EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 354 (2026) EPJ Web Conf., 354 (2026) 02001 References
Open Access
Issue
EPJ Web Conf.
Volume 354, 2026
19th Global Congress on Manufacturing and Management (GCMM 2025)
Article Number 02001
Number of page(s) 18
Section Artificial Intelligence, Machine Learning, and Intelligent Decision Systems
DOI https://doi.org/10.1051/epjconf/202635402001
Published online 02 March 2026
  1. S. Mittal, S. Pathak, H. Dhawan, and S. Upadhyayula, "A machine learning approach to improve ignition properties of high-ash Indian coals by solvent extraction and coal blending," Chemical Engineering Journal, vol. 413, Jun. 2021, doi: 10.1016/j.cej.2020.127385. [Google Scholar]
  2. B. Brooks, S. K. Rish, H. Lomas, A. Jayasekara, and A. Tahmasebi, "Advances in low carbon cokemaking - Influence of alternative raw materials and coal properties on coke quality," Aug. 01, 2023, ElsevierB.V. doi: 10.1016/j.jaap.2023.106083. [Google Scholar]
  3. R. Sakurovs, "Interactions between coking coals in blends q," 2002. [Online]. Available: www.fuelfirst.com [Google Scholar]
  4. L. Meng, W. Zhu, N. Xu, and J. C. Hower, "Explainable AI for coal Hardgrove grindability index prediction: a genetic programming and linear regression approach," Fuel, vol. 404, Jan. 2026, doi: 10.1016/j.fuel.2025.136072. [Google Scholar]
  5. R. Sakurovs, "Interactions between coking coals in blends q," 2002. [Online]. Available: www.fuelfirst.com [Google Scholar]
  6. R. Roest, H. Lomas, and M. R. Mahoney, "Fractographic approach to metallurgical coke failure analysis. Part 2: Cokes from binary coal blends," Fuel, vol. 180, pp. 794–802, Sep. 2016, doi: 10.1016/j.fuel.2016.04.022. [Google Scholar]
  7. R. Roest, H. Lomas, K. Hockings, and M. R. Mahoney, "Fractographic approach to metallurgical coke failure analysis. Part 1: Cokes of single coal origin," Fuel, vol. 180, pp. 785–793, Sep. 2016, doi: 10.1016/j.fuel.2016.01.058. [Google Scholar]
  8. S. Gupta, F. Shen, W. J. Lee, and G. O'Brien, "Improving coke strength prediction using automated coal petrography," Fuel, vol. 94, pp. 368–373, Apr. 2012, doi: 10.1016/j.fuel.2011.09.045. [Google Scholar]
  9. K. Warren, G. Krahenbuhl, M. Mahoney, G. O'Brien, and P. Hapugoda, "Estimating the fusible content of individual coal grains and its application in coke making," Int J Coal Geol, vol. 152, pp. 3–9, Nov. 2015, doi: 10.1016/j.coal.2015.09.012. [Google Scholar]
  10. B. D. Flores et al., "How coke optical texture became a relevant tool for understanding coal blending and coke quality," Fuel Processing Technology, vol. 164, pp. 13–23, 2017, doi: 10.1016/j.fuproc.2017.04.015. [Google Scholar]
  11. B. Cui, B. Wu, M. Wang, X. Jin, Y. Shen, and L. Chang, "A preliminary study on the quality evaluation of coking coal from its structure thermal transformation: Applications of fluidity and swelling indices," Fuel, vol. 355, Jan. 2024, doi: 10.1016/j.fuel.2023.129418. [Google Scholar]
  12. J. Guo et al., "Impact of chemical structure of coal on coke quality produced by coals in the similar category," J Anal Appl Pyrolysis, vol. 162, Mar. 2022, doi: 10.1016/j.jaap.2022.105432. [Google Scholar]
  13. J. J. Duffy, M. R. Mahoney, and K. M. Steel, "Influence of coal thermoplastic properties on coking pressure generation: Part 2 - A study of binary coal blends and specific additives," Fuel, vol. 89, no. 7, pp. 1600–1615, Jul. 2010, doi: 10.1016/j.fuel.2009.08.035. [Google Scholar]
  14. N. Guelton, "The prediction of the Gieseler characteristics of coal blends," Fuel, vol. 209, pp. 661–673, Dec. 2017, doi: 10.1016/j.fuel.2017.07.017. [Google Scholar]
  15. F. Gayo, R. Garcia, and M. A. Diez, "Modelling the Gieseler fluidity of coking coals modified by multicomponent plastic wastes," Fuel, vol. 165, pp. 134–144, Feb. 2016, doi: 10.1016/j.fuel.2015.10.053. [Google Scholar]
  16. M. Rzychon, A. Zogala, and L. Rog, "Experimental study and extreme gradient boosting (XGBoost) based prediction of caking ability of coal blends," J Anal Appl Pyrolysis, vol. 156, Jun. 2021, doi: 10.1016/j.jaap.2021.105020. [Google Scholar]
  17. A. Homafar, H. Nasiri, and S. C. Chelgani, "Modeling coking coal indexes by SHAP-XGBoost: Explainable artificial intelligence method," Fuel Communications, vol. 13, p. 100078, Dec. 2022, doi: 10.1016/j.jfueco.2022.100078. [Google Scholar]
  18. Y. Qiu et al., "A novel image expression-driven modeling strategy for coke quality prediction in the smart cokemaking process," Energy, vol. 294, May 2024, doi: 10.1016/j.energy.2024.130866. [Google Scholar]
