Rapid Analysis of Composition of Coal Gangue Based on Deep Learning and Thermal Infrared Spectroscopy
نویسندگان
چکیده
Coal gangue is the main solid waste in coal mining areas, and its annual emissions account for about 10% of production. The composition information basis reasonable utilization gangue, according to one can choose appropriate application scene. not only effectively alleviate environmental problems areas but also produce significant economic social benefits. Chemical analysis techniques are principal ones used traditional analysis; however, they slow expensive. Many researchers have machine learning analyze spectral data primarily random forests (RFs), extreme machines (ELMs), two-hidden-layer (TELMs). However, these heavily reliant on preprocessing data. This research suggests a quick approach based thermal infrared spectroscopy deep light drawbacks aforementioned methodologies. proposed model named SR-TELM, which extracts features using convolutional neural network (CNN) consisting spatial attention mechanism residual connections implements content prediction with TELM as regressor, overcome dependence preprocessing. usefulness speed SR-TELM were demonstrated by comparing several models order verify model. experimental findings show that, tasks moisture, ash, volatile matter, fixed carbon content, respectively, attained an R2 0.947, 0.972, 0.967, 0.981 RMSE 0.274, 4.040, 1.567, 2.557 test time just 0.03 s. It offers method that low cost, highly effective, reliable.
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ژورنال
عنوان ژورنال: Sustainability
سال: 2022
ISSN: ['2071-1050']
DOI: https://doi.org/10.3390/su142316210