Economic Appraisal and Enhanced Efficiency Optimization for Liquid Methanol Production Process
Bibliographic record
Abstract
The presented study examines the economic viability and optimization of a previously designed integrated process for producing liquid methanol. The annualized cost of the system method is applied for economic analysis. The optimization method includes a robust hybrid approach that combines the NSGA-II multi-objective optimization algorithm with artificial intelligence. Decision variables for the optimization are taken from a sensitivity analysis to optimize the exergy and energy efficiencies and the investment return period. Decision-making methodologies, including LINMAP, fuzzy, and TOPSIS, are utilized to identify the optimal outcomes, effectively identifying points along the Pareto-optimal front. Compared with the original design, the research outcomes demonstrate an over 38% reduction in the process’s investment return period post optimization, as evaluated through the TOPSIS and LINMAP methodologies. Additionally, the highest level of thermal efficiency achieved through optimization stands at 79.9%, assessed using the LINMAP and TOPSIS methods, and 79.2% using the fuzzy Bellman–Zadeh method. The process optimization in the presented research, coupled with the improved economic feasibility, mitigates energy consumption through maximizing efficiency, thereby fostering sustainable and environmentally friendly development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".