Sustainable process design for heat exchanger network considering inherent safety and process economics
Bibliographic record
Abstract
Abstract Process lifecycle has several stages, including process design covered in multiple stages. Process economics is a vital factor in finalizing the process design. Besides economics, inherent safety is an important concept contributing to sustainable process design generation. The inherent safety concept has been applied via equipment characteristics for individual equipment. Since a method considering inherent safety with equipment aspects and process economics has not been available, therefore, a new method has been proposed, namely sustainable process design for heat exchanger network (SPDHEN), to integrate inherent safety, equipment aspects, and process economics. SPDHEN uses indexing to identify the critical heat exchanger, which is then examined via hazard analysis for an explosion. For unacceptable hazards, inherent safety principles are engaged to generate design alternatives for which process economics is examined too. The final design would be inherently safer with the best profit margin. The proposed method has been studied for the ammonia synthesis loop. It is concluded that the explosion hazard has been reduced to a tolerable level by using inherent guide words with a marginal compromise on quantity of ammonia produced, that is, 0.32%. This method is straightforward and can be useful for process engineers to generate sustainable process designs for heat exchanger networks considering safety and process economics simultaneously.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".