Prediction analysis of a wastewater treatment system using a Bayesian network

Wastewater treatment is a complicated dynamic process, the effectiveness of which is affected by microbial, chemical, and physical factors. At present, predicting the effluent quality of wastewater treatment systems is difficult because of complex biological reaction mechanisms that vary with both time and the physical attributes of the system. Bayesian networks are useful for addressing uncertainties in artificial intelligence applications. Their powerful inferential capability and convenient decision support mechanisms provide flexibility and applicability for describing and analyzing factors affecting wastewater treatment systems. In this study, a Bayesian network-based approach for modeling and predicting a wastewater treatment system based on Modified Sequencing Batch Reactor (MSBR) was proposed. Using the presented approach, a Bayesian network model for MSBR can be constructed using experiential information and physical data relating to influent loads, operating conditions, and effluent concentrations. Additionally, MSBR prediction analysis, wherein effluent concentration can be predicted from influent loads and operational conditions, can be performed. This approach can be applied, with minimal modifications, to other types of wastewater treatment plants.

[1]  John Bromley,et al.  Integrated water resources management of overexploited hydrogeological systems using Object-Oriented Bayesian Networks , 2010, Environ. Model. Softw..

[2]  David J. Spiegelhalter,et al.  Bayesian networks for patient monitoring , 1992, Artif. Intell. Medicine.

[3]  Sakari Kuikka,et al.  Bene-Eia: A Bayesian Approach to Expert Judgment Elicitation with Case Studies on Climate Change Impacts on Surface Waters , 1997 .

[4]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[5]  Kenneth H. Reckhow,et al.  An evaluation of automated structure learning with Bayesian networks: An application to estuarine chlorophyll dynamics , 2011, Environ. Model. Softw..

[6]  Nir Friedman,et al.  Discretizing Continuous Attributes While Learning Bayesian Networks , 1996, ICML.

[7]  Finn Verner Jensen,et al.  Public participation modelling using Bayesian networks in management of groundwater contamination , 2007, Environ. Model. Softw..

[8]  Henry Tirri,et al.  B-Course: a Web service for Bayesian data analysis , 2001, Proceedings 13th IEEE International Conference on Tools with Artificial Intelligence. ICTAI 2001.

[9]  Martin Cote,et al.  Dynamic modelling of the activated sludge process: Improving prediction using neural networks , 1995 .

[10]  P. Spirtes,et al.  An Algorithm for Fast Recovery of Sparse Causal Graphs , 1991 .

[11]  Rafael Rumí,et al.  Bayesian networks in environmental modelling , 2011, Environ. Model. Softw..

[12]  David C. Wilkins,et al.  Bayesian Network Models for Generation of Crisis Management Training Scenarios , 1998, AAAI/IAAI.

[13]  Krist V. Gernaey,et al.  Activated sludge wastewater treatment plant modelling and simulation: state of the art , 2004, Environ. Model. Softw..

[14]  Kevin B. Korb,et al.  Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment , 2007, Environ. Model. Softw..

[15]  Satoru Simizu,et al.  Scientific and Technical Report , 2000 .

[16]  Mogens Henze,et al.  Activated Sludge Model No.2d, ASM2D , 1999 .

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

[18]  Gregory M. Provan,et al.  The Sensitivity of Belief Networks to Imprecise Probabilities: An Experimental Investigation , 1996, Artif. Intell..

[19]  W. J. Walley,et al.  Rule-based versus probabilistic approaches to the diagnosis of faults in wastewater treatment processes , 1996, Artif. Intell. Eng..

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

[21]  Andrea Castelletti,et al.  Bayesian Networks and participatory modelling in water resource management , 2007, Environ. Model. Softw..

[22]  Olli Varis,et al.  Belief networks for modelling and assessment of environmental change , 1995 .

[23]  D. Heckerman,et al.  ,81. Introduction , 2022 .

[24]  Serena H. Chen,et al.  Good practice in Bayesian network modelling , 2012, Environ. Model. Softw..

[25]  Judea Pearl,et al.  Bayesian Networks , 1998, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[26]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[27]  Finn Verner Jensen,et al.  Introduction to Bayesian Networks , 2008, Innovations in Bayesian Networks.

[28]  H Lee,et al.  Application of remote monitoring and automatic control system using neural network for small wastewater treatment plants in Korea. , 2005, Water science and technology : a journal of the International Association on Water Pollution Research.

[29]  Mark E. Borsuk,et al.  Comparison of Estuarine Water Quality Models for Total Maximum Daily Load Development in Neuse River Estuary , 2003 .

[30]  J. Bromley,et al.  The use of Hugin® to develop Bayesian networks as an aid to integrated water resource planning , 2005, Environ. Model. Softw..

[31]  Mark E. Borsuk,et al.  A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis , 2004 .

[32]  Kevin Murphy,et al.  Bayes net toolbox for Matlab , 1999 .

[33]  Laura Uusitalo,et al.  Advantages and challenges of Bayesian networks in environmental modelling , 2007 .

[34]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[35]  Mark E. Borsuk,et al.  Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian probability network , 2006 .

[36]  Jukka Saarinen,et al.  Target identification with Bayesian networks , 2000, SPIE Defense + Commercial Sensing.

[37]  Olli Varis,et al.  Policy Analysis for the Tonle Sap Lake, Cambodia: A Bayesian Network Model Approach , 2006 .

[38]  Brian S. G. E. Sahely,et al.  Diagnosing Upsets in Anaerobic Wastewater Treatment Using Bayesian Belief Networks , 2001 .

[39]  Gary R. Weckman,et al.  Modeling net ecosystem metabolism with an artificial neural network and Bayesian belief network , 2011, Environ. Model. Softw..