Techno-economic optimization of e-methanol production integrated with oxy-fuel power plants: an adaptive power management case study in Australia
Bibliographic record
Abstract
The urgent need to decarbonize the energy and chemical sectors necessitates innovative pathways that integrate renewable energy with carbon utilization. This study presents a novel Power-to-Methanol (PtM) system. It uniquely combines solar-driven hydrogen supply via a thermochemical method, flexible operation tied to electricity markets, and detailed techno-economic modelling, distinguishing it from previous e-methanol integration research. The CO 2 utilized in the methanol synthesis unit is sourced from a retrofitted oxy-fuel power plant. Among the evaluated configurations, the best option achieves a capture rate of 350.1 kg CO2 /MWh, with an associated efficiency penalty of 6.7%. Despite these promising features, the standalone carbon capture approach yields a high CO 2 avoidance cost of $217.4/t CO2 , making it economically unviable. This study investigates the conversion of captured CO 2 into methanol to improve economic feasibility, thereby creating financial incentives for the adoption of advanced capture technologies. A detailed commercial-scale modular e-methanol production unit (750 t MeOH /day) is presented. The system operates dynamically, adapting to fluctuations in electricity markets to improve economic returns through flexible grid interaction. Required hydrogen and oxygen are supplied via a solar-driven Copper–Chlorine (Cu–Cl) thermochemical cycle. Multi-objective optimization identifies the optimal design, achieving a Levelized Cost of Methanol (LCOM) of $1,190/t MeOH , an overall efficiency of 11.8%, and a specific avoided CO 2 of 1.2 t CO2 /t MeOH . The produced e-methanol remains non-competitive with grey methanol. However, future projections for 2050 indicate that, under anticipated CO 2 incentive schemes and reductions in critical cost components, the LCOM could decrease significantly to $745/t MeOH .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".