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Calorific value prediction of coal and its optimization by machine learning based on limited samples in a wide range

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  • Büyükkanber, Kaan
  • Haykiri-Acma, Hanzade
  • Yaman, Serdar

Abstract

Measuring the heating value through experimentation is a laborious process that demands precise instruments and a skilled technician. Due to the challenges associated with experimental determination of calorific value, a wide range of linear and non-linear models have been created as an alternative approach. Unlike the chiefly studies on calorific value estimation, this study represents hyperparameter tuning application especially within Random Forest (RF) and Artificial Neural Network (ANN) methodologies. In addition, limited amount of data in a wide range was considered to establish comprehensive and consistent models. The fundamental aim of the study is to optimize and simplify the model while maintaining satisfactory performance. When RF method was applied, equations including six parameters (fixed carbon, volatile matter, ash, carbon, hydrogen, and sulfur contents) and single parameter (carbon content) gave comparable prediction performances with R2 values of 0.968 and 0.961, and mean absolute error (MAE) of 1.101 and 1.134, respectively. ANN, Decision Tree (DT), and Multiple Linear Regression (MLR) methods were also tested. It was concluded that the RF and ANN methods, which uses even a univariate equation of carbon, can provide satisfactory prediction, despite the fact that sample properties changed in wide ranges and the number of data was limited.

Suggested Citation

  • Büyükkanber, Kaan & Haykiri-Acma, Hanzade & Yaman, Serdar, 2023. "Calorific value prediction of coal and its optimization by machine learning based on limited samples in a wide range," Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:energy:v:277:y:2023:i:c:s0360544223010605
    DOI: 10.1016/j.energy.2023.127666
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    References listed on IDEAS

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    1. Xing, Jiangkuan & Wang, Haiou & Luo, Kun & Wang, Shuai & Bai, Yun & Fan, Jianren, 2019. "Predictive single-step kinetic model of biomass devolatilization for CFD applications: A comparison study of empirical correlations (EC), artificial neural networks (ANN) and random forest (RF)," Renewable Energy, Elsevier, vol. 136(C), pages 104-114.
    2. Mutlu, Ali Yener & Yucel, Ozgun, 2018. "An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification," Energy, Elsevier, vol. 165(PA), pages 895-901.
    3. Ascher, Simon & Watson, Ian & You, Siming, 2022. "Machine learning methods for modelling the gasification and pyrolysis of biomass and waste," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    4. Xing, Jiangkuan & Luo, Kun & Wang, Haiou & Gao, Zhengwei & Fan, Jianren, 2019. "A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches," Energy, Elsevier, vol. 188(C).
    5. Ahmadi, Mohammadali, 2023. "Data-driven approaches for predicting wax deposition," Energy, Elsevier, vol. 265(C).
    6. Leng, Erwei & He, Ben & Chen, Jingwei & Liao, Gaoliang & Ma, Yinjie & Zhang, Feng & Liu, Shuai & E, Jiaqiang, 2021. "Prediction of three-phase product distribution and bio-oil heating value of biomass fast pyrolysis based on machine learning," Energy, Elsevier, vol. 236(C).
    7. Li, Jie & Pan, Lanjia & Suvarna, Manu & Tong, Yen Wah & Wang, Xiaonan, 2020. "Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning," Applied Energy, Elsevier, vol. 269(C).
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    1. Chen, Zhiwen & Zhao, Ming & Lv, Yi & Wang, Iwei & Tariq, Ghulam & Zhao, Sheng & Ahmed, Shakil & Dong, Weiguo & Ji, Guozhao, 2024. "Higher heating value prediction of high ash gasification-residues: Comparison of white, grey, and black box models," Energy, Elsevier, vol. 288(C).

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