Machine learning approach to handle data-driven model for simulation and forecasting of the cone crusher output in the stone crushing plant

摘要

Grinding and crushing of stones and other particles are associated with various significant applications. Different sectors have continuously evolved in this area. In the crushing industry, plants function under strict conditions, many of which involve grinding materials. Therefore, various factors are responsible for how the crushers perform. This research investigated the ability of the adaptive neuro fuzzy inference system (ANFIS) to simulate the effects of throw, eccentric speed, closed side setting, and the size of the particle on crusher output. The developed simulation model was adjusted and authenticated alongside the experimental data of the investigated parameters. The model's performance was computed by the use of several prediction criteria skills. The results of the study indicated that the developed ANFIS model could simulate the Cone crusher output and give a dependable forecast of the cumulative weight fraction. The researchers resolved that the model fostered was a suitable instrument for the onsite cone crusher assessment.