Opportunities for CO2 Capture and Storage in Iranian Electricity Generation
Bibliographic record
Abstract
CO2 capture and storage (CCS) systems are technologies that can be used to reduce CO2 emissions by different industries where combustion is part of the process. A major problem of CCS system utilization in electricity generation industry is their high efficiency penalty in power plants. For different types of power plants fueled by oil, natural gas and coal, there are three main techniques that can be applied: • CO2 capture after combustion (post-combustion); • CO2 capture after concentration of flue gas by using pure oxygen in boilers and furnaces (oxy-fuel power plant); • CO2 capture before combustion (pre-combustion). More than 90% of electricity generation in Iran is based on fossil fuel power plants. Worldwide, electricity generation is responsible for 54% of GHG emissions. Thus, it is vital to reduce CO2 emission in fossil fuel-fired power plants. In this paper, it is shown that, by applying CO2 capture systems in power generation industry, very low CO2 emission intensity is possible but the energy and economic penalties are substantial. The analyses showed that for different technologies efficiency penalty could be as high as 25% and cost of electricity might increase by more than 65%. Two scenarios for Iranian electricity generation sector were investigated in this paper: installing CCS in the existing power plants with current technologies and replacing existing power plants by natural gas combined cycle plants equipped with CO2 capture system. The results revealed that the GHG intensity can be reduced from 610 to 79 gCO2eq/kWh in the first scenario and to 54 gCO2eq/kWh in the second scenario.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".