Reduction of CO2 Emissions from Cement Plants
Bibliographic record
Abstract
Reduction of CO2 Emissions from Cement Plants \n \nGovernments around the world have been pressured by society to discuss environmental issues, and global warming is one of the most controversial debates. The Kyoto Protocol is an agreement made under the United Nations Framework Convention on Climate Change (UNFCCC). Under Kyoto protocol some countries committed to reduce their Greenhouse Gas (GHG) emissions. The Intergovernmental Panel on Climate Change (IPCC) has predicted global rise in temperature and carbon dioxide is a major greenhouse gas responsible for global warming. The cement industry contributes approximately five per cent of the total CO2 emitted worldwide. \n\tCurrently Canada sustains a very aggressive objective to reduce GHG emissions to support the Kyoto Protocol. It is clear that international affairs and global polices will affect different sectors and even though cement production and distribution is constrained by location and natural resource availability, the major cement producers around the globe will be required to meet more stringent environmental regulations. \nKyoto presents a ‘cap and trade’ mechanism that requires countries to reduce, on average, 5.2 per cent below their 1990 baseline. This reduction must take place between 2008 and 2012. Although these caps are country specific, most countries are requiring industries to have particular objectives for reduction. This can be seen especially in European countries. \nThe credit trade opportunity increases the possibility for an economical justification of new and environmentally friendly solution for GHG emissions abatement. \nSt Marys Plant, located in St Marys, Ontario, was used as a case study to evaluate the results of various modifications on cement plants operation that can impact on the plant CO2 emissions. An economic model which objective is to highlight the best selection strategy to reduce CO2 emissions with the least cost was developed using St Marys Plant data as part of this thesis. \n St Marys Plant achieved a significant result of 23.6 per cent reduction in CO2 emissions per tonne of cement produced. The results were achieved mainly by applying a progressive approach prioritising project implementation effort and feasibility. \nSt Marys main steps were 1) implementation of a more robust maintenance system, 2) plant optimization and Kiln expert system; 3) alternative fuels and 4) major equipment modifications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".