Introducing Ensemble Methods to Predict the Performance of Waste Water Treatment Plants (WWTP)

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1 Introducng Ensemble Methods to Predct the Performance of Waste Water Treatment Plants (WWTP) Bharat B. Gulyan and Arsha Fathma Abstract Optmzaton and control of waste water treatment plants (WWTP) s an ongong effort to make the process more effcent and cost-effectve. As found n lterature, data mnng models such as neural networks have been appled to smulate and model varous aspects of the plant such as performance, qualty parameters and process parameters. In ths paper, we ntroduce baggng model, an ensemble data mnng model, to predct the performance of the WWTP. Ensemble models have been shown to stablze the base classfer used and avod overfttng the data. Baggng was used to predct the performance of ndvdual unts (prmary and secondary ) and the global plant performance. The predcted performance of ndvdual unts was also used as nputs to predct the global performance thereby enablng good process control va predctve data models. Upon applcaton to the WWTP dataset, t was found that baggng models perform at par or even better than A or SVM for the predcton and hence are sutable models that can be mplemented for process control of the water treatment plants. Index Terms Waste water treatment plant (WWTP), ensemble models, baggng, process control. I. ITRODUCTIO Wth the ever-ncreasng demand for water, research efforts are beng made to enhance the water treatment process and desgns to enable cost-effectve and sustanable technology development for the future. One of the focus areas n water-related research s to cut down costs va the optmzaton of the waste water treatment plant (WWTP). Studes have been conducted on better operatonal control and mantenance of the water treatment plants usng ntellgent process control methods ncludng neural networks. Data mnng methods such as neural networks offer the advantage of smulaton and modellng of complex and mult-varable dependent behavors such as those found n water treatment. Some of these studes have focused on the use of Artfcal eural etworks (A) for coagulant dosage control [1], [2], smulaton and modellng of fltraton and osmotc processes [3], [4], and also model for UV-dsnfecton control [5]. In addton to modellng and smulaton of the water treatment plants, few studes have also predcted the performance of these plants [4], by the applcaton of the data mnng algorthms based on the effluent qualty parameters such as bochemcal oxygen demand (BOD), chemcal oxygen demand (COD) and Manuscrpt receved August 25, 2016; revsed February 12, Bharat B. Gulyan s wth the Department of Chemcal Engneerng at BITS Plan, Duba Campus, Academc Cty, Duba , UAE (e-mal: gulyanbb@gmal.com). Arsha Fathma s wth anolabs, Alfasal Unversty, Saud Araba (e-mal: arshafathma92@gmal.com). suspended solds (SS). Ths paper llustrates the use of baggng, an ensemble data mnng method, to develop a predcton model for the performance of WWTP and compares the stablty of such models wth A. II. LITERATURE REVIEW Data mnng methods have been employed to predct the performance of WWTP as a means to have enhanced process control and effcent operaton of the plant. The non-lnear complex behavor of these plants have also been captured effectvely usng data mnng methods [6]. Accordng to the lterature, mostly A were used to modelng and smulaton of varous aspects of the water treatment plants. One such study predcted the long-term membrane foulng n order to capture the effects of nfluent water qualty changes thereby provdng for better operatonal control of the process [7]. Studes have also used A as ntellgent controls to model and control anaerobc dgesters as well as control the chlornaton n dsnfecton process [8]. Besdes A, some other data mnng methods such as fuzzy networks have been used n conjuncton wth A to develop robust water treatment models. An example of such studes s the fuzzy neural network that was developed to control the coagulant addton process for wastewater from paper mll. Ths model aded the real-tme control and optmzaton of coagulant dosage wth excellent effcency as the error was almost zero [9]. Smlar studes have been reported for coagulant dosage wth good performance of the A [2]. Another focus of neural network models has been to predct the performance of WWTP based on the qualty of the nfluent and effluent water. These A models have been developed usng algorthms such as back-propagaton, Levenberg Marquardt algorthm and fuzzy models. A recent study used A model to predct the WWTP performance n terms of BOD, COD and total suspended solds (TSS). The plant modeled was a sequencng batch reactor and t was found that the A model was able to predct the performance wth a correlaton coeffcent of 0.90, hence establshng ts potental to smulate the non-lnear behavor of the WWTP by data mnng models [6]. Another recent study used feed-forward back propagaton A to model the reverse osmoss unts n a wastewater treatment plant. The model was based on a small dataset and descrbed the permeate flow profles for the reverse osmoss (RO) unts wth a hgh correlaton coeffcents up to 0.99 wth mnmum error [3]. Other data mnng technques that were used to assess the WWTP performance wth regards to organc matter removal do: /jesd

