The coal fed to gravity enrichment consists of coals coming from different deposits and exploitation fronts. These coals differ in quality parameters, especially the amount of gangue (stone) changing over time. This results in the instability of work, especially jiggers, which have a relatively low accuracy assessed by probable scattering or imperfection rates. This deteriorates the quality of the concentrate obtained, the quality parameters of which change over time. The improvement of jiggers work would be possible by averaging the feed. This process is practically impossible due to the failure to design such a node during plant construction, which are, in most cases, directly related to the shaft. In the article, the authors propose to solve the process of averaging the feed before directing it to the enrichment process in jiggers by introducing its deshaling in vibratory- air separators of the FGX type.
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.