Singh Aparna, Panwar Deepak Singh, Maurya Satish Kumar, Oraon Ajay, Srivastava Sanjeev, Siddiqui Md Irfanul Haque, Dixit Saurav, Chan Choon Kit, Sonawane Chandrakant
WA School of Mines, Minerals, Energy and Chemical Engineering, Curtin University, Perth, Australia.
Department of Chemical Engineering, P P Savni University, Surat, Gujarat, India.
Sci Rep. 2025 Aug 2;15(1):28235. doi: 10.1038/s41598-025-12065-9.
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 SnCl 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% SnCl-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 (R = 0.9965) indicates superior predictability compared to RSM.
持续可靠的能源供应对于维持一个国家的经济稳定至关重要。在全球范围内，煤炭是仅次于石油和天然气的第二大化石燃料资源，这促使了燃煤发电基础设施的建立。然而，高灰分煤的热解和“燃尽”反应存在一些基本限制，阻碍了其有效利用，并加剧了环境恶化。煤炭热解过程受到众多实验因素的显著影响，包括化学浓度、操作温度、处理时间等。在510°C下长达30分钟的时间内观察到显著的重量损失；然而，在此之后反应性的变化有所降低。研究发现，随着SnCl浓度的增加，燃尽率显著提高，最高可达9%，而在较低浓度下，恒定的温度和热解时间则显示出显著降低。在610°C和710°C下，9% SnCl浸渍煤。在本研究中，采用人工神经网络和响应面方法来预测高灰分煤的燃尽率。敏感性分析表明，热解温度是最显著的输入参数，热解时间和催化剂浓度紧随其后。采用人工神经网络和响应面方法预测高灰分煤的燃尽率。人工神经网络（R = 0.9965）显示出比响应面方法更好的预测能力。
