Mathematical and Simulation Modelling of an Ideal Mixing Chemical Reactor for Operating Environment Volume Optimising

The article presents the results of a comprehensive study of mathematical and simulation modeling of a continuous stirred tank reactor (CSTR) aimed at optimizing the operating volume during the esterification of acetic acid with ethanol. Classical approaches to building mathematical models based on mass and energy balance equations, including stationary and non-stationary regimes, are analyzed. The analytical methods are shown to be effective for preliminary process analysis and parameter estimation under relatively simple technological conditions. The mathematical model is demonstrated to predict dynamics of changes in concentrations and justify technological parameters when designing key production equipment with acceptable accuracy. Special focus is given to the simulation of the CSTR in the DWSIM environment using the NRTL (Non-Random Two-Liquid) thermodynamic model, which accurately captures the non-ideal interactions of polar liquid-phase components. A reversible homogeneous kinetic esterification reaction was simulated under isothermal conditions in a continuous mode. The simulation results included evaluation of reactant conversion levels and showed that the initial reactor volume was insufficient to reach industrial-level conversion. To solve this, a controller block was implemented to automatically select the optimal reactor volume needed to achieve over 98 % conversion of the base reactant (ethanol). A comparative analysis of mathematical and simulation results modeling was conducted, which indicates the high adequacy of mathematical models when they are properly defined for assessing the basic characteristics of the technological process and equipment parameters. Underlined that simulation modeling in DWSIM provides the advantage of integrating a broader range of physical and chemical parameters, adapting to real process conditions, and exploring variable scenarios without conducting physical experiments. The paper substantiates the practical value of combining both modeling approaches to enhance maximum accuracy, flexibility, robustness, and process efficiency in practices of computer chemical engineering and equipment design decisions.