Mining the fuzzy control rules of aeration in a Submerged Biofilm Wastewater Treatment Process

Authors:
Jeng-Chung Chen;Ni-Bin Chang
Affiliations:
Department of Environmental Engineering and Science, Fooyin University, Kaohsiung, Taiwan 831, ROC;Department of Civil and Environmental Engineering, University of Central Florida, Orlando, FL 32816, USA
Venue:
Engineering Applications of Artificial Intelligence
Year:
2007

Citing 3
Cited 3

Fuzzy model and decision of COD control for an activated sludge process

Fuzzy Sets and Systems
Neural Networks: A Comprehensive Foundation (3rd Edition)

Neural Networks: A Comprehensive Foundation (3rd Edition)
A neural network controller for systems with unmodeled dynamics with applications to wastewater treatment

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Control rules of aeration in a submerged biofilm wastewater treatment process using fuzzy neural networks

Expert Systems with Applications: An International Journal
Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system

Applied Soft Computing
Using fuzzy neural network to explore the effect of internet on quality of life

ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a special rule base extraction analysis for optimal design of an integrated neural-fuzzy process controller using an "impact assessment approach." It sheds light on how to avoid some unreasonable fuzzy control rules by screening inappropriate fuzzy operators and reducing over fitting issues simultaneously when tuning parameter values for these prescribed fuzzy control rules. To mitigate the design efforts, the self-learning ability embedded in the neural networks model was emphasized for improving the rule extraction performance. An aeration unit in an Aerated Submerged Biofilm Wastewater Treatment Process (ASBWTP) was picked up to support the derivation of a solid fuzzy control rule base. Four different fuzzy operators were compared against one other in terms of their actual performance of automated knowledge acquisition in the system based on a partial or full rule base prescribed. Research findings suggest that using bounded difference fuzzy operator (Ob) in connection with back propagation neural networks (BPN) algorithm would be the best choice to build up this feedforward fuzzy controller design.