한국환경과학회지 제31권 제7호 (p.555-567)

신경망 분석을 활용한 하수처리장 데이터 분석 기법 연구

Wastewater Treatment Plant Data Analysis Using Neural Network
키워드 :
Multilayer perceptron analysis,Root mean square error analysis,TMS data,Wastewater treatment plants

목차

Abstract
1. 서 론
2. 분석방법
   2.1. 데이터 사전 준비
   2.2. 다층 퍼셉트론 분석 (Multi Layer Perceptron,MLP)
   2.3. 예측값의 평가를 위한 회귀분석 및 평균 제곱근오차 분석 (Root Mean Square Error, RMSE)
3. 결과 및 고찰
   3.1. MLP 분석 결과
   3.2. 회귀분석 및 평균제곱근오차 RMSE 결과
   3.3. 전력사용량 예측 및 분석
4. 결 론
REFERENCES

초록

With the introduction of the tele-monitoring system (TMS) in South Korea, monitoring of the concentration of pollutants discharged from nationwide water quality TMS attachments is possible. In addition, the Ministry of Environment is implementing a smart sewage system program that combines ICT technology with wastewater treatment plants. Thus, many institutions are adopting the automatic operation technique which uses process operation factors and TMS data of sewage treatment plants. As a part of the preliminary study, a multilayer perceptron (MLP) analysis method was applied to TMS data to identify predictability degree. TMS data were designated as independent variables, and each pollutant was considered as an independent variables. To verify the validity of the prediction, root mean square error analysis was conducted. TMS data from two public sewage treatment plants in Chungnam were used. The values of RMSE in SS, T-N, and COD predictions (excluding T-P) in treatment plant A showed an error range of 10%, and in the case of treatment plant B, all items showed an error exceeding 20%. If the total amount of data used MLP analysis increases, the predictability of MLP analysis is expected to increase further.