are predcted wth the fuson value of two BP networks. The test results shows that the predcton error of fuson method s less than predcton error of common BP network. Except ths, the tranng stablty of BP network are also mproved. ACKNOWLEDGEMENTS Ths study s supported by the Scentfc research plan projects of Shaanx Educaton Department (15JK1358). 924

17 INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 9, NO. 2, JUNE 2016 REFERENCES [1] Vapnk, Vladmr N. Controllng the Generalzaton Ablty of Learnng Processes. TheNature of Statstcal Learnng Theory. Sprnger New York, 2000: [2]V.Vapnk. The nature of statstcal Learnng Theory, New York: Sprnger-Verlag,1999. [3] Baley, Max, et al. "Hybrd Systems for Predcton - A Case Study of Predctng Effluent Flow to a Sewage Plant." annes IEEE Computer Socety, 1995:261. [4] Tan, Null Jngwen, N. M. Gao, and N. Y. Xang. "Intellgent Optmzed Control of Flocculaton Process of Sewage Treatment Based on Support Vector Machne." Informaton Acquston, 2006 IEEE Internatonal Conference on IEEE, 2006: [5] Chen, W. C., N. B. Chang, and J. C. Chen. "Rough set-based hybrd fuzzy-neural controller desgn for ndustral wastewater treatment." Water Research 37.1(2003): [6] Wang, Wen Cheng, et al. "Soft Measurement Technque of Sewage Treatment Parameters Based on Wavelet Neural Networks." Appled Mechancs & Materals (2014): [7] Côté, P., et al. "Energy Consumpton of MBR for Muncpal Wastewater Treatment: Current Stuaton and Potental." Forest Engneerng (2013). [8] Wang, M. H., et al. "Mathematcal modelng of electrcal energy consumpton and heatng requrements by muncpal wastewater treatment plants." J. Envron. Sc.; (Unted States) 22:4.4(1979): [9] OLSSON G, ANDREWS J F. Dssolved oxygen control n the actvated sludge process. Wat. Sc. Tech., 1981,13(10): [10] CHARPENTIER J, FLORENTZ M, DAVID G. Oxdaton-reducton potental (ORP) regulaton: a way to optmze polluton removal and energy savngs n the low load actvated sludge process. Wat. Sc. Tech., 1987,19(Ro): [11] Thevenot, D. R. "Oxdaton Reducton Potental (Orp) Regulaton as a Way to Optmze Aeraton and C, N and P Removal - Expermental Bass and Varous Full-Scale Examples - Dscusson." Waterence & Technology 21.12(1989): [12] Ferrer, J., et al. "Energy savng n the aeraton process by fuzzy logc control." Water Scence & Technology 38.3(1998):

18 Ljuan Wang, PREDICTION OF SEWAGE QUALITY BASED ON FUSION OF BP NETWORKS [13] Bongards, M., A. Ebel, and T. Hlmer. "Predctve control of wastewater works by neural networks." Automaton Congress, Proceedngs. World 2004: [14]Yuzhao, F., et al. " Optmal parameters of actvated sludge system robust control method" Chna Water & Wastewater 19.3(2003): [15] Tzu-Y, Pa, et al. "Predctng the co-meltng temperatures of muncpal sold waste ncnerator fly ash and sewage sludge ash usng grey model and neural network.." Waste Manag Res 29.3(2011): [16] Huang, Y. W., and M. Q. Chen. "Artfcal neural network modelng of thn layer dryng behavor of muncpal sewage sludge." Measurement73(2015): [17] Jeong, Hyeong Seok, S. H. Lee, and H. S. Shn. "Feasblty of On-lne Measurement of Sewage Components Usng the UV Absorbance and the Neural Network." Envronmental Montorng & Assessment133.11(2007): [18] Sadegh, R., et al. "Use of support vector machnes (SVMs) to predct dstrbuton of an nvasve water fern Azolla flculodes (Lam.) n Anzal wetland, southern Caspan Sea, Iran." Ecologcal Modellng (2012): [19]Olsson G. [Sweden]. Wastewater Treatment Systems modelng,dagnoss and control.chemcal Industry Press,