Coal is one of the world's most important energy substances. China is rich in coal resources, accounting for more than 90 % of all ascertained fossil energy reserves. The consumption share of coal energy reaches 56.5 % in 2021. Due to the high moisture content of low-rank coal, it is easy to cause equipment blockage in the dry sorting process. This paper considers low-rank coal coming from Inner Mongolia (NM samples) and Yunnan (YN samples). The weight loss performance of the samples was analyzed using thermogravimetric experiments to determine the appropriate temperature for drying experiments. Thin-layer drying experiments were carried out at different temperature conditions. The drying characteristics of low-rank coal were that the higher the drying temperature, the shorter the drying completion time; the smaller the particle size, the shorter the drying completion time. The effective moisture diffusion coefficient was fitted using the Arrhenius equation. The effective water diffusion coefficient of NM samples was 5.07·10–11 - 9.58·10–11 m2/s. The effective water diffusion coefficients of the three different particle sizes of YN samples were 1.89·10–11 - 4.92·10–11 (–1 mm), 1.38·10–10 - 4.13·10–10 (1-3 mm), 5.26·10–10 - 1.49·10–9 (3-6 mm). The activation energy of Inner Mongolia lignite was 10.97 kJ/mol (–1 mm). The activation energies of Yunnan lignite with different particle sizes were 17.97 kJ/mol (–1 mm), 33.52 kJ/mol (1-3 mm), and 38.64 kJ/mol (3-6 mm). The drying process was simulated using empirical and semi-empirical formulas. The optimal model for Inner Mongolia samples was the Two-term diffusion model, and Yunnan samples were the Hii equation was used.
Real-time testing of coal ash plays a vital role in the chemical, power generation, metallurgical, and coal separation sectors. The rapid online testing of coal ash using radiation measurement as the mainstream technology has problems such as strict coal sample requirements, poor radiation safety, low accuracy, and complicated equipment replacement. In this study, an intelligent detection technique based on feed-forward neural networks and improved particle swarm optimization (IPSO-FNN) is proposed to predict coal quality ash content in a fast, accurate, safe，and convenient manner. The data set was obtained by testing the elemental content of 198 coal samples with X-ray fluorescence (XRF). The types of input elements for machine learning (Si, Al, Fe, K, Ca, Mg, Ti, Zn, Na, P) were determined by combining the X-ray photoelectron spectroscopy (XPS) data with the change in the physical phase of each element in the coal samples during combustion. The mean squared error and coefficient of determination were chosen as the performance measures for the model. The results show that the IPSO algorithm is useful in adjusting the optimal number of nodes in the hidden layer. The IPSO-FNN model has strong prediction ability and good accuracy in coal ash prediction. The effect of the input element content of the IPSO-FNN model on the ash content was investigated, and it was found that the potassium content was the most significant factor affecting the ash content. This study is essential for real-time online, accurate, and fast prediction of coal ash.
