From Grinding to Green Energy: Pursuit of Net-Zero Emissions in Cement Production
Bibliographic record
Abstract
In an age of heightened environmental awareness and the pressing need for net-zero emissions, concerns over rising energy consumption in cement production, responsible for 5–8% of global CO2 emissions, have intensified. This paper proposes a novel pioneering framework that integrates Shannon’s entropy and Multi-Criteria Decision Making (MCDM) methods to steer the cement industry towards sustainability and net-zero emissions. Utilizing Shannon’s entropy, the research impartially determines the significance of multiple criteria, reducing biases in decision-making for energy efficiency in cement production. Four MCDM methods (TOPSIS, VIKOR, ELECTRE, WSM) are applied to rank energy efficiency alternatives, providing a nuanced analysis of options for the cement industry. The study integrates sensitivity analysis to evaluate the robustness of MCDM methods under varying conditions, assessing the impact of changes in criteria weights on the ranking of energy efficiency alternatives and showcasing the adaptability of the proposed framework. Examining six diverse scenarios reveals the framework’s adaptability and the versatility of the Horizontal Roller Mill (HRM), with the Vertical Roller Mill (VRM) emerging as a cost-effective emission reduction alternative. This interdisciplinary approach, integrating information theory, decision science, and environmental engineering, extends beyond industry relevance, providing valuable insights aligned with global sustainability goals. Harmonizing economic viability with ecological responsibility, this report offers an instructive guide, propelling the cement industry toward a more sustainable future.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".