Abstract:The paper presents a study on the effectiveness of the grinding process in an electromagnetic mill devoted to ultrafine grinding, and the influence of processing parameters on the mill's performance. The research was focused on the optimization of the duration of the grinding process and selection of the grinding media type in order to obtain the highest relative increase of the selected particle size fraction. Copper ore with a particle size between 0-1 mm was used in the experiments. A model was created that determines the relationship between the processing time and efficiency of the grinding, and can be used for the optimization of the process. A comparison of the relative growth of particle size fractions in milling products was performed. The obtained milling efficiency results measured by the growth of the analyzed particle size fraction in the milling product confirmed that the best grinding media set includes a grinding medium with a diameter of 1 mm and a length of 10 mm.

Abstract. In the past years there has been an increase in production and consumption of plastics, which are widely used in many areas of life. Waste generated from this material are a challenge for the whole of society, regardless of awareness of sustainable development and its technological progress. Still the method of disposal of plastic waste are focused mainly on their storage and incineration, not using energy contained there. In this paper technology for plastic waste depolymerization with characteristics of fuel oil resulting in the process, as an alternative to traditional energy carriers such as: coal, fine coal or coke used in households will be presented. Oil has a high calorific value and no doubt could replace traditional solutions which use conventional energy sources. Furthermore, the fuel resulting from this process is sulfur-free and chemically pure. The paper presents the installation for plastics waste depolymerization used in selected Polish Institute of Plastics Processing, along with the ability to use the main thermocatalytic transformation product.

The paper presents a way of combining neural networks with evolutionary algorithms in order to find optimal parameters of the copper flotation enrichment process. The neural network was used in order to build a model describing the flotation process. The network learning was carried out with the use of samples from previous empirical measurements of the actual process. The model created in this way made it possible to find optimal parameters not only from among the measurement spaces, but also those that go beyond the measurements. Then, evolutionary algorithms were used in order to find optimal flotation parameters. The learned neural network previously described was used to calculate the criterion in the evolutionary algorithm.

Abstract. The article presents the impact of the separation parameters on the operation of the grinding and classification system using the electromagnetic mill and impingement-inertial classifier specially designed for this machine. Preliminary tests for the classifier have been carried out, and the effect of the separation efficiency on the value of the recycle flow that optimizes the operation of the grinding system is determined. Fourteen experiments were carried out to test and evaluate the classifier's work, with a variety of damping flaps acting on the airflow. The results provide an accurate assessment of the effectiveness and efficiency of the classifier. The approximation of the electromagnetic mill partition curves using the impingement-inertial classifier was performed by using Weibull distribution function.

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