Application of Surrogate Models for Approximating Molecular Weight Characteristics of Polymers Under Conditions of Catalytic System Heterogeneity
During the synthesis of polyisoprene on a catalyst, several types of active centers [1] with different kinetic activities are present. This leads to the formation of complex molecular weight distributions and significantly complicates the tasks of process modeling, optimization, and control. Classical kinetic [2] and statistical [3] approaches to modeling make it possible to describe the process dynamics in detail; however, their application in optimal control problems is associated with difficulties caused by the need to perform multiple computational experiments [4]. In this regard, the development of surrogate models capable of reproducing the dependence of key product characteristics on technological and kinetic parameters at substantially lower computational costs is of particular relevance. In the present work, an approach to the construction of surrogate models for approximating the molecular weight characteristics of polymers formed under conditions of catalytic system heterogeneity is considered. The initial information is based on the results of a series of simulation calculations performed using detailed polymerization process models that account for the presence of several types of active centers and differences in kinetic parameters. The generated datasets include information on the initial composition of the reaction mixture, the operating conditions of the continuous process, as well as detailed output molecular weight characteristics of the polymer product. On the basis of these data, surrogate models are trained using regression-based machine learning methods aimed at approximating complex nonlinear relationships. The performed analysis showed that the use of surrogate models provides satisfactory accuracy in reproducing molecular weight characteristics over a wide range of technological parameters and makes it possible to significantly reduce computational time compared to direct simulation modeling. This creates prerequisites for the application of the developed approach in problems of operational analysis, optimization of synthesis conditions, and the development of intelligent decision support systems for the control of polymer production processes.