Use of Artificial Intelligence in Improving Coal Processing

Abstract

The environmental impact of coal processing is considered. Means of reducing the environmental impact of coal combustion are discussed. Attention focuses on supercritical pyrolysis, whose modification could significantly decrease harmful emissions. An interesting possibility is the use of data augmentation by means of artificial intelligence (AI) to improve a model developed for supercritical pyrolysis on the basis of the Monte Carlo method. That permits modeling and the generation of similar model data. The use of neural networks to control and optimize supercritical pyrolysis is analyzed, especially when the available information is limited. The results of augmentation to expand and improve the initial data set are noted. It must be stressed that the use of neural networks for this purpose requires the selection of the correct approach and adjustment of the models in accordance with the specifics of the problem and the characteristics of the data. It is concluded that, despite significant limitations, neural networks may be an effective tool for optimizing industrial processes. The proposed approaches offer new means of decreasing the environmental impact of coal processing and improving its profitability and may be used in various industrial sectors.