The proximate analysis of coal has been widely utilized as the basis for determining the rank of coal which is in connection with coal price and utilization. However, these determinations are time consuming and require various laboratory equipment. To address this concern, we propose a novel strategy for proximate analysis based on near-infrared spectroscopy and an MOA-Unet. The proposed method is able to simultaneously predict the moisture, ash, volatile content, and calorific value with correlation coefficients of 0.9015, 0.9538, 0.8986, and 0.8884. The required time is significantly shortened from 4 hours per sample of traditional proximate analysis to 19 ms per sample.

Proximate analysis of coal indicates the moisture, ash, volatile content, and calorific value, which has been widely utilized as the basis for coal characterization. It involves heating the coal under various conditions until a constant weight is obtained. Although it is a relatively simple process that does not require expensive analytical equipment, determining these characteristics is time consuming. An alternative way for proximate analysis is spectral analysis in combination with various machine learning methods. However, most previous works analyze individual characteristics and fail to explore the relationship among them. In this study, we propose a method for proximate analysis based on near-infrared spectroscopy and a multioutput attention Unet (MOA-Unet), which can predict multiple characteristics simultaneously. First, an attention-based Unet is designed as the shared feature extraction subnetwork, including an encoder, a decoder, convolutional block attention modules, and m...