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Heterogeneous feature ensemble modeling with stochastic configuration networks for predicting furnace temperature of a municipal solid waste incineration process

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Abstract

Considering the accuracy, generalization ability, stability, and training efficiency of a furnace temperature model in the process of municipal solid waste incineration, a heterogeneous feature ensemble modeling method for furnace temperature is proposed in this paper. First, heterogeneous features are generated according to the operation mechanism of the waste incineration process, and the training subset of the furnace temperature- and grate temperature-based model is determined from the historical data of this process. Second, the base model pools of furnace temperature and grate temperature are constructed by a regularized stochastic configuration network, and a set of optimal base models are retained by selective base model technology. Then, a negative correlation learning strategy is employed to establish a simultaneous training ensemble model of furnace temperature, and a regularized stochastic configuration network is used to establish a secondary training ensemble model of furnace temperature. The final output of the furnace temperature is obtained by the average value of the output of the above two ensemble models. Finally, a comparative experiment is carried out using the historical data of a waste incineration plant. The results show that the furnace temperature model established in this paper has advantages in accuracy, generalization ability, stability, and training efficiency. It can be applied to the field of furnace temperature prediction and control in the waste incineration process.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61873009), the Beijing Natural Science Foundation of China (4192009), and the National Key R&D Program of China under Grant (2018AAA0100304).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Aijun Yan & Jingcheng Guo

  2. Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China

    Aijun Yan & Jingcheng Guo

  3. Beijing Laboratory for Urban Mass Transit, Beijing, 100124, China

    Aijun Yan

  4. Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China

    Dianhui Wang

  5. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China

    Dianhui Wang

  6. Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, 3086, Australia

    Dianhui Wang

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  1. Aijun Yan
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  2. Jingcheng Guo
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  3. Dianhui Wang
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Correspondence to Dianhui Wang.

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Yan, A., Guo, J. & Wang, D. Heterogeneous feature ensemble modeling with stochastic configuration networks for predicting furnace temperature of a municipal solid waste incineration process. Neural Comput & Applic 34, 15807–15819 (2022). https://doi.org/10.1007/s00521-022-07271-9

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  • DOI: https://doi.org/10.1007/s00521-022-07271-9

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