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Calorific Value Forecasting of Coal Gangue with Hybrid Kernel Function–Support Vector Regression and Genetic Algorithm

Author

Listed:
  • Xiangbing Gao

    (Xinjiang Xinneng Group Company Limited, Urumqi Electric Power Construction and Commissioning Institute, Urumqi 830000, China)

  • Bo Jia

    (State Grid XinJiang Company Limited Electric Power Research Institute, Urumqi 830000, China)

  • Gen Li

    (School of Electrical Power Engineering, South China University of Technology, Guangzhou 510641, China)

  • Xiaojing Ma

    (School of Electrical Engineering, Xinjiang University, Urumqi 830017, China)

Abstract

The calorific value of coal gangue is a critical index for coal waste recycling and the energy industry. To establish an accurate and efficient calorific value forecasting model, a method based on hybrid kernel function–support vector regression and genetic algorithms is presented in this paper. Firstly, key features of coal gangue gathered from major coal mines are measured and used to build a sample set. Then, the forecasting performance of single kernel function-based models is established, and linear kernel and Gaussian kernel functions are chosen according to forecasting results. Next, a hybrid kernel combined with the two kernel functions mentioned above is used to establish a calorific value forecasting model. In addition, a genetic algorithm is introduced to optimize critical parameters of SVR and the adjustable weight. Finally, the forecasting model based on hybrid kernel function–support vector regression and genetic algorithms is built to predict the calorific value of new coal gangue samples. The experimental results indicate that the hybrid kernel function is more suitable for forecasting the calorific value of coal gangue than that of a single kernel function. Moreover, the forecasting performance of the proposed method is better than other conventional forecasting methods.

Suggested Citation

  • Xiangbing Gao & Bo Jia & Gen Li & Xiaojing Ma, 2022. "Calorific Value Forecasting of Coal Gangue with Hybrid Kernel Function–Support Vector Regression and Genetic Algorithm," Energies, MDPI, vol. 15(18), pages 1-15, September.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6718-:d:914694
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    References listed on IDEAS

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    1. Xiao, Jianli & Wei, Chao & Liu, Yuncai, 2018. "Speed estimation of traffic flow using multiple kernel support vector regression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 989-997.
    2. Tusongjiang Kari & Wensheng Gao & Ayiguzhali Tuluhong & Yilihamu Yaermaimaiti & Ziwei Zhang, 2018. "Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers," Energies, MDPI, vol. 11(9), pages 1-19, September.
    3. Bi, Haobo & Wang, Chengxin & Lin, Qizhao & Jiang, Xuedan & Jiang, Chunlong & Bao, Lin, 2020. "Combustion behavior, kinetics, gas emission characteristics and artificial neural network modeling of coal gangue and biomass via TG-FTIR," Energy, Elsevier, vol. 213(C).
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    Cited by:

    1. Bilin Shao & Zixuan Yao & Yifan Qiang, 2023. "Point-Interval Forecasting for Electricity Load Based on Regular Fluctuation Component Extraction," Energies, MDPI, vol. 16(4), pages 1-20, February.

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