2 nclude self-organzng maps (SOM), prncpal component analyss (PCA), parallel factor analyss (PARAFAC), partal least squares (PLS) and regresson technques whch were also used n combnaton wth A. It was shown that the best results were gven by the combned models namely PARAFAC/PLS and SOM/A combnaton wth a correlaton coeffcent of 0.93 for both models and values less than 0.6 [10]. In the present paper, we have used baggng that has been employed n other felds such as bonformatcs but has not been wdely studed for WWTP performance modelng. The ensemble methods such as baggng have shown to work well wth small data and also avod the problem of over-fttng the data by averagng the results. These models work by combnng multple base classfers wth multple startng ponts and averagng ther predctons thereby reducng the rsk of choosng wrong classfer. As such ensemble methods wll help to stablze base classfers beng used [11]. Based on ths mert of ensemble methods, baggng models wth A and Support Vector Machnes (SVM) as base classfers have been assessed for the predcton of WWTP performance. A. Dataset Processng III. METHODOLOGY The waste water treatment dataset was obtaned from UCI Machne Learnng Repostory [12] whch was obtaned va daly measurement usng sensors for the prmary and secondary s n the plant for The dataset had a total of 38 attrbutes n addton to the date of measurement. For our purpose, the dates were converted to an attrbute called number of days n operaton wth the earlest date consdered as Day 1 (.e. 1 Jan 1990 as Day 1). Ths was done to model the performance wth respect to tme n days to account for measurements that were mssng n the tme seres. Then dataset reducton was done to deal wth any mssng values for the attrbutes. All rows wth any mssng data were removed from the dataset thereby resultng n a dataset wth 380 nstances. The lst of attrbutes ncludes ph, conductvty, Bochemcal Oxygen Demand (BOD), Chemcal Oxygen Demand (COD), suspended solds (SS), sedments, volatle suspended solds, local performance of the s based on nput BOD/COD/SS and the global performance of the plant based on the nput BOD/COD/SS. As the BOD and COD measurements are tme-consumng and costly, BOD and COD related attrbutes were removed f they ddn t affect the model performance. The nput attrbutes znc n flow to plant (attrbute #2) was not consdered at all for any models. The nput BOD and COD to plant (attrbute #4 and 5) were not consdered for any of the models except for the global performance models. The output attrbutes BOD and COD to the plant (attrbute #24 & 25) were also not consdered for any of the models except for the secondary performance models for they were shown to drastcally mprove the model performance as shown n the results secton. B. Baggng Model for Data Mnng An ensemble model, ncludng baggng, random forests and boostng, smultaneously trans multple base classfers and averages ther results to gve the fnal output for predcton or classfcaton (dependng upon the applcaton). Bootstrap Aggregatng or baggng s an ensemble method that selects nstances by usng bootstrap samplng for gettng the tranng and testng sets from feed data. Bootstrap samplng nvolves samplng n nstances n tmes wth replacement. In ths way, all the data wll be used for tranng and valdatng the data gvng a generalzed model thereby avodng the ssues of errors and overfttng [13]. Hence, ensemble models can be used wth A or SVM as base classfer for the predcton of WWTP performance as hghlghted n ths paper thereby enablng better process control. C. Model Performance Measures The performance measures used n the present study were Root Mean Squared Error (), Mean Absolute Error (), Relatve Absolute Error () and correlaton coeffcent. 1) The s calculated by the followng formula: = ( p a ) 1 where p s the predcted value for the th nstance, a s the actual value for the th nstance and s the total number of nstances n the gven dataset. The smaller the, the better the performance of the model [14]. The tends to have a bas towards larger events [15], so other performance measures need to be evaluated for model selecton. 