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Record W3111902430 · doi:10.1002/cjce.23996

Maximization of the profit and reactant conversion considering partial pressures in an ammonia synthesis reactor using a derivative‐free method

2020· article· en· W3111902430 on OpenAlexvenueno aff
Kennedy B. Matos, Esdras P. Carvalho, Mauro A.S.S. Ravagnani

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMaximizationExothermic reactionAmmonia productionMathematical optimizationAmmoniaDerivative (finance)Profit maximizationPartial derivativeComputer scienceChemistryMathematicsThermodynamicsProfit (economics)EconomicsPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In the present paper, an optimization model is proposed for the ammonia synthesis reactor. The reversible exothermic reaction has nitrogen and hydrogen as reactants, at high temperatures, and high pressures with iron catalyst. Two different single‐objective optimization problems were considered, the maximization of the economic return and the maximization of the nitrogen conversion. The reaction rate model was defined as a function of partial pressures of the components. The problem was coded and solved in MATLAB using a derivative‐free method after reformulating a constrained optimization problem into an unconstrained one, by penalizing the infeasibilities of the constraints in the objective functions (barrier function). The main contributions of this paper are the combination of direct‐search methods in solving the optimization problem, the evaluation of the maximum ammonia production and the temperature profile. The optimal values found were better than the ones published in the literature.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.028
GPT teacher head0.225
Teacher spread0.197 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations2
Published2020
Admission routes1
Has abstractyes

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Same venueThe Canadian Journal of Chemical EngineeringSame topicAmmonia Synthesis and Nitrogen ReductionFrench-language works237,207