Autonomous Green Methanol Production Plant: A techno-economical assessment

Abstract

This thesis explores the techno-economic feasibility of a decentralised autonomous green methanol plant powered by renewable energy sources. The study examines the potential for renewable energy harvesting in remote areas, where grid connectivity is absent and autonomous operation is essential. Green methanol, produced from renewable feedstocks such as carbon dioxide (CO2) and water (H2O), offers a promising alternative to fossil-based fuels and chemicals due to its versatility, transportability, and role as a credible, easy-to-handle green energy vector. The concept developed in this research integrates solar photovoltaic (PV) with a Direct Air Capture (DAC) for CO2, Atmospheric Water Harvesting (AWH), and the indirect CO2 hydrogenation process. A Multi-Criteria Decision Analysis (MCDA) was conducted to select the most suitable technologies for each subsystem. To assess the techno-economic viability of the plant, a comprehensive energy and mass balance was constructed, followed by a cost modelling framework. The Specific Energy Consumption (SEC) for methanol production was calculated to be 2119 [kJ/mol], highlighting the energyintensive nature of the process. Duqm (Oman) showed the most favourable conditions in terms of the techno-economic feasibility due to its consistent solar irradiance and low solar PV and storage costs.
To develop the autonomous concept in detail, a custom simulation framework was built using
SciLab (open-source version of Matlab). The optimised Base Case configuration resulted in a
LCOEproduction of 153.34 [$/MWh] which aligns with projected e-fuel costs in 2030. Additional simulations were conducted to explore varying operational strategies and control mechanisms. A process analysis was also performed to identify the relevant timescales for autonomous
operation, revealing that hourly and daily intervals are most critical for mitigating intermittency and managing energy supply to ensure a continuous and stable performance. A Model Predictive Control (MPC) framework was implemented to manage energy allocation and methanol production levels under variable solar conditions. The uncertainty-aware MPC variant demonstrated the plant’s ability to adapt dynamically, even during periods of unexpected weather changes and fluctuating irradiance.
The study concludes that decentralised autonomous methanol production is technically feasible
and economically competitive in well-selected locations. It emphasises the importance of integrated control strategies, component sizing, and predictive modelling in achieving stable and efficient operation. The findings contribute to the advancement of autonomous renewable energy supply chain development and offer a scalable blueprint for future deployment in remote regions. Future research should focus on improving component maturity, integrating holistic system design, and exploring machine learning-based autonomy to further enhance system resilience and cost-effectiveness.