Sequential modelling of a full-scale wastewater treatment plant using an artificial neural network

This work proposes a sequential modelling approach using an artificial neural network (ANN) to develop four independent multivariate models that are able to predict the dynamics of biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solid (SS), and total nitrogen (TN) removal in a wastewater treatment plant (WWTP). Suitable structures of ANN models were automatically and conveniently optimized by a genetic algorithm rather than the conventional trial and error method. The sequential modelling approach, which is composed of two parts, a process disturbance estimator and a process behaviour predictor, was also presented to develop multivariate dynamic models. In particular, the process disturbance estimator was first employed to estimate the influent quality. The process behaviour predictor then sequentially predicted the effluent quality based on the estimated influent quality from the process disturbance estimator with other process variables. The efficiencies of the developed ANN models with a sequential modelling approach were demonstrated with a practical application using a data set collected from a full-scale WWTP during 2 years. The results show that the ANN with the sequential modelling approach successfully developed multivariate dynamic models of BOD, COD, SS, and TN removal with satisfactory estimation and prediction capability. Thus, the proposed method could be used as a powerful tool for the prediction of complex and nonlinear WWTP performance.

[1]  Muttucumaru Sivakumar,et al.  Prediction of urban stormwater quality using artificial neural networks , 2009, Environ. Model. Softw..

[2]  Mohamed Meselhy Eltoukhy,et al.  The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process. , 2010, Journal of hazardous materials.

[3]  Maged M. Hamed,et al.  Prediction of wastewater treatment plant performance using artificial neural networks , 2004, Environ. Model. Softw..

[4]  Dünyamin Güçlü,et al.  Artificial neural network modelling of a large-scale wastewater treatment plant operation , 2010, Bioprocess and biosystems engineering.

[5]  K. P. Sudheer,et al.  Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions , 2010, Environ. Model. Softw..

[6]  C. Y. Lin,et al.  Grey and neural network prediction of suspended solids and chemical oxygen demand in hospital wastewater treatment plant effluent , 2007, Comput. Chem. Eng..

[7]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  P. Khanna,et al.  Genetic algorithm for optimization of water distribution systems , 1999, Environ. Model. Softw..

[10]  A. Spagni,et al.  Soft sensors for control of nitrogen and phosphorus removal from wastewaters by neural networks. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.

[11]  B. Ayati,et al.  Prediction of moving bed biofilm reactor (MBBR) performance for the treatment of aniline using artificial neural networks (ANN). , 2010, Journal of hazardous materials.

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  S C Wang,et al.  Improving neural network prediction of effluent from biological wastewater treatment plant of industrial park using fuzzy learning approach , 2009, Bioprocess and biosystems engineering.

[14]  Yoon-Seok Hong,et al.  A Genetic Adapted Neural Network Analysis of Performance of the Nutrient Removal Plant at Rotorua , 1998 .

[15]  Yoon-Seok Hong,et al.  Evolutionary self-organising modelling of a municipal wastewater treatment plant. , 2003, Water research.

[16]  Özer Çinar,et al.  New tool for evaluation of performance of wastewater treatment plant: Artificial neural network , 2005 .

[17]  Iván Machón,et al.  Simulation of a coke wastewater nitrification process using a feed-forward neuronal net , 2007, Environ. Model. Softw..

[18]  Yoon-Seok Timothy Hong,et al.  Evolutionary Multivariate Dynamic Process Model Induction for a Biological Nutrient Removal Process , 2007 .

[19]  G. Ibarra-Berastegi,et al.  Neural networks as a tool for control and management of a biological reactor for treating hydrogen sulphide , 2006, Bioprocess and biosystems engineering.

[20]  Vinod Tare,et al.  Application of neural network for simulation of upflow anaerobic sludge blanket (UASB) reactor performance. , 2002, Biotechnology and bioengineering.

[21]  S. Manikandan,et al.  Prediction of biosorption efficiency for the removal of copper(II) using artificial neural networks. , 2008, Journal of hazardous materials.

[22]  S Kim,et al.  Genetic algorithms for the application of Activated Sludge Model No. 1. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.

[23]  Dae Sung Lee,et al.  Monitoring of sequencing batch reactor for nitrogen and phosphorus removal using neural networks , 2007 .

[24]  Aysegul Aksoy,et al.  Modeling of the activated sludge process by using artificial neural networks with automated architecture screening , 2008, Comput. Chem. Eng..

[25]  Guohe Huang,et al.  A neural network predictive control system for paper mill wastewater treatment , 2003 .

[26]  Yoon-Seok Timothy Hong,et al.  Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis. , 2003, Water research.

[27]  R. Braun,et al.  Advanced controlling of anaerobic digestion by means of hierarchical neural networks. , 2002, Water research.

[28]  Hang-sik Shin,et al.  Sequential modeling of fecal coliform removals in a full-scale activated-sludge wastewater treatment plant using an evolutionary process model induction system. , 2009, Water research.

[29]  Mogens Henze,et al.  Activated sludge models ASM1, ASM2, ASM2d and ASM3 , 2015 .

[30]  Thomas J. McAvoy,et al.  Approaches to modeling nutrient dynamics: ASM2, simplified model and neural nets , 1999 .

[31]  Dirk Weuster-Botz,et al.  Genetic algorithm for multi-objective experimental optimization , 2006, Bioprocess and biosystems engineering.

[32]  In-Beum Lee,et al.  Nonlinear modeling and adaptive monitoring with fuzzy and multivariate statistical methods in biological wastewater treatment plants. , 2003, Journal of biotechnology.

[33]  Quan J. Wang,et al.  Using genetic algorithms to optimise model parameters , 1997 .

[34]  Fang Fang,et al.  An integrated dynamic model for simulating a full-scale municipal wastewater treatment plant under fluctuating conditions , 2010 .

[35]  W. Gujer,et al.  Activated sludge model No. 3 , 1995 .

[36]  Kun Soo Chang,et al.  Hybrid neural network modeling of a full-scale industrial wastewater treatment process. , 2002, Biotechnology and bioengineering.