2) Mean Absolute Error () s the average of the absolute values of the dfference between the predcted and actual values. It reduces the bas towards large events unlke. The equaton for [15] s: 1 = a p 1 (1) (2) where p s the predcted value for the th nstance, a s the actual value for the th nstance and s the total number of nstances n the gven data set. 3) Relatve Absolute Error (): It s the relatve equvalent of [15] and s gven by: 1 a p = 1 a (3) where p s the predcted value for the th nstance, a s the actual value for the th nstance and s the total number of nstances n the gven data set. 4) (R 2 ): It measures the degree of lnear relaton between two varables. A correlaton coeffcent of 0 mples no correlaton between varables whle a value of 1 mples perfect correlaton. The correlaton coeffcent between actual and predcted varables enables us to get the accuracy of the predcton model. Ths measure s calculated by [14]: 502

3 pa p a R 2 S / S S (4) where a and p are the averages respectvely, and S pa 1 p pa a 1 1 Sp 1 Sa 1 p p 1 a a IV. RESULTS The data mnng models were developed usng the open source software Wakato Envronment for Knowledge Analyss (Weka) [16]. The models were developed wth 10-fold cross valdaton and default parameters as defned n Weka for A (multlayer perceptron), SVM, Baggng wth A and. The only parameter changed was for kernel type n SVM. The kernel was changed from polykernel (default) to normalzed polykernel as t was found to enhance performance n all models except for global model performance. Indvdual predcton models were bult from these data mnng algorthms to predct the prmary and secondary performance and the global plant performance. The detals on the attrbutes used for nput and output are gven n the appendxes. The results obtaned for the above mentoned models are dscussed below. A. Predcton of Prmary Settler Performance Based on Input BOD The performance predctons of baggng wth A were found to be better than that of A. Though the correlaton coeffcents of both these models were same, the and values were lower for baggng wth A, showng that baggng stablzes the A, hence lowerng the errors. The SVM based models also have hgher accuracy (>95%) but ther error was hgher than those of A models. The results are gven n Table I. TABLE I: MODEL COMPARISO FOR PREDICTIO OF PRIMARY SETTLER PERFORMACE BASED O IPUT BOD (ATTRIBUTE # 30) Internatonal Journal of Envronmental Scence and Development, Vol. 8, o. 7, July A SVM (ormalzed Baggng wth A (10 & B. Predcton of Prmary Settler Performance Based on Input SS The performance predctons of baggng wth A were found to be better than that of A as shown n Table II. The SVM based models had an acceptable accuracy (correlaton coeffcent of 0.90) but ther error was hgher than that of A models and hence they can t be used for process control. TABLE II: MODEL COMPARISO FOR PREDICTIO OF PRIMARY SETTLER PERFORMACE BASED O IPUT SS (ATTRIBUTE # 31) A SVM (ormalzed Baggng wth A (10 & C. Predcton of Secondary Settler Performance Based on Input BOD and COD The secondary performance was best predcted by A model though baggng model also showed smlar performance. However, baggng wth SVM dd not perform as well as SVM even though ts s smlar to that of A. TABLE III: MODEL COMPARISO FOR PREDICTIO OF SECODARY SETTLER PERFORMACE BASED O BOD (ATTRIBUTE # 33) WITHOUT USIG IPUT ATTRIBUTES (#24 & 25) A SVM (ormalzed Baggng wth A (10 & TABLE IV: MODEL COMPARISO FOR PREDICTIO OF SECODARY SETTLER PERFORMACE BASED O BOD (ATTRIBUTE # 33 WITH MODEL USIG IPUT ATTRIBUTES (#24 & 25) A SVM (ormalzed Baggng wth A (10 & It was also observed (as seen from Tables III - VI) that the predcton of the secondary performance drastcally mproved wth the ncluson of the output BOD and COD (attrbutes # 24 & 25). As the dataset consdered was based on 2 s, these results confrm the strong dependence of the secondary performance on the effluent qualty. 503

4 TABLE V: MODEL COMPARISO FOR PREDICTIO OF SECODARY SETTLER PERFORMACE BASED O COD (ATTRIBUTE # 34) WITHOUT USIG IPUT ATTRIBUTES (#24 & 25) A SVM (ormalzed Baggng wth A (10 & TABLE VI: MODEL COMPARISO FOR PREDICTIO OF SECODARY SETTLER PERFORMACE BASED O COD (ATTRIBUTE # 34) WITH MODEL USIG IPUT ATTRIBUTES (#24 & 25) A SVM (ormalzed Baggng wth A (10 & D. Predcton of Global Performance The three global performance attrbutes from the dataset were based on nput BOD, COD, and SS, respectvely. These performance attrbutes were successfully predcted by A and baggng wth A models as shown n Tables VII - IX. TABLE VII: MODEL COMPARISOS FOR PREDICTIO OF GLOBAL PERFORMACE BASED O IPUT BOD (ATTRIBUTE # 35) A SVM (normalzed kernel) SVM ( Baggng wth A ( wth