[논문]Stochastics and Artificial Intelligence-based Analytics of Wastewater Plant Operation

Sung-Hyun Kwon; Daechul Cho

doi:10.7464/ksct.2023.29.2.145

Stochastics and Artificial Intelligence-based Analytics of Wastewater Plant Operation 원문보기

청정기술 = Clean technology, v.29 no.2, 2023년, pp.145 - 150

Sung-Hyun Kwon (Department of Marine Environmental Engineering, Engineering Research Institute Gyeongsang National University) , Daechul Cho (Department of Energy&Environmental Engineering, Soonchunhyang University)

Abstract ▼ AI-Helper

Tele-metering systems have been useful tools for managing domestic wastewater treatment plants (WWTP) over the last decade. They mostly generate water quality data for discharged water to ensure that it complies with mandatory regulations and they may be able to produce every operation parameter and additional measurements in the near future. A sub-big data group, comprised of about 150,000 data points from four domestic WWTPs, was ready to be classified and also analyzed to optimize the WWTP process. We used the Statistical Product and Service Solutions (SPSS) 25 package in order to statistically treat the data with linear regression and correlation analysis. The major independent variables for analysis were water temperature, sludge recycle rate, electricity used, and water quality of the influent while the dependent variables representing the water quality of the effluent included the total nitrogen, which is the most emphasized index for discharged flow in plants. The water temperature and consumed electricity showed a strong correlation with the total nitrogen but the other indices' mutual correlations with other variables were found to be fuzzy due to the large errors involved. In addition, a multilayer perceptron analysis method was applied to TMS data along with root mean square error (RMSE) analysis. This study showed that the RMSE in the SS, T-N, and TOC predictions were in the range of 10% to 20%.

주제어

표/그림 (8)

그림 Figure 1. A typical layout explaining MLP (multilayer perceptron).
표 Table 1. Characteristics of 4 selected wastewater treatment plants
그림 Figure 2. Scattered data distribution between SS and T-N in an inlet stream.
그림 Figure 3. Temperature versus T-N: top, time-line T-N and temperature changes; bottom, Pearson correlation between temperature and effluent T-N.
그림 Figure 4. Time-line data plots for two typical plants.
그림 Figure 5. A preliminary test for multi-layer perceptron analysis: (a) independent variable (IV)=1, (b) IV=5, (c) IV=9.
그림 Figure 6. Schematic diagram depicting couple of trials for MLP analysis: (a) SS, (b) T-N, (c) T-P, (d) TOC.
그림 Figure 7. MLP learning results after 9^th repetitive calculations for T-N in WWTP A (top) and WWTP B (bottom).

참고문헌 (8)

SOOSIRO, 2022, http://www.soosiro.or.kr
Koprea Environment Corporation, 2022, http://www.keco.or.kr
Woongburi, P. and Park, J. K., "Big Data Analytics from a？Wastewater Treatment Plant," Sustainability, 13, 12383 (2021).

상세보기
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