Python-Powered Optimization of Sustainable 1,3-Butadiene Production from Ethanol: Bridging Thermodynamics, Kinetics, and Machine Learning

This work presents an integrated Python-based framework to optimize the ethanol-to-1,3-butadiene conversion over a K2O:ZrO2:ZnO/MgO–SiO2 catalyst, a sustainable alternative in decarbonizing plastics and rubber manufacturing. Thermodynamic evaluations confirmed the feasibility of all elementary steps, while kinetic modeling identified the butadiene-forming reaction as the most sensitive step. Experimental data were analyzed using multivariate surface-response methods, revealing an optimal operating window of 350–375 °C and 0.93–1.24 h−1. A Random Forest model (R2 = 0.91) ranked weight hourly space velocity (WHSV) and selectivity descriptors as the most dominant variables, providing a quantitative basis for data-driven process intensification.