## 3 Results and Discussion  
The MINITAB 21.4.2 software tool was used to evaluate the simulation results [49]. The main objective was to optimize the wear depth at a specific target mass flow of the screw conveyor. The significance of the parameters was evaluated at a 95% confidence level. The method called ANOVA was used to analyze the trial runs and construct a regression model that establishes a connection between the responses and the parameters. The evaluation of the model's \(p\)-value and R-squared values enables the assessment of the model's predictive accuracy. The variance analysis results for mass are presented in Table 3. The ANOVA table is exclusively provided for the mass flow parameter of the screw conveyor. For all other responses such as shear intensity, power, stress, and deformation, ANOVA was similarly used to analyze the trial runs and construct regression models establishing connections between these responses and the parameters. Nevertheless, all examples encompass Pareto charts, regression equations, normal distribution plots and findings concerning the association between the input parameters and the response variable. Overall, the model demonstrates significance, as indicated by a low \(p\)-value of 0.001, suggesting that at least one input parameter has a significant impact on the response variable. All terms, including rotational velocity (\(N\)), Clearance (\(Clearance\)), Serve diameter (\(D_{screw}\)), shaft diameter (\(D_{shaft}\)), and Pitch (\(Pitch\)) display distinguished effects on the response variable, supported by their low \(p\)-values (<0.05). However, the square terms and 2-way Interactions do not exhibit significant effects. This implies that the majority of the variation in the response variable can be explained by the linear effects of the input parameters. The regression equations were subsequently derived based on the experimental output responses to establish the relationship between the input and output variables. The relationship representing the mass flow, shear intensity/wear, power consumption, stresses, and deformations on the screw conveyor can be described by the regression equation/model presented in relations (7-11). Next to each association, there is a corresponding parameter (R\({}^{2}\)) which serves as an indicator of the accuracy of the desired function and its consistency with the observed behaviour of the analyzed parameter. The proximity of this parameter to the value of 100% directly correlates with the accuracy of the function. The standardized Pareto chart can also effectively demonstrate (Figure 7) the importance of various factors that influence the mass flow response. Notably, the screw blade diameter emerged as the primary factor, applying a substantial impact, and categorizing it as the critical factor. At the same time, the remaining factors were considered as fine adjustment factors. Additionally, the regression model, when presented in uncoded units, enables the prediction of values, as depicted in relation 7. As anticipated, our observations in Figure 7. indicate a direct correlation between the screw blade diameter and shaft angular velocity with variation in mass flow. However, an interesting deviation from expectations occurred in the context of clearance. Upon closer examination, we identified that the material behaved as a moving nut traversing along a thread, and the reduction in clearance