A machine learning approach for significant utilization of high-ash Indian coals by metal chloride modification
Abstract A continuous and dependable energy supply is essential for maintaining a nation’s economic stability. Globally, coal ranks as the second largest fossil fuel resource after oil and gas, leading to the establishment of coal-fired power infrastructure. Nonetheless, the pyrolysis and “burn-out”...
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Nature Portfolio
2025-08-01
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| Online Access: | https://doi.org/10.1038/s41598-025-12065-9 |
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| author | Aparna Singh Deepak Singh Panwar Satish Kumar Maurya Ajay Oraon Sanjeev Srivastava Md Irfanul Haque Siddiqui Saurav Dixit Choon Kit Chan Chandrakant Sonawane |
| author_facet | Aparna Singh Deepak Singh Panwar Satish Kumar Maurya Ajay Oraon Sanjeev Srivastava Md Irfanul Haque Siddiqui Saurav Dixit Choon Kit Chan Chandrakant Sonawane |
| author_sort | Aparna Singh |
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| description | Abstract A continuous and dependable energy supply is essential for maintaining a nation’s economic stability. Globally, coal ranks as the second largest fossil fuel resource after oil and gas, leading to the establishment of coal-fired power infrastructure. Nonetheless, the pyrolysis and “burn-out” reactions of High-ash coal impose fundamental limitations that hinder its efficient use and exacerbate environmental degradation. Coal pyrolysis processes is significantly influenced by numerous experimental factors, including the, chemical concentration, operating temperature, process time. A significant weight loss was seen for periods of up to 30 min at 510 °C; yet, the change in responsiveness reduced after this time. It was found that as an increasing the concentration of SnCl2 causes a remarkable burn-out increase, up to 9%, whilst at lower concentrations a consistent temperature and pyrolysis time shows a considerable decrease. At 610 and 710 °C, 9% SnCl2-impregnated coal. In present investigation Artificial Neural Networks and Response Surface Methodology employed to envisage the percentage of burn-out of High-ash coal. The sensitivity analyses indicated that the pyrolysis temperature stands out as the most significant input parameter, with pyrolysis time and catalyst concentration following closely behind. The ANN and RSM techniques were employed to forecast the burn-out percentage of High-ash coal. The ANN (R2 = 0.9965) indicates superior predictability compared to RSM. |
| format | Article |
| id | doaj-art-43ee62b0d5524ac0be6788be00aae70c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-43ee62b0d5524ac0be6788be00aae70c2025-08-20T03:42:45ZengNature PortfolioScientific Reports2045-23222025-08-0115111310.1038/s41598-025-12065-9A machine learning approach for significant utilization of high-ash Indian coals by metal chloride modificationAparna Singh0Deepak Singh Panwar1Satish Kumar Maurya2Ajay Oraon3Sanjeev Srivastava4Md Irfanul Haque Siddiqui5Saurav Dixit6Choon Kit Chan7Chandrakant Sonawane8WA School of Mines, Minerals, Energy and Chemical Engineering, Curtin UniversityDepartment of Chemical Engineering, P P Savni UniversityDepartment of Computer Engineering and Applications, G.L.A. UniversityDepartment of Chemical Engineering, BIT SindriDepartment of Mechanical Engineering, DAV, IETDepartment of Mechanical Engineering, College of Engineering, King Saud UniversityCentre of Research Impact and Outcome, Chitkara UniversityFaculty of Engineering and Quantity Surveying, INTI International UniversityDepartment of Mechanical Engineering, Symbiosis International UniversityAbstract A continuous and dependable energy supply is essential for maintaining a nation’s economic stability. Globally, coal ranks as the second largest fossil fuel resource after oil and gas, leading to the establishment of coal-fired power infrastructure. Nonetheless, the pyrolysis and “burn-out” reactions of High-ash coal impose fundamental limitations that hinder its efficient use and exacerbate environmental degradation. Coal pyrolysis processes is significantly influenced by numerous experimental factors, including the, chemical concentration, operating temperature, process time. A significant weight loss was seen for periods of up to 30 min at 510 °C; yet, the change in responsiveness reduced after this time. It was found that as an increasing the concentration of SnCl2 causes a remarkable burn-out increase, up to 9%, whilst at lower concentrations a consistent temperature and pyrolysis time shows a considerable decrease. At 610 and 710 °C, 9% SnCl2-impregnated coal. In present investigation Artificial Neural Networks and Response Surface Methodology employed to envisage the percentage of burn-out of High-ash coal. The sensitivity analyses indicated that the pyrolysis temperature stands out as the most significant input parameter, with pyrolysis time and catalyst concentration following closely behind. The ANN and RSM techniques were employed to forecast the burn-out percentage of High-ash coal. The ANN (R2 = 0.9965) indicates superior predictability compared to RSM.https://doi.org/10.1038/s41598-025-12065-9High-ash coalPyrolysis% Burn-outTGAANNRSM |
| spellingShingle | Aparna Singh Deepak Singh Panwar Satish Kumar Maurya Ajay Oraon Sanjeev Srivastava Md Irfanul Haque Siddiqui Saurav Dixit Choon Kit Chan Chandrakant Sonawane A machine learning approach for significant utilization of high-ash Indian coals by metal chloride modification Scientific Reports High-ash coal Pyrolysis % Burn-out TGA ANN RSM |
| title | A machine learning approach for significant utilization of high-ash Indian coals by metal chloride modification |
| title_full | A machine learning approach for significant utilization of high-ash Indian coals by metal chloride modification |
| title_fullStr | A machine learning approach for significant utilization of high-ash Indian coals by metal chloride modification |
| title_full_unstemmed | A machine learning approach for significant utilization of high-ash Indian coals by metal chloride modification |
| title_short | A machine learning approach for significant utilization of high-ash Indian coals by metal chloride modification |
| title_sort | machine learning approach for significant utilization of high ash indian coals by metal chloride modification |
| topic | High-ash coal Pyrolysis % Burn-out TGA ANN RSM |
| url | https://doi.org/10.1038/s41598-025-12065-9 |
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