MULTICRITERIA OPTIMISATION OF CRUSHING PARAMETERS FOR THE MnS17 FERROALLOY WITH CONSIDERATION OF REMAINING USEFUL LIFE PREDICTION OF THE JAW CRUSHER PLATES

Abstract

A comprehensive methodology for the optimisation of the crushing process of high-abrasion ferrosilicomanganese grade MnS17 in a DShch-1 jaw crusher under real metallurgical production conditions is presented. Based on the analysis of industrial data obtained during ferroalloy processing, a hybrid parametric model has been constructed, linking the main operational parameters (feed particle size, rotational speed of the eccentric shaft, degree of filling of the crushing chamber) with the key performance indicators: crusher throughput and wear rate of the crushing jaws made of high-manganese steel 110G13L. A continuous vibration diagnostics system for the technical condition of the crusher has been developed and tested under industrial conditions, enabling real-time classification of the current wear level of the working tooling using an artificial neural network based on radial basis functions (RBF). The accuracy of state identification is no less than 85%, as confirmed by direct geometric measurements of the jaws. A multicriteria vector optimisation problem has been solved to determine rational crushing regimes that provide a compromise between the conflicting requirements of maximising throughput and minimising the wear rate. Optimal values of the shaft rotational speed have been determined for different stages of the jaw life cycle. A methodology is proposed for forecasting the remaining useful life of the crushing jaws, subject to its integration into the process control system. The results of the work have practical significance and make it possible to reduce specific operating costs, increase the time between overhauls, and improve the quality of the commercial ferroalloy fraction. The expected economic effect from the implementation of the developed recommendations has been estimated based on the reduction in consumption of expensive tooling and the decrease in equipment downtime.