(407d) Enhancing Methanol Production Via CO? Hydrogenation: Catalyst Selection and Multivariable Analysis Using Design of Experiments Framework

Methanol is a vital industrial chemical used across the chemical, pharmaceutical, olefin, and energy sectors. Global production is approximately 90–100 MMTPA, with a significant portion traded internationally. Traditionally, methanol is produced via steam methane reforming of natural gas (grey methanol) and coal gasification (black methanol). However, rising demand extends beyond conventional applications, with emerging use in fuel blending and marine bunkering supporting global decarbonization efforts. Forecasts suggest that clean methanol (blue or green methanol) demand from the maritime sector alone could reach 60–70 MMTPA by 2030 and 100 MMTPA by 2035.

Hydrogenation of CO₂ offers a sustainable methanol production pathway, reducing emissions while generating cleaner fuel. Catalysts significantly influence key performance parameters such as CO₂ conversion (XCO₂), methanol selectivity (SMeOH), and space-time yield (STYMeOH). Commercially used CuO/ZnO/Al₂O₃ (CZA) catalysts face challenges such as deactivation due to hydrophilic nature of aluminum, copper aggregation & hydrothermal sintering, resulting in reduced turnover frequency (TOF). Alternative catalyst modifiers, such as zirconium (ZrO₂) and cerium (CeO₂) oxides, have shown promise in overcoming these limitations. The ‘Design of Experiments’ (DoE) framework is a statistical method which aids in study of reaction mechanism using a multi-variable approach which captures the interaction of multiple parameters and helps in determining optimum reaction conditions to achieve higher yields of desired product. This is most suitable for studying the new catalyst systems and streamlining parameters to achieve optimum results.

This study reviews existing research on ZrO₂ and CeO₂ as replacements for Al₂O₃ in Cu-Zn (CZ)-based catalysts. A Design of Experiments (DoE) framework is employed to analyze multivariable interactions affecting methanol yield, enabling optimization of reaction conditions. Additionally, Density Functional Theory (DFT) analysis provides insights into reaction mechanisms. This study contributes to developing a systematic approach for selecting improved catalysts to enhance process efficiency and promote sustainable methanol production.