ABSTRACT

Low-grade coals are blended with high-quality coals to meet economic, environmental, and quality specifications. Hence, the grindability and abrasiveness of coal blends are crucial economic and operational parameters. This work evaluates, analyzes, and predicts the grindability and abrasive behavior of coal blends. Three binary coal blends with common low-grade coal were first prepared at various ratios. Blends 1 and 2 were composed of identical and similar ranks, whereas Blend 3 was composed of different ranks. The blends were analyzed using proximate, ultimate analyzers, and a Bomb calorimeter. The grindability and abrasive behavior of the blends were measured using Hardgrove grindability index (HGI) and Yancey, Geer, and Price methods, respectively. Further, the coarser (+75μm) and finer (−75μm) fractions of HGI experiment were characterized using proximate, ultimate and heating value analyses. The additivity of HGI values was observed for Blend 1 and Blend 2, whereas, the non-additive behavior was observed in Blend 3. Further, the blends’ mineral matter contents and abrasiveness index were found to be additive. Several existing models were found to be inaccurate for HGI predictions. Therefore, a new cross-validated model using multi-linear regression was proposed. The model exhibited better HGI predictions of coal blends with a coefficient of determination R² = 0.9416.

摘要

低品位煤与优质煤混合以满足经济、环境和质量规格。因此，混煤的可磨性和磨蚀性是至关重要的经济和操作参数。这项工作评估、分析和预测混合煤的可磨性和磨料行为。首先以不同的比例制备了三种含有普通低品位煤的二元混煤。混合 1 和 2 由相同和相似的等级组成，而混合 3 由不同的等级组成。使用近似的极限分析仪和 Bomb 热量计分析混合物。共混物的可磨性和磨料行为分别使用 Hardgrove 可磨性指数 (HGI) 和 Yancey、Geer 和 Price 方法进行测量。更远，使用近似值、最终值和热值分析来表征 HGI 实验的较粗 (+75μm) 和较细 (-75μm) 部分。在混合物 1 和混合物 2 中观察到 HGI 值的相加性，而在混合物 3 中观察到非相加行为。此外，发现混合物的矿物质含量和磨损指数是相加的。一些现有的模型被发现对于 HGI 预测是不准确的。因此，提出了一种使用多元线性回归的新交叉验证模型。该模型表现出更好的混合煤 HGI 预测，确定系数为 R 共混物的矿物质含量和磨损指数被发现是相加的。一些现有的模型被发现对于 HGI 预测是不准确的。因此，提出了一种使用多元线性回归的新交叉验证模型。该模型表现出更好的混合煤 HGI 预测，确定系数为 R 共混物的矿物质含量和磨损指数被发现是相加的。一些现有的模型被发现对于 HGI 预测是不准确的。因此，提出了一种使用多元线性回归的新交叉验证模型。该模型表现出更好的混合煤 HGI 预测，确定系数为 R²  = 0.9416。