Modeling of Catalytic CO₂ Methanation Using Smart Computational Schemes

Abstract

An analyzing tool as a sustainable method to combine CO2 capture and production of CH4 by utilizing CO2 as a feedstock is proposed. The impact of incorporating metallic promoters such as Fe, La, Ce, and Co to an Al2O3‐supported catalyst containing Ni as the first metal in the CO2 methanation was modeled. Smart models were employed to analyze the CO2 conversion and CH4 selectivity in CH4 production from CO2. The genetic programming (GP) model provides a mathematical framework for the estimation of CO2 conversion and CH4 selectivity. The model inputs are catalyst surface area, temperature, H2/CO2 ratio, gas‐hourly space velocity, and catalyst pore volume and diameter. The results confirm that the GP model estimates the CO2 conversion and CH4 selectivity very well.

Predictive Modeling of Co2 Methanation Reaction for Carbon Capture

“…Yılmaz et al [1] utilized a comprehensive dataset to develop a random forest model, successfully predicting CO2 conversion based on various catalyst properties and reaction conditions. Similarly, Dashti et al [4] investigated CO2 methanation, incorporating metallic promoters to enhance catalyst performance. Their genetic programming model effectively estimated CO2 conversion and CH4 selectivity.…”