Abstract
Dimethyl ether (DME) is a promising clean fuel and chemical intermediate, yet its synthesis from synthesis gas remains highly sensitive to both catalyst formulation and operating conditions. In this work, a data-driven framework is developed that combines machine learning surrogate modeling with multi-objective optimization to support systematic decision-making in DME synthesis. The novelty lies in the systematic comparison of different optimization approaches applied to an identical machine learning surrogate model for DME synthesis, thereby highlighting their respective strengths and limitations as decision-support tools under limited-data conditions. A dataset compiled from published literature includes catalyst composition, preparation methods, physicochemical descriptors, and operating conditions, with CO conversion and DME selectivity as performance indicators. After data preprocessing, feature analysis using correlation analysis and principal component analysis (PCA) is applied to explore dominant trends and interactions. Several supervised machine learning models are trained and benchmarked, and the best-performing model is selected as a surrogate for optimization. Two complementary multi-objective optimization strategies are then applied to the same surrogate model: a fuzzy-enhanced NSGA-II algorithm and a constrained multi-objective Bayesian optimization approach. Gradient boosting is found to provide the most reliable predictive performance among the tested models. Both optimization strategies identify similar catalyst formulations as optimal, while differences emerge in the recommended operating conditions. The comparative analysis highlights how different optimization paradigms influence compromise solutions when balancing CO conversion and DME selectivity.