The use of coevolutionary algorithms for optimizing the operating regimes of the roasting conveyor machine

In modern conditions of constant growth in prices for fuel and energy resources, the problem of increasing the energy and resource efficiency of technological processes of industrial enterprises has acquired particular relevance. It is especially acute for energy-intensive industries, which include high-temperature processing of mining and chemical raw materials. To reduce the energy intensity of complex chemical-technological processes, it is proposed to use the possibilities of computer simulation, for example, to optimize the operating regimes of existing equipment. The article has considered the scientific and practical problem of optimizing the charge heating regimes in various zones of the roasting conveyor machine used to produce phosphorite pellets from apatite-nepheline ore waste stored in dumps of mining and processing plants. The specifics of the optimization task (nonlinearity of the objective function, large dimension of the search space, high computational complexity) are significant limitations for the use of traditional deterministic search methods. It led to the choice of population algorithms, which are based on modeling the collective behavior and are distinguished by the possibility of simultaneous processing of several options. The cuckoo search algorithm, which is distinguished by a small number of “free” parameters that affect the convergence, was used to solve the stated optimization task. To select the optimal values of these parameters, it was proposed to use the idea of coevolution, which consists in the parallel launch of several versions of the selected algorithm with different “settings” for each subpopulation. The management of the chemical-technological system for the processing of apatite-nepheline ore waste, taking into account the basis of the results obtained, will minimize the amount of return and ensure an energy-saving operating regime of the roasting conveyor machine.