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KMID : 1024520220310070555
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Journal of the Environmental Sciences
2022 Volume.31 No. 7 p.555 ~ p.567
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Wastewater Treatment Plant Data Analysis Using Neural Network
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Seo Jeong-Sig
Kim Tae-Wook
Lee Hae-Kag
Youn Jong-Ho
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Abstract
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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.
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KEYWORD
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Multilayer perceptron analysis, Root mean square error analysis, TMS data, Wastewater treatment plants
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