Final Report - Chemical Industry Corrosion Management - A Comprehensive Information System (ASSET 2)
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.080 | 0.088 |
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".