Environmental Issues and Challenges for Coal/Lignite based Thermal Power Plants and Mitigation Measures
Bibliographic record
Abstract
The world is undergoing a massive energy transition. Over the next decades, there will be radical changes in the way we produce and consume energy. The conventional energy infrastructure being set up now would be abandoned ever before their economic life is over. A quarter of India’s population have no access to electricity and our per capita consumption of electricity is very low at almost one third of world average with millions getting power a few hours a day. Surprisingly the plant load factor (PLF) for thermal power plant have steadily declined over the last four years and was only 63.6% in Sep. 2015. Breakthrough in solar plant technology posed a challenge for fossil fuel based thermal power plants. In April 2017, the tariff of utility connected large solar power projects in India hit a record low of Rs.3/kWh. This is the same as the average tariff of coal based power. At this tariff, solar plants are cheaper than a large number of new and old coal fired power plants in India.Stringent pollution norms add to the cost of generation of thermal power. Several projects in the power sector have become unviable on account of states like Andhra Pradesh, Uttar Pradesh and Karnataka have either cancelled the power purchase agreement or reworking on them. Few more states may follow in cancellation of power purchase agreement (PPA). Distribution Companies (DISCOMS) are reluctant to sign PPA because of high tariff, demand uncertainties, long duration of PPA and need to take the responsibility of fixed charges for the life of PPA. This will lead to creation of non-performing asset (NPA) worth Rs.1.5 lakh crores and to the extent the bank loans will be at risk.Some mitigation measures are suggested to meet the challenges in operation of coal/lignite based thermal power plants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".