Wastewater Do concentration control through NH4 prediction based on evolutionary radial basis function neural network

Authors:
Liang Jin;Luo Fei;Xu Yu-Ge
Affiliations:
College of Automation Science and Engineering, South China University of Technology, Guangzhou, China;College of Automation Science and Engineering, South China University of Technology, Guangzhou, China;College of Automation Science and Engineering, South China University of Technology, Guangzhou, China
Venue:
ICNC'09 Proceedings of the 5th international conference on Natural computation
Year:
2009

Citing 7
Cited 0

Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
A differential evolution based incremental training method for RBF networks

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A pareto archive evolutionary strategy based radial basis function neural network training algorithm for failure rate prediction in overhead feeders

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy

Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach

IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Properties of an adaptive archiving algorithm for storing nondominated vectors

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Evolutionary Neural network has been used in many industries control problems. This paper analyzes Dissolved Oxygen (DO) model and set-point control, then using Evolutionary Radial Basis Function (RBF) Neural Network to present a new idea and model for DO concentration control. The idea is to control DO set-point through ammonium concentration prediction based on Evolutionary RBF Neural Network. Compared to the idea of DO set-point control from on-line measurements of the ammonium concentration, new idea is better in response to actual situation. According to analyzing and Evolutionary RBF Neural Network theory, an Evolutionary RBF Neural Network is designed. Real wastewater plant data is used to the model simulation. Simulation shows that the idea and model is a good way to the DO concentration control.