Process Modeling and Optimal Evaluation Analysis for Direct CO2 Conversion to Methanol

Implementing a CO2 conversion process into methanol production offers a dual solution to mitigate increasing air pollution caused by CO2 emissions and address the depletion of non-renewable fuels for a sustainable future. This chapter highlights key technical strategies for enhancing methanol synthesis, emphasizing process modeling, optimization, cost analysis, and energy/exergy analysis. It underscores the potential of machine learning and deep learning to streamline experimentation by simulating the entire process, leveraging predictive and classifying abilities with process datasets, thereby significantly advancing methanol synthesis. Optimizing methanol synthesis process involves employing efficient techniques based on experimental data for maximum yield, with a focus on cost-effectiveness during scale-up, often determined through techno-economic assessments of the entire process. The study integrates energy and exergy assessments to evaluate the feasibility of methanol synthesis from CO2, facilitating its role as a greener fuel option. These approaches contribute to a circular economy and a sustainable environment.