Benchmarking Software for Slagging, Fouling and Other Parameters to Improve Coal-Fired Power Plant Load Factor, Efficiency and Emission
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».