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Intelligent management of coal stockpiles using improved grey spontaneous combustion forecasting models

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  • Peng, Gongzhuang
  • Wang, Hongwei
  • Song, Xiao
  • Zhang, Heming

Abstract

Intelligent coal stockpiles management system is significant for the next-generation cleaner power plants. Prevention of spontaneous combustion is a key issue for such a system, both in economic and environmental terms. As many factors can influence the self heating process of coal such as moisture and ash in coal, temperature distribution and stockpiles' shapes, the remaining ignition time is developed as an aggregative indicator to measure the tendencies of spontaneous coal combustion. Using this value, the grey models have been applied to forecast spontaneous combustion and their performances are good for systems with insufficient information. However, the forecasting accuracy of these models still needs to be improved. Therefore, the ABC-RGM(1,1) model is proposed in this work based on the rolling-GM(1,1) and the Artificial Bee Colony (ABC) optimization algorithm, which has been applied to the management system of a 4 × 600 MW power plant. The computational experiments show that the ABC-RGM(1,1) model achieves better performance than the other popular grey models and accuracy of forecast is greatly improved especially for short-term forecasts. Such an accurate model is highly important and useful for intelligent coal management systems which can improve decision making and reduce risk.

Suggested Citation

  • Peng, Gongzhuang & Wang, Hongwei & Song, Xiao & Zhang, Heming, 2017. "Intelligent management of coal stockpiles using improved grey spontaneous combustion forecasting models," Energy, Elsevier, vol. 132(C), pages 269-279.
    • Handle: RePEc:eee:energy:v:132:y:2017:i:c:p:269-279
    DOI: 10.1016/j.energy.2017.05.067
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    1. Hamzacebi, Coskun & Es, Huseyin Avni, 2014. "Forecasting the annual electricity consumption of Turkey using an optimized grey model," Energy, Elsevier, vol. 70(C), pages 165-171.
    2. Zhao, Ze & Wang, Jianzhou & Zhao, Jing & Su, Zhongyue, 2012. "Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China," Omega, Elsevier, vol. 40(5), pages 525-532.
    3. Li-Hui Chen & Tsuei-Yang Guo, 2011. "Forecasting financial crises for an enterprise by using the Grey Markov forecasting model," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(4), pages 911-922, June.
    4. Bahrami, Saadat & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2014. "Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm," Energy, Elsevier, vol. 72(C), pages 434-442.
    5. Smrekar, J. & Assadi, M. & Fast, M. & Kuštrin, I. & De, S., 2009. "Development of artificial neural network model for a coal-fired boiler using real plant data," Energy, Elsevier, vol. 34(2), pages 144-152.
    6. Lv, You & Liu, Jizhen & Yang, Tingting & Zeng, Deliang, 2013. "A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 319-329.
    7. Davison, John, 2007. "Performance and costs of power plants with capture and storage of CO2," Energy, Elsevier, vol. 32(7), pages 1163-1176.
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    Cited by:

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    3. Li, Jinhu & Li, Zenghua & Yang, Yongliang & Duan, Yujian & Xu, Jun & Gao, Ruiting, 2019. "Examination of CO, CO2 and active sites formation during isothermal pyrolysis of coal at low temperatures," Energy, Elsevier, vol. 185(C), pages 28-38.
    4. Li, Shoujun & Miao, Yanzi & Li, Guangyu & Ikram, Muhammad, 2020. "A novel varistructure grey forecasting model with speed adaptation and its application," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 172(C), pages 45-70.
    5. Lv, Hongpeng & Li, Bei & Deng, Jun & Ye, Lili & Gao, Wei & Shu, Chi-Min & Bi, Mingshu, 2021. "A novel methodology for evaluating the inhibitory effect of chloride salts on the ignition risk of coal spontaneous combustion," Energy, Elsevier, vol. 231(C).
    6. Xu, Yong-liang & Liu, Ze-jian & Wen, Xing-lin & Wang, Lan-yun & Lv, Zhi-guang & Wu, Jin-dong & Li, Min-jie, 2022. "The cataclysmic characteristics for bituminous-coal oxidation under uniaxial stress based on catastrophe theory," Energy, Elsevier, vol. 248(C).
    7. Li, Purui & Yang, Yongliang & Zhao, Xiaohao & Li, Jinhu & Yang, Jingjing & Zhang, Yifan & Yan, Qi & Shen, Chang, 2023. "Spontaneous combustion and oxidation kinetic characteristics of alkaline-water-immersed coal," Energy, Elsevier, vol. 263(PE).
    8. Yan, Li & Wen, Hu & Liu, Wenyong & Jin, Yongfei & Liu, Yin & Li, Chuansheng, 2022. "Adiabatic spontaneous coal combustion period derived from the thermal effect of spontaneous combustion," Energy, Elsevier, vol. 239(PB).
    9. Zhao, Jingyu & Deng, Jun & Wang, Tao & Song, Jiajia & Zhang, Yanni & Shu, Chi-Min & Zeng, Qiang, 2019. "Assessing the effectiveness of a high-temperature-programmed experimental system for simulating the spontaneous combustion properties of bituminous coal through thermokinetic analysis of four oxidatio," Energy, Elsevier, vol. 169(C), pages 587-596.

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