This study explores the possibility of producing methanol from Ghanaian agricultural waste biomass, such as rice husk, sawdust, and cocoa pod husk, by employing Aspen Plus simulation and response surface methodology (RSM) as optimization methods. The process is modeled in Aspen Plus using the RPlug reactor model to simulate the reaction kinetics and optimize production factors. Among these, the major factors investigated included the steam-to-biomass ratio (SBR), the gasification temperature, and the reactor temperature for the purpose of optimizing methanol production through response surface methodology. According to this study, the ideal process parameters of SBR at 0.6039, gasification temperature set to 1000 °C, and reactor temperature maintained at 296.97 °C result in a methanol rate of 14,731 kg/h, corresponding to 78% yield. The simulation was validated against experimental data, revealing its high accuracy, with an R² value of 91.53%, and affirming the practical viability of the model. The economic assessment showed methanol production costs reaching USD 200 per tonne, whereas import prices remained at USD 850 per tonne, which demonstrates the clear financial benefits of local production. The proposed production method generates annual net profits of USD 2.23 million and establishes an investment return of 30%. This study demonstrates that agricultural waste can serve as an eco-friendly methanol production material while helping Ghana improve waste management and achieve energy independence and environmental sustainability. These discoveries lay the groundwork for large-scale methanol production in Ghana, utilizing local biomass resources to enhance the country’s renewable energy strategy, decrease its reliance on imported fuels, and support a circular economy. However, there are still challenges, like fluctuations in biomass characteristics, the high energy consumption of the gasification process, and the integration of a cost-effective catalyst in the methanol reactor. Exploring catalytic enhancements and integrating optimization strategies in future work could further enhance process efficiency.
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