Oxidative Dehydrogenation of Ethane with CO2 over the Fe-Co/Al2O3 Catalyst: Experimental Data Assisted AI Models for Prediction of Ethylene Yield

Abstract

In this work, Fe-Co-based mixed metal oxides supported on Al2O3 are proposed for ethylene production through oxidative dehydrogenation of ethane with CO2 (ODH-CO2). Thermodynamic feasibility analysis followed by a systematic experimental study is performed on catalyst synthesis and its composition optimization along with process condition optimization in a fixed bed reactor. The study revealed that 5% Fe loaded on 10% Co/Al2O3, 700 °C, and 1:1 are the optimal composition, temperature, and molar ratio of CO2 to ethane, respectively, achieving 29% ethane conversion and resulting in 16% ethylene yield. Further, the experimental data was used to develop different linear, nonlinear, and ensemble AI models for ethylene yield prediction through a systematic grid search and k-fold cross-validation procedure. Among all the models, the kernel ridge regression model is found to be the most accurate, exhibiting the highest R2 value of 0.966 and lowest root mean-squared error (RMSE) of 0.032 on test data, successfully capturing the underlying nonlinear dynamics of ODH-CO2.