Improving the sustainability of coal SC in both developed and developing countries by incorporating extended exergy accounting and different carbon reduction policies
Bibliographic record
Abstract
In the age of Industry 4.0 and global warming, it is inevitable for decision-makers to change the way they view the coal supply chain (SC). In nature, energy is the currency, and nature is the source of energy for humankind. Coal is one of the most important sources of energy which provides much-needed electricity, as well as steel and cement production. This manuscript-based PhD thesis examines the coal SC network as well as the four carbon reduction strategies and plans to develop a comprehensive model for sustainable design. Thus, the Extended Exergy Accounting (EEA) method is incorporated into a coal SC under economic order quantity (EOQ) and economic production quantity (EPQs) in an uncertain environment. Using a real case study in coal SC in Iran, four carbon reduction policies such as carbon tax (Chapter 5), carbon trade (Chapter 6), carbon cap (Chapter 7), and carbon offset (Chapter 8) are examined. Additionally, all carbon policies are compared for sustainable performance of coal SCs in some developed and developing countries (the USA, China, India, Germany, Canada, Australia, etc.) with the world's most significant coal consumption. The objective function of the four optimization models under each carbon policy is to minimize the total exergy (in Joules as opposed to Dollars/Euros) of the coal SC in each country. The models have been solved using three recent metaheuristic algorithms, including Ant lion optimizer (ALO), Lion optimization algorithm (LOA), and Whale optimization algorithm (WOA), as well as three popular ones, such as Genetic algorithm (GA), Ant colony optimization (ACO), and Simulated annealing (SA), are suggested to determine a near-optimal solution to an exergy fuzzy nonlinear integer-programming (EFNIP). Moreover, the proposed metaheuristic algorithms are validated by using an exact method (by GAMS software) in small-size test problems. Finally, through a sensitivity analysis, this dissertation compares the effects of applying different percentages of exergy parameters (capital, labor, and environmental remediation) to coal SC models in each country. Using this approach, we can determine the best carbon reduction policy and exergy percentage that leads to the most sustainable performance (the lowest total exergy per Joule). The findings of this study may enhance the related research of sustainability assessment of SC as well as assist coal enterprises in making logical and measurable decisions.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".