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20 October 2022 Research on the classification method of industrial wastewater by UV-vis spectroscopy based on CNN-LSTM
Junfeng Miao, Bin Tang, Zourong Long, Qing Chen, Jinfu Zhang, Ziheng Huang, Mingfu Zhao
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Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124511E (2022) https://doi.org/10.1117/12.2656736
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China
Abstract
Industrial wastewater classification is the basic work of water pollution prevention and control and water resources management, water chemical oxygen demand is the core indicator to measure the quality of water, compared with domestic sewage detection, the research on industrial wastewater classification is relatively lagging behind. Aiming at the shortcomings of COD classification model of industrial wastewater using convolutional neural network or long shortterm memory network alone, this paper constructs a hybrid model based on CNN and LSTM. The COD data of industrial wastewater were measured by ultraviolet-visible spectroscopy, abstract features were extracted from the data using CNN, and finally entered into LSTM, and the results of classification of industrial wastewater according to COD concentration were obtained, and the classification accuracy of the model reached 98.66%, which was 3.36% and 4.7% higher than that of CNN and LSTM alone.
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Junfeng Miao, Bin Tang, Zourong Long, Qing Chen, Jinfu Zhang, Ziheng Huang, and Mingfu Zhao "Research on the classification method of industrial wastewater by UV-vis spectroscopy based on CNN-LSTM", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124511E (20 October 2022); https://doi.org/10.1117/12.2656736
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KEYWORDS
Data modeling
Water
Industrial chemicals
Spectroscopy
UV-Vis spectroscopy
Oxygen
Neural networks
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