Benchmarking Software for Slagging, Fouling and Other Parameters to Improve Coal-Fired Power Plant Load Factor, Efficiency and Emission
Bibliographic record
Abstract
For a 500 MW unit, a 1 % reduction in boiler efficiency equates to a coal cost in the order of $ 1/2 million/annum, while a 5 % unit derate, needed to meet emission compliance, can equate to an annual revenue loss of up to $ 12 million. Many software products are in use, which based on the present plant performance, identify and optimize the operational parameters for the best plant capacity, utilization and efficiency. While they are effective in tuning up the unit, they are applied to an operating plant firing a given coal for the given performance. But what if there were a means of predicting how a coal will perform with respect to slagging, fouling and every other single parameter involved in the use of that coal for capacity, efficiency maximization and emission control without firing even a lb of coal? This would enable the operator to know in advance what to expect and thereby, adjust the operating variables to get the best out of that coal. Such a software product – SCES (Steam Coal Evaluation & Services) has been developed based on the fundamental principles of combustion, mineral matter transformation and emission of particulates, NOx, SO2 and mercury, based on only the standard ASTM coal and ash analyses. The operational parameters evaluated are: Slagging and fouling as well as Grindability, Abrasion of the grinding elements; Combustibility & Unburnt carbon, Corrosion and Erosion; Emission of particulates, the oxides of sulfur and nitrogen (both primary and secondary DeNOx) as well as mercury when data are available. Its advantage over the existing products is its ability to predict, amongst others parameters, corrosion, erosion of convective tubes and the life of grinding elements none of which are discernible from the existing software products or from a limited test burn. These parameters however play important roles in the bottom line O & M costs. The implementation of SCES in providing meaningful rankings alerts the plant operators to the performance they can expect and any measures that need to be taken to be able the plant to operate at its highest load factor and efficiency while under emission compliance with minimum impact on O & M cost. No separate coal sample or small-scale laboratory evaluation work is required in the derivation of the rankings. Because of its simplicity of use and immediate availability of results, SCES can also be used as a routine analytical tool, accompanying the coal analyses by the plant or supplied with each delivery of coal. It is applicable to all coals irrespective of rank and country of origin, it has been used and validated on coals from the US, UK, Russia, Columbia and India. The paper describes the fundamental properties coal used in the development of the software and cites case histories of its validation on US (Bituminous & Sub-bituminous) coals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".