Simulation of a paper mill wastewater treatment using a fuzzy neural network

This paper presents a fuzzy neural network predictive control scheme for studying the coagulation process of wastewater treatment in a paper mill. An adaptive fuzzy neural network is employed to model the nonlinear relationships between the removal rate of pollutants and the chemical dosages, in order to adapt the system to a variety of operating conditions and acquire a more flexible learning ability. The system includes a fuzzy neural network emulator of the reaction process, a fuzzy neural network controller, and an optimization procedure based on a performance function that is used to identify desired control inputs. The gradient descent algorithm method is used to realize the optimization procedure. The results indicate that reasonable forecasting and control performances have been achieved through the developed system.

[1]  Jean-Philippe Steyer,et al.  Hybrid fuzzy neural network for diagnosis - application to the anaerobic treatment of wine distillery wastewater in a fluidized bed reactor , 1997 .

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

[3]  Robert Babuska,et al.  Fuzzy control of aeration in an activated sludge wastewater treatment plant: design, simulation and evaluation , 1999 .

[4]  Pam Haley,et al.  Generalized predictive control for active flutter suppression , 1997 .

[5]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Ma Yongwen,et al.  Study on Hydrolytic-Acidification(HA) during the Treatment of Wastewater in Recycled Fibers base Mills , 2006 .

[7]  J. Comas,et al.  Energy Saving in a Wastewater Treatment Process: an Application of Fuzzy Logic Control , 2005, Environmental technology.

[8]  Wei-Ling Chiang,et al.  Construction of an on-line fuzzy controller for the dynamic activated sludge process , 1994 .

[9]  N. B. Chang,et al.  Assessing wastewater reclamation potential by neural network model , 2003 .

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

[11]  Ni-Bin Chang,et al.  Mining the fuzzy control rules of aeration in a Submerged Biofilm Wastewater Treatment Process , 2007, Eng. Appl. Artif. Intell..

[12]  Stephen J. Stanley,et al.  Real-Time Water Treatment Process Control with Artificial Neural Networks , 1999 .

[13]  Manel Poch,et al.  Fuzzy model and decision of COD control for an activated sludge process , 1998, Fuzzy Sets Syst..

[14]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[15]  M. Sugeno,et al.  Derivation of Fuzzy Control Rules from Human Operator's Control Actions , 1983 .

[16]  Y. Takahashi,et al.  Adaptive predictive control of nonlinear time-varying systems using neural networks , 1993, IEEE International Conference on Neural Networks.

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

[18]  M Bongards,et al.  Improving the efficiency of a wastewater treatment plant by fuzzy control and neural network. , 2001, Water science and technology : a journal of the International Association on Water Pollution Research.

[19]  Sunwon Park,et al.  Neural model predictive control for nonlinear chemical processes , 1993 .

[20]  Antonio Delgado,et al.  State detection and control of overloads in the anaerobic wastewater treatment using fuzzy logic. , 2002, Water research.

[21]  Monique Polit,et al.  Control of sludge height in a secondary settler using fuzzy algorithms , 2006, Comput. Chem. Eng..

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

[23]  M A Rodrigo,et al.  Nonlinear control of an activated sludge aeration process: use of fuzzy techniques for tuning PID controllers. , 1999, ISA transactions.

[24]  José Ferrer,et al.  Energy saving in the aeration process by fuzzy logic control , 1998 .

[25]  A. J. Morris,et al.  Artificial neural network based control , 1991 .

[26]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[27]  Manuel J. Rodríguez,et al.  Chlorcast©: a methodology for developing decision-making tools for chlorine disinfection control , 2001, Environ. Model. Softw..

[28]  Lluís A. Belanche Muñoz,et al.  Prediction of the bulking phenomenon in wastewater treatment plants , 2000, Artif. Intell. Eng..