IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v300y2024ics0360544224013872.html
   My bibliography  Save this article

A quantitative investigation on pyrolysis behaviors of metal ion-exchanged coal macerals by interpretable machine learning algorithms

Author

Listed:
  • Yao, Qiuxiang
  • Wang, Linyang
  • Ma, Mingming
  • Ma, Li
  • He, Lei
  • Ma, Duo
  • Sun, Ming

Abstract

Generalizing the rules from complex processes such as catalytic pyrolysis to guide their process control is always a difficult but attractive task. The influences of ion-exchange of metal ions (Na+, K+, Ca2+, Mg2+, Co2+ and Ni2+) on the pyrolysis behavior of vitrinite and inertinite from Shendong coal were investigated by thermogravimetric analyzer-Fourier transform infrared spectrometer (TG-FTIR), fixed-bed reactor (FBR), gas chromatograph-mass spectrometer (GC-MS) and X-ray diffractometer (XRD). A set of machine learning models was successfully constructed based on random forest, support vector machine, and Gaussian process regression, to quantify the relationships between pyrolysis behaviors and the properties of metals and macerals. The leave-one-out-cross validation showed that there are considerable determination coefficients (R2 > 0.9) between predicted and experimental values for most responses (17 out of 29). By combining genetic programming-based symbolic regression with the black-box algorithm, 23 symbolic regression expressions with high confidence were successfully constructed. This work is a pioneering attempt of optimization in small-scale experiments. By utilizing the highly interpretable models, a demand-orientated (multi-)optimization coal pyrolysis can be achieved. The bi-objective optimization was conducted on the yield of tar and the content of light aromatics in tar, and the results show that Co is the optimal loading metal.

