Soft sensing of effluent ammonia nitrogen using rule automatic formation-based adaptive fuzzy neural network
Hongbiao Zhou, Junfei Qiao · Desalination and Water Treatment · 2019
ABSTRACT This paper proposes a data-driven soft-sensing method for predicting effluent ammonia nitrogen (NH 4 –N) in the wastewater treatment process (WWTP). In this method, a rule automatic formation-based adaptive fuzzy neural network (RAF-AFNN) is designed. The RAF algorithm, which consists of rule self-splitting strategy and fuzzy Gaussian kernel clustering, is used to automatically partition the input space and adaptively extract the most suitable fuzzy rules. An improved adaptive Levenberg–Marquardt learning algorithm is implemented to tune the parameters of the RAF-AFNN for improving prediction accuracy. An analysis of the convergence is also provided in this paper, which can guarantee the successful application of the proposed RAF-AFNN. Finally, experimental hardware, constructed from an online sensor array and via the soft-sensing method, is used to assess the effectiveness of the RAF-AFNN for solving the problem of effluent NH 4 –N prediction in the WWTP. Experimental results indicate that the proposed RAF-AFNN-based soft-sensing method can predict the effluent NH 4 –N precisely.