Comprehensive Modeling and Numerical Investigation of Entrained-Flow Coal Gasifiers
Bibliographic record
Abstract
Numerical simulations of coal gasification process inside a generic 2-stage entrainedflow gasifier are carried out using the commercial CFD solver ANSYS/FLUENT. The 3-D Navier-Stokes equations and eight species transport equations are solved with three heterogeneous global reactions, three homogeneous reactions, and one thermal cracking equation of volatiles. Finite rates are used for the heterogeneous solid-gas reactions. Both finite rate and eddy-breakup combustion models are calculated for each homogeneous gas-gas reaction, and the smaller of the two rates is used. Lagrangian-Eulerian method is employed. The Eulerian method calculates the continuous phase while the Lagrangian method tracks each coal particle. Fundamental study is carried out to investigate effects of five turbulence models (standard k-ε, k-ω, RSM, k-ω SST, and k-ε RNG) and four devolatilization models (Kobayashi, single rate, constant rate, and CPD) on gasification simulation. A study is also conducted to investigate the effects of different operation parameters on gasification process including coal mixture (dry vs. slurry), oxidant (oxygen-blown vs. air-blown), and different coal distributions between two stages. Finite-rate model and instantaneous gasification model are compared. It is revealed that the instantaneous gasification approach can provide an overall evaluation of relative changes of gasifier performance in terms of temperature, heating value, and gasification efficiency corresponding to parametric variations, but not adequately capture the local gasification process predicted by the finite rate model in most part of the gasifier. Simulations are performed to help with design modifications of a small industrial demonstration entrained-flow gasifier. It is discovered that the benefit of opening the slag tap on the quench-type gasifier wider by allowing slag to move successfully without clogging is compromised by increased heat losses, reduced gasification performance, downgraded syngas heating value, and increased unburned volatiles. The investigation of heat transfer on fuel injectors shows that blunt tip fuel injector is less likely to fail compared to conical tip fuel injector because the maximum high temperature on the injector is scattered. Two concentric fuel/oxidant injections provide better fuel-oxidant mixing and higher syngas heating value than four separate fuel and oxidant injections.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".