Development and validation of a novel process model for fluidized bed combustion: Application for efficient combustion of low‐grade coal
Bibliographic record
Abstract
Abstract Numerous models have been developed in Aspen Plus for the combustion of different coal types in fluidized bed reactors. However, these models are case‐specific, particularly with respect to coal type and bed reactor type, implying limitations to general application of these models. Moreover, these processes were generally developed step‐wisely by employing a series of model blocks to simulate fluidized bed reactors in Aspen Plus. In this study, a novel hybrid approach for modelling coal combustion has been implemented to comprehensively design a model for conversion of low‐grade coal under various operating conditions. The proposed model combines sequential modelling of drying/pyrolysis (devolatilization) and combustion of coal by means of conventionally used units (RYIELD and RGIBBS), and a newly used unit (FLUIDBED) in Aspen Plus. The model validation was performed by experiments on the combustion of low‐grade coal in a pilot‐scale circulating fluidized bed reactor (CFBR). Experimental data were used to further calibrate the Aspen Plus model and decrease model uncertainties. The results obtained from the developed simulation model were found to be in good agreement with the experimental data. Discrepancies of less than 15% were observed, in most of the predictions of molar fractions for the resultant flue gas composition, including NO x and SO x , emissions which were at ppm levels. As a result, the model can easily be used for design, scale‐up, and simulation of coal combustion as well as for other feedstock like biomass in fluidized bed with process optimization based on sensitivity analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".