Advanced monitoring platform for industrial wastewater treatment: Multivariable approach using the self-organizing map |
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| Affiliation: | 1. University of Eastern Finland, Department of Environmental Science, P.O. Box 1627, FIN-70211 Kuopio, Finland;2. Stora Enso Printing and Reading Paper, P.O. Box 196, FI-90101 Oulu, Finland;1. Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8571, Japan;2. JST, CREST, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan;3. Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan;4. Department of Physics, Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8571, Japan;1. Fuels Research Center, Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand;2. Graduate School of Engineering & Green Mobility Collaborative Research Center, Nagoya University, Japan;3. Center of Excellence on Petrochemical and Materials Technology (PETRO-MAT), Chulalongkorn University, Bangkok 10330, Thailand;4. Associate Fellow of Royal Society of Thailand (AFRST), Bangkok 10300 Thailand;1. Department of Chemical Engineering, Boğaziçi University, 34342 Bebek-Istanbul, Turkey;2. Department of Energy Systems Engineering, Istanbul Bilgi University, 34060 Eyupsultan-Istanbul, Turkey;1. Chemical and Biomolecular Engineering Department, Korea Advaced Institute of Science and Technology, Daejeon, Korea;2. Chemical and Biomolecular Engineering Department, Georgia Institute of Technology, Atlanta, GA, USA |
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| Abstract: | Treatment of industrial wastewaters is currently confronting important challenges concerning both cost management of treatment plants and fulfillment of tightening environmental regulations. Online monitoring of wastewater treatment is critical, because changes in the performance of treatment can lead to various problems such as decreased efficiency of purification, decreased energy efficiency, or ineffective use of chemicals. Moreover, changes in the operation of a treatment process can inflict changes that have unforeseen consequences, including an increased amount of harmful effluents, and therefore it is essential for a monitoring system to be able to adapt to various process conditions. It seems, however, that the monitoring systems used currently by the industry are lacking this functionality and are therefore only partially able to meet the needs of modern industry. In addition, there is typically a large amount of measurement data available in the industry, for which advanced data processing and computational tools are needed for monitoring, analysis, and control. For this reason, it would be useful to have a monitoring system which could be able to handle a large amount of measurement data and present the essential information on the state and evolution of the process in a simple, user-friendly and flexible manner. In this paper, we introduce an adaptive multivariable approach based on self-organizing maps (SOM) which can be utilized for advanced monitoring of industrial processes. The system developed can provide a new kind of tool for illustrating the condition and evolution of an industrial wastewater treatment process. The operation of the system is demonstrated using process measurements from an activated sludge treatment plant, which is a part of a pulp and paper plant. |
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| Keywords: | Water treatment Activated sludge Wastewater Monitoring Warning Self-organizing map |
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