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http://dx.doi.org/10.6109/jkiice.2021.25.6.792

Determination of coagulant input rate in water purification plant using K-means algorithm and GBR algorithm  

Kim, Jinyoung (Department of Computer Engineering, Paichai University)
Kang, Bokseon (Department of Computer Engineering, Paichai University)
Jung, Hoekyung (Department of Computer Engineering, Paichai University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.6, 2021 , pp. 792-798 More about this Journal
Abstract
In this paper, an algorithm for determining the coagulant input rate in the drug-injection tank during the process of the water purification plant was derived through big data analysis and prediction based on artificial intelligence. In addition, analysis of big data technology and AI algorithm application methods and existing academic and technical data were reviewed to analyze and review application cases in similar fields. Through this, the goal was to develop an algorithm for determining the coagulant input rate and to present the optimal input rate through autonomous driving simulator and pilot operation of the coagulant input process. Through this study, the coagulant injection rate, which is an output variable, is determined based on various input variables, and it is developed to simulate the relationship pattern between the input variable and the output variable and apply the learned pattern to the decision-making pattern of water plant operating workers.
Keywords
Plant automation; Machine learning; Water plant operation; Water treatment system; Artificial intelligence;
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  • Reference
1 J. Y. Kim, "A study on the determination of coagulant input rate in water purification plants based on artificial intelligence," Ph. D. thesis, Paichai Univ., 2020.
2 Global Outlook of the Water Industry, Frost & Sullivan Report, 2018.
3 M. M. Khan, D. R. Lester, L. A. Plana, A. Rast, X. Jin, E. painkras, and S. B. Furber, "SpiNNaker: mapping neural networks onto a massively-parallel chip multiprocessor," 2008 IEEE International Joint Conference on Neural Networks, pp. 2849-2856, 2008.
4 S. D. Yang, J. S. Lee, and H. K, Jung, "Fault Diagnosis Management Model using Machine," Journal of information and communication convergence engineering, vol. 17, no. 2, pp. 128-134, Jun. 2019.   DOI
5 J. Jo and K. W. Lee, "High-Performance Geospatial Big Data Processing System Based on MapReduce," ISPRS Int, J. Geo-Inf, vol. 7, no. 10, 2018.
6 T. W. Hertel and J. Liu, "Implication of water scarcity for economic growth," OECD Environment Working paper, no. 109, pp. 8, 2016.
7 A. K. Lee, J. A. Yang, and N. Kim, "The study on the direction of the control logic improvement for an advancement integrated operation system," The Institute of Webcasting, Internet Television and Telecommunication conference, 2010.
8 M. Grover, T. Malaska, J. Seidman, and G. Shapira, "Hadoop Application Architectures Designing Real-World Big Data Applications," New York : O'Reilly Media, 2015.
9 T. Chai and R. R. Draxler, "Root mean square error (RMSE) or mean absolute error (MAE)?-Arguments against avoiding RMSE in the literature," Geoscientific model development, vol. 7, no. 3, pp. 1247-1250, 2014.   DOI
10 M. A. Abbasi, "Learning Apache Spark 2," Packt Publishing Ltd., pp. 23, 2017.