Pathways to Alternative Power and Decarbonization Technologies in Cement Manufacturing
Bibliographic record
Abstract
Energy costs are typically the single largest variable production cost at cement plants, reaching up to 50% of overall operating costs. The cement making process, which involves crushing, grinding and calcination processes, consumes large amounts of thermal and electrical energy which is often sourced either directly or indirectly from fuels like coal, coke and alternate fuels (for example, tires, biomass, green/blue hydrogen, plastics, wood chips, etc.). While these fuels are often procured at attractive prices, they can result in relatively higher carbon emissions.To remain competitive in a global market and to manage stakeholder pressure to reduce carbon emissions, plants are constantly searching for ways to reduce costs and mitigate climate change-related risks such as carbon pricing and reputational risks. As a result, cement producers have begun to focus on how they source energy and have been implementing solutions that are providing incremental reductions to their greenhouse gas emissions, energy consumption, and, consequently, operating costs.This paper presents an approach for mapping emissions, then identifying, assessing and prioritizing solutions for abating carbon. In addition to presenting an approach, this paper also discusses a handful of technologies and case studies where novel, carbon-abating technologies were evaluated and deployed for large global industrial clients. The use of renewable power and hybrid grids, plasma flames, fleet electrification, off-gas cogeneration and oxygen enriched combustion are some of the potential technology solutions that are discussed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".