Suggested Citation

  • Yao, Qiuxiang & Wang, Linyang & Ma, Mingming & Ma, Li & He, Lei & Ma, Duo & Sun, Ming, 2024. "A quantitative investigation on pyrolysis behaviors of metal ion-exchanged coal macerals by interpretable machine learning algorithms," Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:energy:v:300:y:2024:i:c:s0360544224013872
    DOI: 10.1016/j.energy.2024.131614
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224013872
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.131614?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Tschora, Léonard & Pierre, Erwan & Plantevit, Marc & Robardet, Céline, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Applied Energy, Elsevier, vol. 313(C).
    2. Chen, Yi-Feng & Su, Sheng & Zhang, Liang-Ping & Jiang, Long & Qing, Meng-Xia & Chi, Huan-Ying & Ling, Peng & Han, Heng-Da & Xu, Kai & Wang, Yi & Hu, Song & Xiang, Jun, 2021. "Insights into evolution mechanism of PAHs in coal thermal conversion: A combined experimental and DFT study," Energy, Elsevier, vol. 222(C).
    3. Mehmood, Faiza & Ghani, Muhammad Usman & Ghafoor, Hina & Shahzadi, Rehab & Asim, Muhammad Nabeel & Mahmood, Waqar, 2022. "EGD-SNet: A computational search engine for predicting an end-to-end machine learning pipeline for Energy Generation & Demand Forecasting," Applied Energy, Elsevier, vol. 324(C).
    4. Wang, Wanrong & Ma, Yingjie & Maroufmashat, Azadeh & Zhang, Nan & Li, Jie & Xiao, Xin, 2022. "Optimal design of large-scale solar-aided hydrogen production process via machine learning based optimisation framework," Applied Energy, Elsevier, vol. 305(C).
    5. Baicheng Weng & Zhilong Song & Rilong Zhu & Qingyu Yan & Qingde Sun & Corey G. Grice & Yanfa Yan & Wan-Jian Yin, 2020. "Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    6. Xu, Ying & Wang, Jiming & Zhang, Guojie & Zhang, Xiaodi & Qin, Xiaowei & Zhang, Yongfa, 2022. "Evaluation of hydrothermal treatment on physicochemical properties and re-adsorption behaviors of lignite," Energy, Elsevier, vol. 244(PA).
    7. Léonard Tschora & Erwan Pierre & Marc Plantevit & Céline Robardet, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Post-Print hal-03621974, HAL.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yuan, Zhiyuan & Guo, Yongsheng & Fang, Wenjun, 2025. "Combining multilevel calculation protocol and QSAR model to predict the activity of phenolic antioxidants suitable for high-energy-density fuels at different temperatures," Energy, Elsevier, vol. 320(C).
    2. Yao, Yecheng & Wei, Guoqiang & Yuan, Haoran & Yang, Xixian & Huang, Zhen & Chen, Liangyong & Xie, Jun, 2025. "Investigation of chemical looping pyrolysis characteristics of biogas residue through experiments, kinetic modeling and machine learning," Energy, Elsevier, vol. 316(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Karol Pilot & Alicja Ganczarek-Gamrot & Krzysztof Kania, 2024. "Dealing with Anomalies in Day-Ahead Market Prediction Using Machine Learning Hybrid Model," Energies, MDPI, vol. 17(17), pages 1-20, September.
    2. Chibuike Chiedozie Ibebuchi, 2025. "Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors," Forecasting, MDPI, vol. 7(2), pages 1-16, April.
    3. Wu, Han & Du, Pei, 2024. "Dual-stream transformer-attention fusion network for short-term carbon price prediction," Energy, Elsevier, vol. 311(C).
    4. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    5. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique," Energies, MDPI, vol. 16(18), pages 1-23, September.
    6. Hilger, Hannes & Witthaut, Dirk & Dahmen, Manuel & Rydin Gorjão, Leonardo & Trebbien, Julius & Cramer, Eike, 2024. "Multivariate scenario generation of day-ahead electricity prices using normalizing flows," Applied Energy, Elsevier, vol. 367(C).
    7. Aliyon, Kasra & Ritvanen, Jouni, 2024. "Deep learning-based electricity price forecasting: Findings on price predictability and European electricity markets," Energy, Elsevier, vol. 308(C).
    8. Loizidis, Stylianos & Kyprianou, Andreas & Georghiou, George E., 2024. "Electricity market price forecasting using ELM and Bootstrap analysis: A case study of the German and Finnish Day-Ahead markets," Applied Energy, Elsevier, vol. 363(C).
    9. van Zyl, Corne & Ye, Xianming & Naidoo, Raj, 2024. "Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP," Applied Energy, Elsevier, vol. 353(PA).
    10. Zhang, Xiaokong & Chai, Jian & Tian, Lingyue & Yang, Ying & Zhang, Zhe George & Pan, Yue, 2023. "Forecast and structural characteristics of China's oil product consumption embedded in bottom-line thinking," Energy, Elsevier, vol. 278(PA).
    11. Zhang, Yuerong & Kamargianni, Maria & Cheng, Long & De Vos, Jonas & Cao, Mengqiu, 2024. "Evaluating the accessibility of on-street household electric vehicle charging stations in London: Policy insights from equity analysis across emission zones," Energy Policy, Elsevier, vol. 195(C).
    12. Ciaran O’Connor & Mohamed Bahloul & Steven Prestwich & Andrea Visentin, 2025. "A Review of Electricity Price Forecasting Models in the Day-Ahead, Intra-Day, and Balancing Markets," Energies, MDPI, vol. 18(12), pages 1-40, June.
    13. Aliyon, Kasra & Rajaee, Fatemeh & Ritvanen, Jouni, 2023. "Use of artificial intelligence in reducing energy costs of a post-combustion carbon capture plant," Energy, Elsevier, vol. 278(PA).
    14. Sai, Wei & Pan, Zehua & Liu, Siyu & Jiao, Zhenjun & Zhong, Zheng & Miao, Bin & Chan, Siew Hwa, 2023. "Event-driven forecasting of wholesale electricity price and frequency regulation price using machine learning algorithms," Applied Energy, Elsevier, vol. 352(C).
    15. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
    16. Paweł Pijarski & Adrian Belowski, 2024. "Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 17(2), pages 1-42, January.
    17. Deniz Kenan Kılıç & Peter Nielsen & Amila Thibbotuwawa, 2024. "Intraday Electricity Price Forecasting via LSTM and Trading Strategy for the Power Market: A Case Study of the West Denmark DK1 Grid Region," Energies, MDPI, vol. 17(12), pages 1-15, June.
    18. Mao, Xuehui & Chen, Shanlin & Yu, Hanxin & Duan, Liwu & He, Yingjie & Chu, Yinghao, 2025. "Simplicity in dynamic and competitive electricity markets: A case study on enhanced linear models versus complex deep-learning models for day-ahead electricity price forecasting," Applied Energy, Elsevier, vol. 383(C).
    19. Wu, Han & Liang, Yan & Gao, Xiao-Zhi & Du, Pei, 2024. "Auditory-circuit-motivated deep network with application to short-term electricity price forecasting," Energy, Elsevier, vol. 288(C).
    20. Huang, Siwan & Shi, Jianheng & Wang, Baoyue & An, Na & Li, Li & Hou, Xuebing & Wang, Chunsen & Zhang, Xiandong & Wang, Kai & Li, Huilin & Zhang, Sui & Zhong, Ming, 2024. "A hybrid framework for day-ahead electricity spot-price forecasting: A case study in China," Applied Energy, Elsevier, vol. 373(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:300:y:2024:i:c:s0360544224013872. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.