Process parameters’ influence on integrated CO ₂ capture and methanation: finding optimal conditions by Bayesian optimisation

Abstract Process parameters can significantly influence the performance of dual functional materials (DFMs) in CO _₂ capture and methanation applications. In this experimental and computational optimisation study, the effects of temperature, CO _₂ concentration, H _₂ concentration, adsorption time, hydrogenation time, and weight hourly space velocity (WHSV) on CO _₂ capture and its subsequent hydrogenation to methane were investigated for a 15%Ni1%Ru,10%Ca/Al _₂ O–CeO _₂ DFM. Results from the parametric analysis indicate that temperature, hydrogen concentration, hydrogenation time, and WHSV markedly affect methane yield. In contrast, adsorption time and CO _₂ concentration exert relatively limited influence. Further insights obtained through hybrid Gaussian process modelling and Bayesian optimisation demonstrate that determining the optimal process parameters is challenging due to the trade-off between hydrogen consumption and methane production. Nonetheless, by employing a human-in-the-loop optimisation approach, optimal conditions were identified and experimentally validated as: temperature, of both adsorption and conversion steps, 374 °C, hydrogenation time of 16 min, and a WHSV of 14 l g ⁻¹ h ⁻¹ for both adsorption and conversion steps, with a 10% increase in methane production at optimal conditions. Under these conditions, methane production reached 345.44 μ mol gDFM ⁻¹ . This study demonstrates the power of computational approaches to speed up process optimisation and design of DFMs for improved future integrated carbon capture and utilisation applications.