An exergy calculator tool for process simulation
Bibliographic record
Abstract
Abstract The constant tightening of environmental regulations and the ongoing need to reduce operating costs have posed a challenge for the design of any chemical process. Process engineers use process simulators to help them perform calculations that will, ultimately, result in design parameters or operating conditions for a plant or process. Exergy is a potential indicator that can aid in the design of energy efficient chemical processes and plants. The exergy concept has been increasingly used as a tool to locate the critical energy use in many industrial processes, both chemical and non‐chemical. However, currently most process simulators in the market do not offer the capability of calculating the exergy of a process. An open‐source exergy calculator has been created by embedding the calculation procedure in an open‐source chemical process simulator. This improves process simulation by including a potential tool for design teams to quickly evaluate several process options in detail in order to understand their energy utilisation. A simple exergy analysis for a gas processing facility is used to demonstrate the capabilities of the tool. The analysis shows where the largest quantities of exergy are being consumed within the plant, thus pointing to areas where improvement in energy usage can be made. The use of exergy as a potential design and retrofit tool is also discussed. Copyright © 2007 Curtin University of Technology and John Wiley & Sons, Ltd.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".