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Frontiers of Environmental Science & Engineering >> 2023, Volume 17, Issue 6 doi: 10.1007/s11783-023-1667-3

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator

1. SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou 510006, China;1. SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou 510006, China;1. SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou 510006, China;1. SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou 510006, China;2. Fujian Environmental Protection Design Institute Co. Ltd, Fuzhou 350000, China;2. Fujian Environmental Protection Design Institute Co. Ltd, Fuzhou 350000, China;2. Fujian Environmental Protection Design Institute Co. Ltd, Fuzhou 350000, China;3. National Institute of Oceanography and Fisheries, NIOF, Alexandria 21556, Egypt;3. National Institute of Oceanography and Fisheries, NIOF, Alexandria 21556, Egypt;1. SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou 510006, China;4. SCNU Qingyuan Institute of Science and Technology Innovation Co., Ltd., Qingyuan 511517, China

Received: 2022-03-07 Available online: 2022-03-07

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Abstract

● Hybrid deep-learning model is proposed for water quality prediction.

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