An Overview of Energy and Exergy Analysis to the Industrial Sector, a Contribution to Sustainability
Bibliographic record
Abstract
Globally, industry remains one of the main consumers of fossil fuels; hence, it is one of the prime sources of greenhouse gases (GHG). Exergy analysis methods can be applied to detect the processes inefficiencies. To enhance the interconnection of the exergy and the environment theories, renewable share and exergy efficiency are suggested, in a contribution to sustainability. Exergy analysis is proposed and lifted to study the industrial sector. Even though exergy analysis has been applied to study societies, few studies have been conducted to study emerging-market economies. In 2015, Mexico’s crude oil production was the 12th biggest worldwide, therefore fossil fuels are still the main source to produce energy for the domestic and productive sectors of the Mexican society. Consequently, a prospective study case in Mexico is suggested. The industrial sectorial exergy consumption amounts 1350 PJ in 2000, increasing to 1591 PJ in 2015. Both energy and exergy efficiencies show similar results along the 16 years, with average energy and exergy efficiencies of 78% and 23%, respectively. In comparison with results of similar studies, Mexican exergy efficiency falls within the ranges, yet below the average of 48%. Thus, poor efficiency is still occurring in the sector. Our proposal could provide significant opportunities to become a more sustainable sector, based on the exergetic renewable share and the exergy efficiency.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".