articles

A promising way to ensure the competitiveness of industrial products is to improve the energy efficiency of technological processes by reducing fuel and energy consumption. For energy-intensive production such as a chemical-engineering system for processing ore beneficiation waste, this task boils down to optimizing the operating modes of conveyor roasting machines that thermally prepare the raw material for remelting. The high computational complexity of the problem, caused by the extensive set of controlled process line parameters and the polyfractional nature of the ore raw material, limits the applicability of analytical methods. It is proposed to use a nature-inspired method based on the principles of social behavior in cockroach colonies. Its specific has led to the following modifications to the basic Cockroach Swarm Optimization. Firstly, a multicolony approach, which involves considering a multi-layered arrangement of dispersed ore components of different sizes within a single colony, is used to narrow the search space. Secondly, to improve the algorithm’s convergence rate, it is proposed to introduce elements of fuzzy set theory into it to determine the “free” parameters (the “perception” distance, which defines the maximum distance from the best solutions; the step size of agent movement within the search space toward the best solutions). The developed fuzzy multicolony method was implemented as a computer program using Python. The results of computational experiments demonstrated the convergence of the proposed method for optimizing the operating modes of a conveyor roaster, and a comparison with its standard operating mode demonstrated its potential for use in controlling the chemical-engineering process of thermally preparing polyfractional ore raw materials for remelting.