Robust Design of a Dimethyl Ether Production Process Using Process Simulation and Robust Bayesian Optimization.

Abstract

As greenhouse gases such as CO2 continue to promote global warming, the reduction of CO2 emissions is attracting increasing attention. In this study, we design a process for producing dimethyl ether (DME), which is a promising means of using CO2 as a resource. Design variables such as temperature and pressure need to be optimized to reduce CO2 emissions while maintaining high product purity and DME production. Conventional process designs determine these design variables from the chemical background and through trial-and-error simulations, which are very time-consuming. The proposed method optimizes the design variables efficiently by repeating the process simulations and selecting promising candidates for the design variables using machine learning. For an adaptive design of experiments, Bayesian optimization is used to achieve the objectives of the DME process while efficiently optimizing the design variables. In addition, we also optimize the design variables considering variations in the temperature and pressure data, meaning robust Bayesian optimization. The proposed method successfully identifies design variables that satisfy all experimental targets in an average of 54 simulations while achieving 100% of the targets with product purity 0.95-1.00, amount of DME in the product 350-845 kmol/h, and CO2 emissions 0-835 kmol/h, confirming the effectiveness of the proposed robust Bayesian optimization method.