Kinetic and thermodynamic modelling of thermal decomposition of bitumen under high pressure enhanced with simulated annealing and artificial intelligence
Bibliographic record
Abstract
Abstract Thermogravimetric analysis (TGA) of energy resources including bitumen provides kinetic parameters, which can be applied in design and simulation of processes for thermal recovery and conversion. In this investigation, pressurized non‐isothermal decomposition of bitumen under inert environment has been studied. The kinetic parameters were calculated at different pressures (0.1, 0.5, and 1 MPa) and heating rates (10, 20, and 30°C/min) using the differential method. The parameters were later optimized using simulated annealing (SA) optimization algorithm. The results were subsequently validated by comparing the predicted conversion (α) with those of artificial neural network (ANN). Thermodynamic parameters (enthalpy, entropy, and Gibb's free energy) were also calculated using the optimized kinetic parameters. The TGA results show that weight loss and thermal conversion decreased as the total pressure increased from 0.1–1 MPa, at all heating rates. Conversely, the thermal conversion rate ( dα/dT ) was observed to first decrease with increasing pressure (0.1–1 MPa) within the low temperature oxidation (LTO) and fuel deposition (FD) regions. In contrast, it increased with increasing total pressure within the high temperature oxidation (HTO) region. Furthermore, it was observed that the activation energy ( E a ) increased with increasing pressures at all heating rates, while the frequency factor ( A ) was independent of the pressure or the heating rates. In addition, the thermodynamic parameters tend to increase with increasing pressure. Ultimately, the results established that the SA algorithm could be used to enhance the performance of the differential modelling method in calculating kinetic parameters and predicting the conversion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".