Sustainable methanol production from CO2 hydrogenation: Boosting productivity and profitability using dynamic multi-stage cooling strategy

Abstract

CO2 emissions pose a critical environmental threat, contributing to global warming and climate change. The efficient hydrogenation of CO2 to methanol is a critical strategy for captured carbon, providing a sustainable pathway to reduce greenhouse gases. This study evaluates two comprehensive approaches, a simple quasi-steady-state (QSS) model and a complex dynamic model, to boost methanol production efficiency from CO2 hydrogenation in a multi-tubular fixed-bed reactor by identifying optimal cooling strategies in single- and multi-stage systems. Key operating parameters, including pressure and H2/CO2 ratio, are distinguished as pivotal to shifting equilibrium toward higher methanol yields, while a tailored cooling system can effectively manage CO2 hydrogenation reactions. At first, the optimal temperature profile along the reactor's length is determined using both models. The comparison of the results showed that both models exhibit similar behavior and are in good agreement with industrial data. Subsequently, a time-stepwise strategy is employed to achieve an optimal one-stage cooling system over 4 years. Results indicated that the QSS model offers a practical and cost-effective approach compared to the dynamic model, achieving consistent temperature control, particularly in multi-stage cooling systems. Meanwhile, the dynamic model adjusts coolant temperatures across the catalyst's lifespan, achieving up to a 9.95 % increase in methanol yield under declining catalyst activity. Economic analysis reveals substantial revenue enhancements due to applying the optimal two-stage cooling strategy—up to $144.3 million over 4 years, underscoring the two-stage cooling system with higher performance as the promising potential to increase profitability in industrial CO2 hydrogenation. In conclusion, this study provides valuable insights into effective temperature management strategies to maximize methanol production in the presence of catalyst deactivation. © 2025 Elsevier Ltd.