Final Report - Chemical Industry Corrosion Management - A Comprehensive Information System (ASSET 2)
Notice bibliographique
Résumé
The research sponsored by this project has greatly expanded the ASSET corrosion prediction software system to produce a world-class technology to assess and predict engineering corrosion of metals and alloys corroding by exposure to hot gases. The effort included corrosion data compilation from numerous industrial sources and data generation at Shell Oak Ridge National Laboratory and several other companies for selected conditions. These data were organized into groupings representing various combinations of commercially available alloys and corrosion by various mechanisms after acceptance via a critical screening process to ensure the data were for alloys and conditions, which were adequately well defined, and of sufficient repeatability. ASSET is the largest and most capable, publicly-available technology in the field of corrosion assessment and prediction for alloys corroding by high temperature processes in chemical plants, hydrogen production, energy conversion processes, petroleum refining, power generation, fuels production and pulp/paper processes. The problems addressed by ASSET are: determination of the likely dominant corrosion mechanism based upon information available to the chemical engineers designing and/or operating various processes and prediction of engineering metal losses and lifetimes of commercial alloys used to build structural components. These assessments consider exposure conditions (metal temperatures, gas compositions and pressures), alloy compositions and exposure times. Results of the assessments are determination of the likely dominant corrosion mechanism and prediction of the loss of metal/alloy thickness as a function of time, temperature, gas composition and gas pressure. The uses of these corrosion mechanism assessments and metal loss predictions are that the degradation of processing equipment can be managed for the first time in a way which supports efforts to reduce energy consumption, ensure structural integrity of equipment with the goals to avoid premature failure, to quantitatively manage corrosion over the entire life of high temperature process equipment, to select alloys for equipment and to assist in equipment maintenance programs. ASSET software operates on typical Windows-based (Trademark of Microsoft Corporation) personal computers using operating systems such as Windows 2000, Windows NT and Vista. The software is user friendly and contains the background information needed to make productive use of the software in various help-screens in the ASSET software. A graduate from a university-level curriculum producing a B.S. in mechanical/chemical/materials science/engineering, chemistry or physics typically possesses the background required to make appropriate use of ASSET technology. A training/orientation workshop, which requires about 3 hours of class time was developed and has been provided multiple times to various user groups of ASSET technology. Approximately 100 persons have been trained in use of the technology. ASSET technology is available to about 65 companies representing industries in petroleum/gas production and processing, metals/alloys production, power generation, and equipment design.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,080 | 0,088 |
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 source (Gemma direct ou Codex distillé), 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 ».