Soft-sensing method for effluent nitrogen parameters based on a dynamic fuzzy neural network
Aiming at the real-time and accurate measurements of the effluent ammonium nitrogen(NH4+-N)and the effluent total nitrogen(TN)in municipal wastewater treatment process,a soft-sensing method for effluent nitrogen param-eters based on a dynamic fuzzy neural network(DFNN)is proposed in this paper.First,by utilizing a self-organizing growing-and-pruning mechanism and an improved second-order learning algorithm,a fuzzy neural network(FNN)is con-structed in order to obtain a soft-sensing model with a simplified structure.Then,by introducing an adaptive firing strength threshold,a hierarchical updating strategy of FNN is designed,which can effectively ensure the prediction accuracy of the soft-sensing model under non-stationary environments.Finally,the effectiveness of the proposed DFNN soft-sensing method is verified based on the simulation data which were provided by the benchmark simulation model No.1(BSM1)platform.The simulation results show that the proposed soft-sensing method can achieve online and accurate measurements of the effluent NH4+-N and the effluent TN.
municipal wastewater treatment processfuzzy neural networkhierarchical updatingeffluent nitrogen concentrationsoft-sensing
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