Research on Optimization of Total Nitrogen Peak Suppression in Wastewater Treatment Based on the Data Driven Method

Abstract

In order to solve the problems of water eutrophication, algae anoxic decay, and death by biological poisoning, which are caused by the excessive emission of total nitrogen in wastewater treatment process, this paper proposes a method of total nitrogen peak suppression which is based on neural network decision optimization. First, the SSORBF neural network is established according to the wastewater treatment process, and total nitrogen, inflow flow, current total nitrogen, dissolved oxygen concentration, and nitrate nitrogen concentration are selected to predict the total nitrogen concentration. Second, the density- and memory-based NSGA2 multiobjective optimization method is used to set the optimal solution to meet the requirement of energy consumption. If the prediction of total nitrogen exceeded the set value, the optimal control strategy is adopted to control the peak value of total nitrogen in advance, and it cannot exceed the national maximum allowable emission value. If the prediction of total nitrogen is lower than the set value, it continues to track the parameter set value. Finally, compared with other methods, the proposed method can effectively suppress the peak value of total nitrogen under 18 mg/L and reduce the energy consumption in wastewater treatment by 7.6%. It can provide decisions and advice for wastewater treatment plants.