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Multi-Temperature Synchronous Prediction of Municipal Solid Waste Incineration Based on a Non-Stationary Crossformer

Author

Listed:
  • Liqun Chang

    (College of Information Engineering, Dalian Ocean University, Dalian 116023, China
    Dalian Key Laboratory of Smart Fisheries, Dalian Ocean University, Dalian 116023, China)

  • Wei Wang

    (College of Information Engineering, Dalian Ocean University, Dalian 116023, China
    Dalian Key Laboratory of Smart Fisheries, Dalian Ocean University, Dalian 116023, China)

  • Luyang Zhang

    (College of Information Engineering, Dalian Ocean University, Dalian 116023, China
    Dalian Key Laboratory of Smart Fisheries, Dalian Ocean University, Dalian 116023, China)

  • Jing Lv

    (College of Information Engineering, Dalian Ocean University, Dalian 116023, China
    Dalian Key Laboratory of Smart Fisheries, Dalian Ocean University, Dalian 116023, China)

  • Yunlai Fu

    (College of Information Engineering, Dalian Ocean University, Dalian 116023, China
    Dalian Key Laboratory of Smart Fisheries, Dalian Ocean University, Dalian 116023, China)

  • Xianhui Hu

    (College of Information Engineering, Dalian Ocean University, Dalian 116023, China
    Dalian Key Laboratory of Smart Fisheries, Dalian Ocean University, Dalian 116023, China)

Abstract

In this study, we designed a multi-temperature synchronous prediction method for municipal solid waste incineration (MSWI) based on a non-stationary Crossformer model. It is mainly divided into the following steps: First, use a normalization module to normalize the preprocessed temperature sample data. Secondly, add a non-stationary attention module to calculate the non-stationary attention of a single time series. Next, construct an attention convolutional network to extract non-stationary attention features, and based on this, calculate the attention between multiple temperature time series. Finally, perform data restoration based on a de-normalization module. By using the above methods, not only can the data characteristics lost during the model’s over-processing of the original temperature data during training be restored, but also the deeper level of data features and the correlation between various temperatures can be captured. The non-stationary recovery and correlation calculation of multi-temperature time series were achieved simultaneously. The experimental results based on actual data from Chinese MSWI factories show that this method has high accuracy in the multi-temperature synchronous prediction of MSWI.

Suggested Citation

  • Liqun Chang & Wei Wang & Luyang Zhang & Jing Lv & Yunlai Fu & Xianhui Hu, 2025. "Multi-Temperature Synchronous Prediction of Municipal Solid Waste Incineration Based on a Non-Stationary Crossformer," Sustainability, MDPI, vol. 17(5), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:5:p:1789-:d:1595780
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    References listed on IDEAS

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    1. Altaf Hussain Kanhar & Shaoqing Chen & Fei Wang, 2020. "Incineration Fly Ash and Its Treatment to Possible Utilization: A Review," Energies, MDPI, vol. 13(24), pages 1-35, December.
    2. Zheng Zhao & Ziyu Zhou & Ye Lu & Zhuoge Li & Qiang Wei & Hongbin Xu, 2023. "Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model," Sustainability, MDPI, vol. 15(19), pages 1-22, October.
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