Fuzzy rule-based inference of reasons for high effluent quality in municipal wastewater treatment plant

Abstract

A fuzzy inference system (FIS), which could classify the state of effluent quality if it was high or not and identify visually the reasons for the high effluent quality in municipal wastewater treatment plants (WWTPs), was developed in this study. The decision tree algorithm and fuzzy technique were applied in the development of this system. By applying the classification and regression tree (CART) algorithm as a decision tree algorithm, the knowledge related to effluent quality was extracted and IF-THEN rules with crisp boundary values were generated. By applying the fuzzy technique, the fuzzification of these rules was conducted, where the trapezoidal and triangular membership function was used as a membership function type. And a Mamdani model with the Max-min operation was used as an inference model and the center of area (CoA) method was used for deffuzification. The accuracy achieved by using the developed system to classify the effluent state was confirmed by comparing the result with measured data. Furthermore, the developed system was demonstrated to be a useful tool for inferring the reasons for the high effluent quality.