Application of Genetic Algorithms for Finding Optimal Modes in Polymer Synthesis Processes

This paper presents a methodology and algorithm for solving the problem of finding optimal modes for organizing continuous polymer synthesis processes. Due to the high dimensionality of the parameter space, the large number of local solutions, and the nonlinear nature of the mathematical description, a genetic algorithm is proposed for solving optimization problems. The algorithm's software implementation enables optimization tasks to be solved for various efficiency criteria. To validate this approach, a computational experiment was conducted aimed at finding the optimal mode for the copolymerization process of butadiene with styrene. A kinetic modeling approach was employed for the mathematical description of this process. The software implementation of the proposed genetic algorithm and a series of computational trials allowed for the establishment of a two-point regulation mode of molecular mass, which contributed to maintaining the product's intrinsic viscosity value while increasing conversion to 82%. The study results demonstrated that altering the mode of regulator additive supply helps achieve the specified efficiency criterion and is a key factor in controlling continuous polymer synthesis processes.