Highly efficient production of dichloroethane, an important component for the synthesis of vinyl chloride and other chemical products, is impossible without optimization of the ethylene oxidative chlorination process. This process requires precise control due to its complexity and sensitivity to changes in temperature and reagent concentration. To solve this problem, this paper proposes an integrated approach that includes the development of a virtual analyzer based on a neural network and a fuzzy controller. The virtual analyzer provides prediction of dichloroethane and carbon dioxide concentrations, and the fuzzy controller automatically adjusts the process parameters in real time. The result is improved product quality, reduced operating costs, increased competitiveness, and stable operation of the ethylene oxidative chlorination process.

doi.org/10.32737/0005-2531-2025-3-41-53