title: Monitoring of anoxic/oxic process for nitrogen and chemical oxygen demand removal using fuzzy neural networks.
abstract: In this paper, a software sensor based on a fuzzy neural network (FNN) approach was proposed for the real-time estimation of nutrient concentrations and overcoming the problem of delayed measurements. To improve the FNN performance, fuzzy subtractive clustering was used to identify the model's architecture and optimize the fuzzy rule; meanwhile, a split network structure, applied separately for anaerobic and aerobic conditions, was used with dynamic modeling methods, such as an auto-regressive model with exogenous inputs. The proposed methodology was applied to a bench-scale anoxic/oxic process for biological nitrogen removal. It was possible to partially overcome the extrapolation problem of FNNs with the aid of multi-way principal component analysis, because it has the ability to detect abnormal situations, which could generate extrapolation. Real-time estimation of chemical oxygen demand, nitrate, and ammonium concentrations based on the model was successfully carried out with the simple online information of the anoxic/oxic system.
authors.affiliation: College of Environmental Science and Engineering, South China University of Technology, Guangzhou, People's Republic of China. mz.huang@mail.scut.edu.cn
authors.forename: Mingzhi
authors.initials: M
authors.lastname: Huang
authors.forename: Jinquan
authors.initials: J
authors.lastname: Wan
authors.forename: Yongwen
authors.initials: Y
authors.lastname: Ma
mesh_terms: D019149:Bioreactors; D017143:Fuzzy Logic; D006863:Hydrogen-Ion Concentration; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000378:metabolism*; D010084:Oxidation-Reduction; D010100:Oxygen / Q000378:metabolism*; D014865:Waste Disposal, Fluid / Q000379:methods*; D014873:Water Pollutants / Q000378:metabolism*; D018508:Water Purification / Q000379:methods
chemical_list: D014873:Water Pollutants; D009584:Nitrogen; D010100:Oxygen