The Scale-up and Economic Evaluation of Non-Thermal Plasma Technology for a Coal Fired Power Plant Exhaust Gas Emission Control
Bibliographic record
Abstract
Abstract Economies of pollution control devices are critical to the decision-making in power plant emission control upgrades. The computer code (SUENTP) to predict scale up and economic evaluation of several eligible non-thermal plasma processes for power plant gaseous pollution controls was developed for electron beam, pulsed corona, and corona radical shower processes. This code was written by the spread sheet type MS Excel with visual basic for application and comprises data input procedure, scale-up (design) procedure, economic calculation procedure, and output procedures. Data obtained from pilot plant tests was used as an input data together with general data so that they might be led to the conceptual design data of commercial plants by scaleup procedure. The economic evaluation procedure consisted of the total capital investment and the total annual cost. The total capital investment was presented as the indirect annual cost in items of capital recovery. The levelized cost and the levelized bus bar cost were shown in the output table. Typical calculation was presented to evaluate the cost of three non-thermal systems based on existing pilot plant experiments. The results show that the economy of the non-thermal plasma systems are similar or less costs by compared with a conventional wet-scrubber/selective catalytic reduction combined system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".