The paper presents a simulation analysis of four control systems of the raw coal feed to a jig: stabilization of the volumetric flow of the feed, stabilization of the feed tonnage, stabilization of the feed flow with the additional measurement of the feed bulk density or the additional measurement of ash content in the feed. Analysis has been performed for the first and second compartments of a jig. The aim of the feed control was to stabilize the mass of the bed in the zone where the material stratifies; the mass may change due to changes in the washability characteristics of the feed. Such control should result in stable conditions in which material loosens during subsequent media pulsation cycles; stabilizing conditions minimizes the dispersion of coal particles in the bed. The best results have been achieved for the system of feed control where the ash content was measured in the first compartment, and for feed tonnage control in the second compartment.

The purpose of the work was to predict the selected product parameters of the dry separation process using a pneumatic sorter. From the perspective of application of coal for energy purposes, determination of process parameters of the output as: ash content, moisture content, sulfur content, calorific value is essential. Prediction was carried out using chosen machine learning algorithms that proved to be effective in forecasting output of various technological processes in which the relationships between process parameters are non-linear. The source of data used in the work were experiments of dry separation of coal samples. Multiple linear regression was used as the baseline predictive technique. The results showed that in the case of predicting moisture and sulfur content this technique was sufficient. The more complex machine learning algorithms like support vector machine (SVM) and multilayer perceptron neural network (MPL) were used and analyzed in the case of ash content and calorific value. In addition, k-means clustering technique was applied. The role of cluster analysis was to obtain additional information about coal samples used as feed material. The combination of techniques such as multilayer perceptron neural network (MPL) or support vector machine (SVM) with k-means allowed for the development of a hybrid algorithm. This approach has significantly increased the effectiveness of the predictive models and proved to be a useful tool in the modeling of the coal enrichment process.