Predictive analysis of effective separation processes for 2,2-Dimethoxypropane and water from reaction mass

The separation of 2,2-dimethoxypropane (DMP) and water from the reaction mass is a critical industrial challenge due to strong non-ideal vapor–liquid equilibrium (VLE) behavior and possible azeotrope formation, which limits the effectiveness of conventional distillation despite a moderate boiling point difference (2,2-Dimethoxypropane ≈83°C, water 100°C). The primary problem lies in achieving high-purity separation with minimum energy consumption while maintaining process feasibility under realistic operating conditions. This work is motivated by the need to develop energy-efficient and sustainable separation strategies that reduce operational cost and improve process performance. The study proposes a predictive framework uniting experimental separation analysis, thermodynamic modeling, process simulation, and hybrid distillation–pervaporation concepts for the physical separation of DMP and water. Potential hydrolysis effects were measured as a restriction for future reactive phase equilibrium studies. The main objective is to design and authorize an optimized separation strategy that enhances purity, minimizes energy usage, and provides reliable predictive ability for azeotropic and non-ideal systems in industrial applications. The methodology united experimental distillation studies, thermodynamic modeling, and process simulation using the Nonrandom Two-Liquid model (NRTL), Wilson, and UNIQUAC Functional-group Activity Coefficients (UNIFAC) models in Aspen Plus, HYSYS, and Python to analyze 2,2-dimethoxypropane–water separation. Results showed that hybrid distillation–pervaporation outperformed standalone methods, achieving up to 99.2% product purity, 97.6% separation efficiency, and a minimum energy consumption of 750 kWh/kmol. Model validation using an independent experimental dataset displayed excellent agreement between predicted and experimental data, with an RMSE of 0.018 and an R2 value of 0.992. Future work will focus on integrating reactive distillation, advanced membrane materials, and data-driven optimization techniques, including machine learning and genetic algorithms, to further expand the separation of non-ideal azeotropic systems and diminish overall process energy consumption.