Process design, techno-economic, and life cycle assessment of methanol production routes
Bibliographic record
Abstract
Methanol plays a crucial role as a versatile chemical feedstock and energy carrier. Urgent and increasing environmental impacts require exploring renewable pathways for methanol production to achieve a sustainable transition. This article evaluates five scenarios for methanol production: the conventional method (baseline – natural gas), biomass gasification-based configurations, and CO 2 hydrogenation with hydrogen produced through water electrolysis. Thermodynamic analysis conducted to assess energy efficiency, with pinch analysis employed for heat integration in all pathways, effectively utilizing waste heat to enhance system efficiency and reduce environmental impacts. Life cycle assessment is conducted to evaluate the environmental impacts of each scenario, with a focus on identifying the most significant parameters influencing these impacts. Additionally, a techno-economic analysis is performed to assess the profitability of each scenario. Results indicate that the scenario of biomass-based methanol production producing biochar (BPBCB) achieves the highest energy efficiency at approximately 69%. In terms of environmental performance, the scenario of biomass-based methanol production without producing biochar (BWOBB) has the lowest impact on total human health, while CO 2 hydrogenation (DCM) demonstrates the lowest impact on total ecosystem quality. Both BWOBB and DCM scenarios exhibit the lowest climate change impacts, with 0.15 and 0.19 CO 2 ,eq /kg methanol , respectively, highlighting the role of biomass and renewable hydroelectricity in mitigating climate change. Economically, the natural gas scenario is the most favorable, but among renewable methods, BPBCB achieves the best net present value of 2.043 B$ and a payback period of 6.2 years, making it the most viable alternative to fossil-based methanol production under current conditions.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".