polykernel ( TABLE VIII: MODEL COMPARISOS FOR PREDICTIO OF GLOBAL PERFORMACE BASED O IPUT COD (ATTRIBUTE # 36) A SVM (normalzed kernel) SVM ( Baggng wth A ( wth polykernel ( The SVM based models also showed smlar correlaton coeffcents lke A but ther values were much hgher thereby ndcatng lower accuracy. For SVM model, the polykernel performed better than the normalzed kernel thereby showng a better ft of global performance data wth polynomal functon. TABLE IX: MODEL COMPARISOS FOR PREDICTIO OF GLOBAL PERFORMACE BASED O IPUT SS (ATTRIBUTE # 37) A SVM (normalzed kernel) SVM ( Baggng wth A ( wth polykernel ( E. Predcton of Global Performance Usng Prevously Predcted Indvdual Performance Values Besdes ndvdual predcton models, models were also bult usng prevously predcted attrbutes as nputs for the predcton of the global performance of the plant. Buldng sequental predctors by usng prevously predcted performance of the ndvdual s as nputs for the global performance predcton, we can also develop feedback control on the nput streams. For example, f the predcted performance of the secondary based on nput BOD/COD values s good but the correspondng predcted global performance s low, then controller can adjust the process parameters of the s accordngly. For buldng these models, as ngle algorthm was used throughout the sequental predctors. For example, to predct the global performance based on BOD (attrbute #36) usng A, the correspondng predcted performance values were obtaned from the ndvdual A models. For secondary, best predcted values for A (from Table IV) were used. Accordng to the results as gven n Table X-XII, t was observed that usng predcted performance values dd lower the predcton performance, however A and baggng wth A dd gve acceptable results wth an average correlaton coeffcent of 0.95 for the global performance parameters. TABLE X: MODEL COMPARISOS FOR PREDICTIO OF GLOBAL PERFORMACE BASED O IPUT BOD (ATTRIBUTE # 35) USIG PREDICTED PERFORMACE DATA OF SETTLERS AS IPUTS A SVM ( Baggng wth A ( (polykernel & The SVM and baggng wth SVM models were only able to gve a correlaton coeffcent of 0.82 and 0.75 respectvely for global performance based on BOD, but were able to gve hgher correlaton coeffcents for global performance based on COD and SS. Ths dfference can be explaned based on results from Tables I and IV, whch show that the predcted outputs based on BOD had larger values. Ths error n predcton of performance n ndvdual s was carred forward nto the global performance predcton models 504

5 thereby affectng the accuracy of the models. TABLE XI: MODEL COMPARISOS FOR PREDICTIO OF GLOBAL PERFORMACE BASED O IPUT COD (ATTRIBUTE # 36) USIG PREDICTED PERFORMACE DATA OF SETTLERS AS IPUTS A SVM ( Baggng wth A ( (polykernel & TABLE XII: MODEL COMPARISOS FOR PREDICTIO OF GLOBAL PERFORMACE BASED O IPUT SS (ATTRIBUTE # 36) USIG PREDICTED PERFORMACE DATA OF SETTLERS AS IPUTS A SVM ( Baggng wth A ( (polykernel & V. COCLUSIOS Data mnng algorthms such as A and baggng offer the capablty for better process control usng predcted performance based on nput qualty parameters that can be easly measured. Ths provdes for a cost-effectve, tmely and effcent way to operate and mantan the WWTP. In ths paper we have ntroduced baggng, an ensemble model, for accurate predctons of the WWTP performance whch has shown to perform at par wth neural networks whle avodng overfttng. Global performance predcton models based on prevously predcted ndvdual performance values were also developed. These models based on A and baggng wth A also had acceptable predcton capabltes whch wll enable for enhanced feedback control of the WWTP. A seres of predctve models based on A or Baggng wth A have shown to predct the plant performance satsfactorly thereby provdng a model for feedback control based on predcted performance. Future optmzaton studes can be done on usng a combnaton of data mnng models to predct the ntermedate output/performance parameters and also develop further models based on these ntermedate results for global output performance predcton. APPEDIX The followng appendxes gve the detals on the nput and output attrbutes used for developng the models. Appendx A gves the attrbute lst whle Appendx B gves attrbutes used for specfc models. Attrbute umber APPEDIX A: ATTRIBUTE DETAILS FROM UCI REPOSITORY Attrbute ame 1 Input flow to plant Input Input or for Model 2 Input Znc to plant Input 3 Input ph to plant Input 4 Input BOD to plant Input 5 Input COD to plant Input 6 Input SS to plant Input 7 Input volatle SS to plant Input 8 Input sedments to plant Input 9 Input conductvty to plant Input 10 Input ph to prmary Input 11 Input BOD to prmary Input 12 Input SS to prmary Input 