Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process

Abstract

Abstract Aeration control is a way to have a wastewater treatment plant (WWTP) that uses less energy and produces higher effluent quality to meet state and federal regulations. The goal of this research is to develop a neural network (NN) ammonia-based aeration control (ABAC) that focuses on reducing total nitrogen and ammonia concentration violations by regulating dissolved oxygen (DO) concentration based on the ammonia concentration in the final tank, rather than maintaining the DO concentration at a set elevated value, as most studies do. Simulation platform used in this study is Benchmark Simulation Model No. 1, and the NN ABAC is compared to the Proportional-Integral (PI) ABAC and PI controller. In comparison to the PI controller, the simulation results showed that the proposed controller has a significant improvement in reducing the AECI up to 23.86%, improving the EQCI up to 1.94%, and reducing the overall OCI up to 4.61%. The results of the study show that the NN ABAC can be utilized to improve the performance of a WWTP’s activated sludge system.