Chemical exergy of ideal and non-ideal gas mixtures and liquid solutions with applications
Bibliographic record
Abstract
Exergy or availability, although not a recent concept, is receiving extensive coverage in scientific publications due to its vast applications in different scientific and engineering fields. Exergy of a system consists of two parts: thermo-mechanical exergy and chemical exergy. While thermo-mechanical exergy of systems is covered to a certain extent in modern undergraduate textbooks on engineering thermodynamics, chemical exergy is mentioned only briefly. In particular, the theoretical and conceptual developments related to chemical exergy are not covered in any detail. The focus of this article is the chemical exergy of materials. Special attention is given to the theoretical treatment of non-ideal gas mixtures and liquid solutions. The equations necessary to estimate the chemical exergy of ideal and non-ideal mixtures and solutions are developed from the fundamental concepts. Where necessary, numerical examples are given to illustrate the concepts for the benefit of the students. Finally, a practical problem dealing with the furnace/boiler unit of a practical steam power plant is solved using the concepts of chemical exergy and exergy analysis. As the material presented in this article involves advanced level concepts in thermodynamics, it is most suitable for the second, advanced level, course in engineering thermodynamics in third year, after the students have completed a full one-term course on introductory thermodynamics in their second year.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".