Process control within a coal processing plant requires rapid and reliable intervention to the operation. Ash content has paramount importance for the identification of a particular lignite’s quality. Among the methods used for on-line content determination, efforts covering the ash content estimation at the processed particle size ranges and ensuring fast responses are more favorable. The aim of this study is to investigate the applicability of the imaging to estimate the ash content of a particular lignite sample. Experimental study was powered by filtered and unfiltered imaging in visible and near-infrared ranges, determination of the reflectance values distribution of individual particles, and proposing the best linear model to define the proximate ash content by using stepwise multiple linear regression (SMLR) followed by a statistical validation. Results showed that ash content could be estimated with a determination coefficient up to 87% by using the best linear SMLR correlation formed with reflectance values of individual particles.

煤炭加工厂内的过程控制要求对操作进行快速而可靠的干预。灰分含量对于识别特定褐煤的质量至关重要。在用于在线含量测定的方法中，覆盖处理后的粒度范围内的灰分含量估算并确保快速响应的工作更加有利。这项研究的目的是调查成像的适用性，以估计特定褐煤样品的灰分含量。实验研究由可见光和近红外范围的过滤和未过滤成像，确定单个颗粒的反射率值分布，并提出最佳的线性模型，以通过逐步多元线性回归（SMLR）定义最近的灰分含量，然后进行统计验证。结果表明，通过使用由单个颗粒的反射率值形成的最佳线性SMLR相关性，可以以高达87％的测定系数估算灰分含量。