## 7 Results and Discussion
Figure 3: A comparison of 5 input parameters for test (**a**) 72 and (**b**) 84 carried out within 30 min. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Repetitions** & **Denver Flotation Machine (Test 72)** & **Jameson Cell (Test 84)** \\ \hline
100 & & \\
200 & & \\
500 & & \\
1000 & & \\
2000 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The graphical evaluation of matching the model of output parameters to empirical data according to the assumptions included in test 72 and 84 (red line—output values of the neural network obtained as a result of the learning process, blue line—empirical values). As a result of the neural network operation, a model of the flotation process of individual lithological types of Polish copper ore in the Denver flotation machine was created. Based on this model, the optimisation was done with the use of the evolutionary algorithm. The number of generations to be generated during the calculation process was verified. For example, it can be mentioned that when establishing one generation for the evolutionary algorithm, no satisfactory results were obtained. For the examined cases, a significant dispersion of the selected indicators' values could be observed. A gradual increase in the number of algorithm generations clarified the results. Tables 10 and 11 show the obtained optimal values of the copper ore enrichment process in the Denver flotation machine and in the Jameson cell, depending on the evolutionary algorithm advancement. By analysing the obtained parameter values, it can be concluded that it seems sufficient to use 20 evolutionary algorithm generations for the flotation process optimisation since the results differ slightly above this value. The flotation process model created by NN extended the range of examined parameters beyond the measurement space and constituted the basis for the optimisation with the use of an evolutionary algorithm. Comparing the results of the flotation process evaluation indicators (\(\beta\), \(\delta\), \(\varepsilon\)) for both devices, it can be concluded that the pneumatic-mechanical Denver flotation machine is characterised by higher efficiency in copper ore enrichment. However, it is misleading to some extent as the Jameson cell is a device dedicated to enrichment of the feed characterised by a finer particle size. At the same time, it should be noted that the Denver flotation machine is not able to ensure enrichment of copper ore with such particle characteristics. The clear difference in the flotability of the designated lithological types which favour the Denver flotation machine is also worth mentioning. The best results for the Denver flotation machine were obtained for easily enrichable sandstone copper ore.  
Searching for optimal flotation parameters is a complex process and the results obtained from evolutionary algorithms and neural networks depend on the so-called \"batch\", i.e., experimental data on the basis of which the program learns the examined process. In order to check optimal conditions of the enrichment process for individual lithological types and particle fractions, the analysis was repeated while limiting the raw data to specific groups. In this way, information on input and output parameters of flotation enrichment of selected varieties of copper ore (Table 12) and particle size distribution of copper ore directed for separation in the Denver flotation machine (Table 13) was collected.