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Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received August 23, 2023
Accepted August 23, 2023

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

Taesup Moon Yejin Kim¹ Hyosu Kim¹ Myungwon Choi Changwon Kim^1†

Environsoft Co., Ltd., #511 Industry-University Co., Bld, Pusan National University, Busan 609-735 Korea ¹Department of Environmental Engineering, Pusan National University, Busan 609-735, Korea

cwkim@pusan.ac.kr

Korean Journal of Chemical Engineering, March 2011, 28(3), 817-824(8), 10.1007/s11814-010-0428-8

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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_x000D_ 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.

Keywords

Rule Generation Decision Tree Fuzzy Inference Diagnosis Wastewater Treatment Plant

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