Journal of Korean Society on Water Environment (한국물환경학회지)

Korean Society on Water Environment (한국물환경학회)
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The Case Studies of Artificial Intelligence Technology for apply at The Sewage Treatment Plant

국내 하수처리시설에 인공지능기술 적용을 위한 사례 연구

  • Kim, Taewoo (Department of Railroad Engineering, Korea National University of Transportation) ;
  • Lee, Hosik (Department of Railroad Infra System Engineering, Korea National University of Transportation)
  • 김태우 (한국교통대학교 철도시설공학과) ;
  • 이호식 (한국교통대학교 철도인프라시스템공학과)
  • Received : 2019.04.11
  • Accepted : 2019.07.25
  • Published : 2019.07.30
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Abstract

In the recent years, various studies have presented stable and economic methods for increased regulations and compliance in sewage treatment plants. In some sewage treatment plants, the effluent concentration exceeded the regulations, or the effluent concentration was manipulated. This indicates that the process is currently inefficient to operate and control sewage treatment plants. The operation and control method of sewage treatment plant is mathematically dealing with a physical and chemical mechanism for the anticipated situation during operation. In addition, there are some limitations, such as situations that are different from the actual sewage treatment plant. Therefore, it is necessary to find a more stable and economical way to enhance the operational and control method. AI (Artificial Intelligence) technology is selected among various methods. There are very few cases of applying and utilizing AI technology in domestic sewage treatment plants. In addition, it failed to define specific definitions of applying AI technologies. The purpose of this study is to present the application of AI technology to domestic sewage treatment plants by comparing and analyzing various cases. This study presented the AI technology algorithm system, verification method, data collection, energy and operating costs as methods of applying AI technology.

Keywords

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Fig. 1. Multi-Layer Perceptron (MLP) Algorithm

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Fig. 2. Backpropagation (BP) Algorithm

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Fig. 3. Genetic Algorithm (GA)

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Fig. 4. Power usage by industry, Based on 2016 year (KEPCO, 2016; K-eco, 2016)

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Fig. 5. Forecasting operations and maintains using Bigdata and IoT platforms (Chalabi and CH2M Beca, 2018)

Table. 1 Advantages and Disadvantages of Sewage Treatment Process Model (ME, 2009a)

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Table. 2 Application Methods of Artificial Intelligence Technology

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Table. 3 Algorithm Characteristic

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Table. 4 Data after applying AI (R, RMSE only) (ME, 2009a)

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Table. 5 Energy consumption ranking of domestic sewage treatment plant (Park et al., 2008)

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Table. 6 Percentage of energy consumption in domestic sewage treatment plant (Park et al., 2008)

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