Bayesian optimization of industrial-scale toluene diisocyanate liquid-phase jet reactor with 3-D computational fluid dynamics model

Abstract

Toluene diisocyanate (TDI) is an important raw material to produce a flexible polyurethane foam, and the demand for TDI is growing as the polyurethane market is driven by high demand. The cold phosgenation reactor plays a vital role in the production of TDI, in that the overall reaction selectivity is determined and the most of by-product urea, which is critical to the entire downstream process, is produced inside the reactor. Therefore, the optimal design of the cold phosgenation reactor is very important to improve the overall process efficiency and operability of the TDI production process. In this research, we develop a framework for designing and optimizing TDI reactors through Bayesian optimization methods with design parameters including the diameter of two inlet nozzles, the angle between the nozzles, the size of the mixing zone, and the ratio of the converging-diverging nozzle. A comprehensive 3-dimensional computational fluid dynamics (CFD) reactor model is incorporated into the Gaussian process (Kriging) to construct a surrogate model, whose posterior is subsequently updated with new sample points searched by the acquisition function evaluated within the Bayesian optimization algorithm. As a result, the optimal design is obtained, and the urea selectivity is reduced by 11.6% compared with the basic design scheme. Compared to the 6×107 simulations required for full grid search, only 61 function evaluations were performed to attain the optimum, demonstrating that the proposed framework will help efficiently achieve the optimal design of the expensive CFD reactor models that demand a high computational cost and time for evaluation.