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Using a Novel Algorithm Based on the Random Vector Functional Link Network and Multi-Verse Optimizer to Forecast Effluent Quality

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  • Huixian Shi

    (National Engineering Research Center of Protected Agriculture, Tongji University, Shanghai 200092, China)

  • Zijing Wang

    (National Engineering Research Center of Protected Agriculture, Tongji University, Shanghai 200092, China)

  • Haiyi Zhou

    (National Engineering Research Center of Protected Agriculture, Tongji University, Shanghai 200092, China)

  • Kaiyan Lin

    (National Engineering Research Center of Protected Agriculture, Tongji University, Shanghai 200092, China)

  • Shuping Li

    (College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China)

  • Xinnan Zheng

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China)

  • Zheng Shen

    (National Engineering Research Center of Protected Agriculture, Tongji University, Shanghai 200092, China
    Shanghai Engineering Research Center of Protected Agriculture, Tongji University, Shanghai 200092, China)

  • Jiaoliao Chen

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China)

  • Lei Zhang

    (College of Transportation Engineering, Tongji University, Shanghai 200092, China)

  • Yalei Zhang

    (College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China)

Abstract

The treatment of wastewater is a complicated biological reaction process. Reliable effluent prediction is critical in the scientific management of water treatment plants. This research proposes a soft sensor design strategy to address the issues above, Multi-Verse Optimizer (MVO)-based random vector functional link network (MVO-RVFL). The proposed approach is utilized to anticipate real-time effluent data obtained from the Benchmark Simulation Model 1 (BSM1). The results of the experiments demonstrate that the MVO methodology can successfully find the optimum input-hidden weights and hidden biases of the RVFL model while outperforming the original RVFL and other typical machine learning approaches in all types of influent datasets. In the situation of significant water quality variations, the use of the fusion process for model development was also investigated. The experimental results demonstrate that incorporating prior knowledge can effectively improve the model’s ability to cope with unexpected situations.

Suggested Citation

  • Huixian Shi & Zijing Wang & Haiyi Zhou & Kaiyan Lin & Shuping Li & Xinnan Zheng & Zheng Shen & Jiaoliao Chen & Lei Zhang & Yalei Zhang, 2022. "Using a Novel Algorithm Based on the Random Vector Functional Link Network and Multi-Verse Optimizer to Forecast Effluent Quality," Sustainability, MDPI, vol. 14(14), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8314-:d:857612
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    References listed on IDEAS

    as
    1. Farzin Golzar & David Nilsson & Viktoria Martin, 2020. "Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
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