Forecasting mixture composition in the extractive distillation of n-hexane and ethyl acetate with n-methyl-2-pyrrolidone through ANN for a preliminary energy assessment

We developed an artificial neural network (ANN) to predict mole fractions in the extractive distillation of an n-hexane and ethyl acetate mixture, which are common organic solvents in chemical and pharmaceutical manufacturing. The ANN was trained on 250 data pairs from simulations in DWSIM software. The training dataset consisted of four inputs: Feed flow inlet (T1-F), Feed Stream Mass Flow temperature pressure (FM1-F), Make-up stream mass flow (FM2-MU), and ERC tower reflux ratio (RR-ERC). The ANN demonstrated the ability to forecast four output variables (neurons): Mole fraction of n-hexane in the distillate of EDC (XHE-EDC), Mole fraction of N-methyl-2 pyrrolidone in the bottom of EDC (XNMP-EDC), Mole fraction of ethyl acetate in the distillate of ERC (XEA-ERC), and Mole fraction of N-methyl-2 pyrrolidone in the bottom of ERC (XNMP-ERC).The ANN architecture contained 80 hidden neurons. Bayesian regularization training yielded high prediction accuracy (MSE = 2.56 × 10–7, R = 0.9999). ANOVA statistical validation indicated that ANN could reliably forecast mole fractions. By integrating this ANN into process control systems, manufacturers could enhance product quality, decrease operating expenses, and mitigate composition variability risks. This data-driven modeling approach may also optimize energy consumption when combined with genetic algorithms. Further research will validate predictions onsite and explore hybrid energy optimization technologies.