13 Input volatle SS to prmary Input 14 Input sedments to prmary Input 15 Input conductvty to prmary Input 16 Input ph to secondary Input 17 Input BOD to secondary Input 18 Input COD to secondary Input 19 Input SS to secondary Input 20 Input volatle SS to secondary Input 21 Input sedments to secondary Input 22 Input conductvty to secondary Input 23 ph of plant Input 24 BOD of plant Input 25 COD of plant of plant Input 26 SS Input 27 volatle SS Input 28 sedments Input 29 conductvty Input Performance based on nput BOD n prmary Performance based on nput SS to prmary Performance based on nput sedments to prmary Performance based on nput BOD to secondary Performance based on nput COD to secondary 35 Global performance based on nput BOD 36 Global performance based on nput COD 37 Global performance based on nput SS 38 Global performance based on nput sedments APPEDIX B: IPUT AD OUTPUT ATTRIBUTE DETAILS FOR MODELS Model ame Input Attrbute # Attrbute # Predcton of prmary performance based on nput BOD Predcton of prmary performance based on nput SS Predcton of secondary performance based on nput BOD 1, 3, 6, 7, 8, 9, 10, 11, 12, 29 and number of days n operaton 1, 3, 6, 7, 8, 9, 10, 11, 12, 29 and number of days n operaton Wthout O/P BOD & COD: 1, 3, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 28, 29 and number of days n operaton & 34 (based on BOD & COD respectvely) 505

6 Model ame Input Attrbute # Attrbute # Wth O/P BOD & COD: 1, 3, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 and number days n operaton. Predcton of global performance Predcton of global performance usng prevously predcted ndvdual performance values Based on nput BOD: 1, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 29, 30, 33 and number of days n operaton Based on nput COD: 1, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 29, 30, 34 and number of days n operaton Based on nput SS: 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 28, 29, 31 and # days n operaton. Based on nput BOD - Ths was carred out wth nput parameters # 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 28, 29 and # days n operaton. Predcted parameters 30 and 33. COD based perf- Ths was carred out wth nput parameters # 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 28, 29 and # days n operaton. Predcted parameters 30 and 34. SS based perf- Ths was carred out wth nput parameters # 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 28, 29 and # days n operaton. Predcted parameter , 36 & 37 (based on BOD, COD and SS respectvely) 35, 36 & 37 (based on BOD, COD and SS respectvely) [3] A. Salgado-Reyna, E. Soto-Regalado, R. Gómez-González, F. J. Cerno-Córdova, R. B. García-Reyes, M. T. Garza-González, and M. M. Alcalá-Rodríguez, Artfcal neural networks for modelng the reverse osmoss unt n a wastewater plot treatment plant, Desalnaton and Water Treatment, vol. 53, no. 5, pp , Jan [4] Y. Zhao, J. S. Taylor, and S. Chellam, Predctng RO/F water qualty by modfed soluton dffuson model and artfcal neural networks, Journal of membrane scence, vol. 263, no. 1, pp , [5] C.-H. 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A. Perce, B. Robson, R. Seppelt, A. A. Vonov, B. D. Fath, and V. Andreassan, Charactersng performance of envronmental models, Envronmental Modellng & Software, vol. 40, pp. 1 20, Feb [16] M. Hall, E. Frank, G. Holmes, B. Pfahrnger, P. Reutemann, and I. H. Wtten, The WEKA data mnng software: An update, SIGKDD Explorer ewsletters, vol. 11, no. 1, pp , ov Bharat B. Gulyan had hs bachelor, masters and doctoral degrees from Unversty of Roorkee, Inda (now IIT Roorkee) n the feld of chemcal engneerng. He has more than 20 years of research experence ad had publshed and presented at varous conferences more than 30 research papers. He s currently assocate professor at Brla Insttute of Technology and Scence at ther Duba campus. REFERECES [1] S. Krt and J. Smta, Artfcal neural network modellng of shyamala water works, Bhopal MP, Inda: A green approach towards the optmzaton of water treatment process, Research Journal of Recent Scences, vol. 2, pp , [2] G.-D. Wu and S.-L. Lo, Predctng real-tme coagulant dosage n water treatment by artfcal neural networks and adaptve network-based fuzzy nference system, Engneerng Applcatons of Artfcal Intellgence, vol. 21, no. 8, pp , Dec journals. Arsha Fathma has receved her bachelors n chemcal engneerng and computer scence from BPDC, UAE n 2014 and her masters n chemcal engneerng wth specalzaton n product development from UC Berkeley, USA n Currently, she s pursung research at the anolabs, Alfasal Unversty under Dr. Edreese Alsharaeh. Actvely nvolved n research, she has publshed 5 papers tll date n conferences and 506