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Record W1567621886 · doi:10.5772/5985

Transient Runaway in a Fixed-Bed Catalytic Reactor

2008· book-chapter· en· W1567621886 on OpenAlexafffund
Redhouane Henda

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMaterials Science
TopicThermal and Kinetic Analysis
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExothermic reactionThermal runawayNuclear engineeringPacked bedMaterials scienceContinuous stirred-tank reactorCatalysisHeat transferMechanicsChemical reactorWaste managementChemistryProcess engineeringChemical engineeringThermodynamicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Fixed-bed tubular reactors have important commercial applications in the chemical and allied process industries. A typical reactor consists of a cylindrical tube filled with solid catalyst particles and is mounted in a vertical upright position. After the reacting fluid has entered the reactor, it moves along the packed bed and reacts on the catalyst particles to produce the desired products. While fixed-bed reactors, relatively to other types of catalytic reactors, are flexible, efficient, low-cost, and require low maintenance, their most serious disadvantage is poor heat transfer with attendant poor temperature control (e.g., see Bartholemew and Farrauto, 2006). When a highly exothermic reaction is carried out in a packed bed reactor some problems associated with the operation of the reactor may arise. In this case, if the reaction generates heat faster than it can escape the reactor, viz., through reactor walls, the temperature of the fixed bed increases. The reaction and heat generation rates then increase, which causes a further rise in the reactor temperature. This can result in thermal runaway, with the temperature increasing ever more rapidly until the reactants have been finally exhausted. Runaway, or parametric sensitivity, describes a situation in which a small change in an operating variable such as feed temperature, concentration, or flow rate induces a large change in the temperature profile of the reactor. Further, a number of runaways occur due to scale-up, inadequate procedures and training, raw material quality control, maintenance, etc Runaway can promote undesired side reactions, catalyst deactivation, productivity loss, and deterioration of product selectivity. Because of the risk associated with thermal runaway, many industrial reactors are operated under overly suboptimal conditions. It should be noted that near-runaway operation is potentially advantageous to catalytic processes as it may result in optimum reactor operation, especially if energy savings can be attained. For these reasons, understanding thermal runaway is of significant industrial importance. The concept of thermal runaway was first introduced to chemical reactor analysis in late 1950s (Bilous and Amundson, 1956). There have been many studies in the literature on the derivation of runaway criteria, e.g., in tubular and fixed-bed reactors with a single reaction (

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.886
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.017
GPT teacher head0.208
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2008
Admission routes2
Has abstractyes

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