Machine Learning Assisted-Barnacles Mating Optimization of non-enzymatic isoamyl acetate in a solvent-free system using miniaturized intensified reactor

Process intensification can improve esterification productivity with lower energy demand. This work develops a machine learning assisted Barnacles Mating Optimization (BMO) framework for solvent free isoamyl acetate synthesis in a miniaturized intensified reactor, targeting ester maximization and energy minimization. Decision tree, SVM, and ANN models were trained to predict ester concentration from temperature, acid to alcohol ratio, flowrate, and retention time, and bootstrap resampling improved all models. The bootstrap ANN achieved the highest accuracy (R2 = 0.989) and was selected as the surrogate for optimization. Using ANN BMO, ester maximization converged to 45.343 °C, ratio 0.6116, 51.092 µL min−1, and 42.701 min, yielding 1.240 mol L−1 ester with 47.056 mW energy. Energy minimization produced a boundary solution at 30.0 °C, ratio 0.60, 40.0 µL min−1, and 33.0 min, giving 9.22 mW but low ester (0.2559 mol L−1). Multi objective optimization generated a Pareto trade off where higher ester requires higher energy, with temperature identified as the primary decision variable driving the compromise.