Fuzzy inference‐based control and decision system for precise aeration of sewage treatment process

Abstract

The sewage treatment systems manage to reduce pollutants of wastewater to make it reach some requirement. The core of it is to effectively control and determine intermediate aeration amount, which is always challenging. Therefore, a multi-objective planning mechanism (multi-objective optimization design combining GRA and fuzzy logic inference) that combines grey relational analysis and fuzzy logic, to find the optimal dissolved oxygen solubility for achieving better outlet water quality, is proposed. First of all, grey correlation coefficient between each optimization target and the reference target is calculated, and are converted into fuzzy inference values through the four steps. After that, it is expected to analyse the average values of process variables to obtain the optimal parameter combinations. A real-world dataset collected from a realistic sewage treatment plant is utilized as the simulation environment to evaluate the proposed multi-objective optimization design combining GRA and fuzzy logic inference. Experimental results show that the multi-objective optimization design combining GRA and fuzzy logic inference makes promotion of 47.34% for fitted outlet water quality compared with the original average annual water quality.