Prediction of effluent quality of an anaerobic treatment plant under unsteady state through ANFIS modeling with on-line input variables

Abstract A neural fuzzy model based on adaptive network-based fuzzy inference system (ANFIS) was proposed in terms of on-line input variables CH 4 %, Q gas , Q anarecycle , Q inf-bypass and Q inf to estimate the effluent chemical oxygen demand, COD eff , of a real scale unsteady anaerobic wastewater treatment plant of a sugar factory. Two new variables were added into the input variables matrix of the model; phase vectors of the plant operation and the history of effluent COD values in order to increase the fitness of simulated results. ANFIS was able to estimate the water quality discharge parameter with success for the case when only limited on-line variables were available without requiring the measurement of inlet COD . Acceptable correlation coefficient (0.8354) and root mean square error (0.1247) were found between estimated and measured values of the system output variable, effluent COD , in the case of excluding inlet volumetric flow rate of the wastewater treatment plant from the on-line input variable matrix. The developed ANFIS model may be integrated into an advanced control system for the anaerobic treatment plant using different control strategies with further work.

[1]  Joo-Hwa Tay,et al.  Neural fuzzy modeling of anaerobic biological wastewater treatment systems , 1999 .

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

[3]  Taizo Hanai,et al.  Construction of COD Simulation Model for Activated Sludge Process by Recursive Fuzzy Neural Network , 2001 .

[4]  Chuen-Tsai Sun,et al.  Functional equivalence between radial basis function networks and fuzzy inference systems , 1993, IEEE Trans. Neural Networks.

[5]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[6]  Lluís A. Belanche Muñoz,et al.  Towards a model of input-output behaviour os wastewater treatment plants using soft computing techniques , 1999, Environ. Model. Softw..

[7]  Sever Arslan,et al.  Evaluation of Input Variables in Adaptive-Network-Based Fuzzy Inference System Modeling for an Anaerobic Wastewater Treatment Plant under Unsteady State , 2007 .

[8]  A. E. Greenberg,et al.  Standard methods for the examination of water and wastewater : supplement to the sixteenth edition , 1988 .

[9]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[10]  J P Steyer,et al.  Hybrid modelling of anaerobic wastewater treatment processes. , 2001, Water science and technology : a journal of the International Association on Water Pollution Research.

[11]  S. J. Wilcox,et al.  A comparison of the ability of black box and neural network models of ARX structure to represent a fluidized bed anaerobic digestion process , 1999 .

[12]  Stefano Marsili-Libelli,et al.  Fuzzy control of disturbances in a wastewater treatment process , 1997 .

[13]  Rudolf Braun,et al.  Start‐up and recovery of a biogas‐reactor using a hierarchical neural network‐based control tool , 2003 .

[14]  P. Weiland,et al.  The Start-Up, Operation and Monitoring of High-Rate Anaerobic Treatment Systems: Discusser's Report , 1991 .

[15]  Joo-Hwa Tay,et al.  A fast predicting neural fuzzy model for high-rate anaerobic wastewater treatment systems , 2000 .

[16]  Monique Polit,et al.  Prediction of parameters characterizing the state of a pollution removal biologic process , 2005, Eng. Appl. Artif. Intell..

[17]  Sungshin Kim,et al.  SOFTWARE SENSOR USING PNN MODEL AND KNOWLEDGE – BASE FOR SEQUENCING BATCH REACTOR , 2004 .

[18]  P Gras,et al.  Evaluation of a four year experience with a fully instrumented anaerobic digestion process. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.

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

[20]  Bernardino Arcay,et al.  An intelligent system for distributed control of an anaerobic wastewater treatment process , 2000 .

[21]  J P Steyer,et al.  Software sensor design for COD estimation in an anaerobic fluidized bed reactor. , 2001, Water science and technology : a journal of the International Association on Water Pollution Research.

[22]  Makram T. Suidan,et al.  Anaerobic Treatment Kinetics: Discussers’ Report , 1991 .