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Data mining from process monitoring of typical polluting enterprise

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Abstract

With the increasing volume of environmental monitoring data, extracting valuable insights from multivariate time series sensor data can facilitate comprehensive information utilization and support informed decision-making in environmental management. However, there is a dearth of comprehensive research on multivariate data analysis for process monitoring in typical polluting enterprises. In this study, an artificial neural network model based on back-propagation algorithm (BP-ANN) was developed to predict the wastewater and exhaust gas emissions using IoT data obtained from process monitoring of a typical polluting enterprise located in Taizhou, Zhejiang Province, China. The results indicate that the model constructed has a high predictive coefficient of determination (R2) with values of 0.8510, 0.9565, 0.9561, 0.9677, and 0.9061 for chemical oxygen demand (COD), potential of hydrogen (pH), electrical conductivity (EC), flue gas emission (FGE), and non-methane hydrocarbon concentration (NMHC) respectively. For the first time, the variable importance measure (VIM)–assisted BP-ANN was employed to investigate the internal and external correlations between wastewater and exhaust gas treatment, thereby enhancing the interpretability of mapping features in the BP-ANN model. The predicted errors for pH and FGE have been demonstrated to fall within the range of − 0.62 ~ 0.30 and − 0.21 ~ 0.15 m3/s, respectively, with average relative errors of 1.05% and 9.60%, which is advantageous in detecting anomalous data and forecasting pollution indicator values. Our approach successfully addresses the challenge of segregating data analysis for wastewater disposal and exhaust gas disposal in the process monitoring of polluting enterprises, while also unearthing potential variables that significantly contribute to the BP-ANN model, thereby facilitating the selection and extraction of characteristic variables.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Funding

This work was supported by the Science and Technology Program of Zhejiang Province (No. 2021C03178), the Ecological Environment Research and Achievement Extension Project of Zhejiang Province (No. 2022HT0010), and the Science and Technology Plan Project of Taizhou (No. 22gyb37).

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Authors and Affiliations

  1. Taizhou Pollution Control Technology Center Co. LTD, Taizhou , Zhejiang, 318000, China

    Wenya Zhao, Peili Zhang, Da Chen, Hao Wang, Binghua Gu & Jue Zhang

  2. Key Laboratory of the Eco-Environmental Big Data of Taizhou, Taizhou , Zhejiang, 318000, China

    Wenya Zhao, Peili Zhang, Da Chen, Hao Wang, Binghua Gu & Jue Zhang

Authors
  1. Wenya Zhao
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  2. Peili Zhang
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  3. Da Chen
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  4. Hao Wang
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  5. Binghua Gu
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  6. Jue Zhang
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Contributions

All authors contributed to the study conception and design. Wenya Zhao: methodology; investigation; writing—original draft. Peili Zhang: project supervision; research design; data collection; writing—editing and modification. Da Chen: data processing; writing—editing and modification. Hao Wang: funding support, data collection. Binghua Gu: artificial intelligence technology support. Jue Zhang: writing—editing and modification.

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Correspondence to Peili Zhang.

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Zhao, W., Zhang, P., Chen, D. et al. Data mining from process monitoring of typical polluting enterprise. Environ Monit Assess 195, 1109 (2023). https://doi.org/10.1007/s10661-023-11733-5

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