  19. Y. Qiu et al., "The interpretable deep learning-based coke quality prediction strategy aligned with domain knowledge for sustainable smart cokemaking applications," Fuel, vol. 398, Oct. 2025, doi: 10.1016/j.fuel.2025.135533. [Google Scholar]
  20. Y. Lei, Y. Chen, J. Chen, X. Liu, X. Wu, and Y. Chen, "A novel modeling strategy for the prediction on the concentration of H2 and CH4 in raw coke oven gas," Energy, vol. 273, Jun. 2023, doi: 10.1016/j.energy.2023.127126. [Google Scholar]
  21. Y. Ji, G. Sheng, Q. Zhang, and J. Shen, "Energy consumption simulation and diagnosis in the coking process: A mechanism and data-driven approach," Energy for Sustainable Development, vol. 88, Oct. 2025, doi: 10.1016/j.esd.2025.101763. [Google Scholar]
  22. Y. jie Zhao, J. cheng Wang, and Q. Yi, "Bridging uncertainty gaps with artificial intelligence-assisted syngas precise prediction in coal gasification," Chem Eng Sci, vol. 301, Jan. 2025, doi: 10.1016/j.ces.2024.120734. [Google Scholar]
  23. Y. Yuan, Q. Qu, L. Chen, and M. Wu, "Modeling and optimization of coal blending and coking costs using coal petrography," Inf Sci (N Y), vol. 522, pp. 49–68, Jun. 2020, doi: 10.1016/j.ins.2020.02.072. [Google Scholar]
  24. M. Liu, Z. Yu, B. Li, Q. Wang, H. Ren, and D. Xu, "Coal allocation optimization based on a hybrid residual prediction model with an improved genetic algorithm," Eng Appl Artif Intell, vol. 137, Nov. 2024, doi: 10.1016/j.engappai.2024.109072. [Google Scholar]
  25. Z. Li, L. Liu, Z. Zhao, H. Jin, and Y. Zhuo, "Optimizing coal blending for metallurgical coke production using a reinforcement learning-enhanced evolutionary algorithm," Fuel, vol. 406, Feb. 2026, doi: 10.1016/j.fuel.2025.136777. [Google Scholar]
  26. Q. Yang, D. Rong, Q. Guo, R. Bao, and D. Zhang, "Ensemble learning-driven multi-objective optimization of the co-pyrolysis process of biomass and coal for high economic and environmental performance," Chin J Chem Eng, Aug. 2025, doi: 10.1016/j.cjche.2025.06.001. [Google Scholar]
  27. H. Qi et al., "An optimized machine learning framework for prediction of coal abrasive index: Leveraging supervised learning, metaheuristic optimization, and interpretability analysis," Fuel, vol. 403, Jan. 2026, doi: 10.1016/j.fuel.2025.136065. [Google Scholar]
  28. W. jia Hu et al., "Relevance between various phenomena during coking coal carbonization. Part 3: Understanding the properties of the plastic layer during coal carbonization," Fuel, vol. 292, May 2021, doi: 10.1016/j.fuel.2021.120371. [Google Scholar]
  29. S. Pusz and R. Buszko, "Reflectance parameters of cokes in relation to their reactivity index (CRI) and the strength after reaction (CSR), from coals of the Upper Silesian Coal Basin, Poland," Int J Coal Geol, vol. 90-91, pp. 43–49, Feb. 2012, doi: 10.1016/j.coal.2011.10.008. [Google Scholar]
  30. A. Mohanty, S. Chakladar, S. Mallick, and S. Chakravarty, "Structural characterization of coking component of an Indian coking coal," Fuel, vol. 249, pp. 411–417, Aug. 2019, doi: 10.1016/j.fuel.2019.03.108. [Google Scholar]
  31. M. A. Diez and A. G. Borrego, "Evaluation of CO2-reactivity patterns in cokes from coal and woody biomass blends," Fuel, vol. 113, pp. 59–68, 2013, doi: 10.1016/j.fuel.2013.05.056. [Google Scholar]
  32. Y. Mochizuki, Y. Wang, S. Konno, T. Shishido, and N. Tsubouchi, "Analysis of swelling in coke formed by carbonization of low-quality coal in a pressurized atmosphere," Carbon Resources Conversion, 2025, doi: 10.1016/j.crcon.2025.100370. [Google Scholar]
  33. Z. Klika, J. Serencisovâ, I. Kolomaznik, L. Bartoriovâ, and P. Baran, "Prediction of CRI and CSR of cokes by two-step correction models for stamp-charged coals - Statistical analysis," Fuel, vol. 262, Feb. 2020, doi: 10.1016/j.fuel.2019.116623. [Google Scholar]
  34. Y. jie Zhao, J. cheng Wang, and Q. Yi, "Bridging uncertainty gaps with artificial intelligence-assisted syngas precise prediction in coal gasification," Chem Eng Sci, vol. 301, Jan. 2025, doi: 10.1016/j.ces.2024.120734. [Google Scholar]
  35. Y. Qiu et al., "The novel adaptive graph neural network-based coke quality prediction for coal samples with missing properties in sustainable smart cokemaking applications," Fuel, vol. 397, Oct. 2025, doi: 10.1016/j.fuel.2025.135377. [Google Scholar]
  36. L. North, K. Blackmore, K. Nesbitt, and M. R. Mahoney, "Models of coke quality prediction and the relationships to input variables: A review," May 01, 2018, Elsevier Ltd. doi: 10.1016/j.fuel.2018.01.062. [Google Scholar]
  37. P. Zhao et al., "Surrogate model-integrated deep reinforcement learning for temperature and pyrolysis behavior control in the coal coking procs," Fuel, vol. 405, Feb 2026, doi: 10.1016/j.fuel [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.