Mishra Sameeksha, Prasad Anup K, Vinod Arya, Shukla Anubhav, Mukherjee Shailayee, Purkait Bitan, Varma Atul K, Sarkar Bhabesh C
Photogeology and Image Processing Laboratory, Department of Applied Geology, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India.
Geocomputational & GIS Laboratory, Department of Applied Geology, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India.
Sci Rep. 2025 Apr 21;15(1):13786. doi: 10.1038/s41598-025-98071-3.
The ash yield resulting from the alteration of inorganic elements during the processes of combustion and gasification of coal stands as a crucial quality indicator for coal. Ash yield, along with calorific value, determines the commercial rating, ranking, and industrial usage of coal. Traditional methods of determining the ash yield in coal as per proximate analysis protocols are tedious and time-consuming as they involve the combustion of coal samples. A novel approach that uses mid-infrared Fourier Transform Infrared spectroscopy (FTIR) (optical technique) data in the range of 1450-350 cm to identify spectrally sensitive zones (fourteen selective absorption bands) and to predict the ash yield in coal samples is presented. Multiple algorithms, including piecewise linear regression (PLR), artificial neural networks (ANN), partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF), were utilized to predict the ash yield in coal. The present study suggests a multi-model estimation (MME) approach, using the average of the best three models (PLR, PLSR, and ANN) to achieve greater accuracy and robustness. This method outperforms individual models with a coefficient of determination - R-squared (R) of 0.883, Root Mean Square Error (RMSE) of 3.059 wt%, RMSE in percentage (RMSE%) of 30.080, Mean Bias Error in percentage (MBE%) of 3.694, and Mean Absolute Error (MAE) of 2.249 wt%. The two-tailed t-test and F-test for mean and variance (99% Confidence Interval, CI) show no significant difference between the proximate analysis-derived ash yield and the multi-model estimated ash yield using FTIR data. FTIR spectroscopy data can accurately predict the ash yield in coal and perform well for coal samples from Johilla Coalfield, Umaria, Madhya Pradesh. The present model using FTIR analysis is a potential industrial tool for the quick determination of ash yield in coal and can be further improved by including data from other basins worldwide.
煤在燃烧和气化过程中无机元素变化所产生的灰分产率是煤炭的一项关键质量指标。灰分产率与热值一起决定了煤炭的商业评级、排名和工业用途。按照近似分析规程测定煤中灰分产率的传统方法既繁琐又耗时，因为它们涉及煤样的燃烧。本文提出了一种新颖的方法，该方法利用中红外傅里叶变换红外光谱（FTIR）（光学技术）在1450 - 350厘米范围内的数据来识别光谱敏感区（14个选择性吸收带）并预测煤样中的灰分产率。多种算法，包括分段线性回归（PLR）、人工神经网络（ANN）、偏最小二乘回归（PLSR）、支持向量回归（SVR）和随机森林（RF），被用于预测煤中的灰分产率。本研究提出了一种多模型估计（MME）方法，使用最佳的三个模型（PLR、PLSR和ANN）的平均值来实现更高的准确性和稳健性。该方法优于单个模型，其决定系数 - 决定系数（R）为0.883，均方根误差（RMSE）为3.059 wt%，百分比均方根误差（RMSE%）为30.080，百分比平均偏差误差（MBE%）为3.694，平均绝对误差（MAE）为2.249 wt%。均值和方差的双尾t检验和F检验（99%置信区间，CI）表明，近似分析得出的灰分产率与使用FTIR数据的多模型估计灰分产率之间没有显著差异。FTIR光谱数据可以准确预测煤中的灰分产率，并且对于来自中央邦乌玛里亚乔希拉煤田的煤样表现良好。使用FTIR分析的当前模型是一种快速测定煤中灰分产率的潜在工业工具，通过纳入来自全球其他盆地的数据可以进一步改进。
