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Record W2805447980 · doi:10.1021/acs.iecr.8b00785

Optimization and Modeling of an Industrial-Scale Sulfuric Acid Plant under Uncertainty

2018· article· en· W2805447980 on OpenAlexafffund
Manuel Tejeda-Iglesias, Jason Szuba, Ron Koniuch, Luis Ricardez‐Sandoval

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2018
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsSulfuric acidProcess engineeringProfitability indexData scrubbingMaximizationPilot plantEnvironmental scienceProfit (economics)Work (physics)Process optimizationPulp and paper industryComputer scienceProcess (computing)Biochemical engineeringWaste managementEngineeringChemistryEnvironmental engineeringMathematicsMathematical optimizationMechanical engineering

Abstract

fetched live from OpenAlex

The production of sulfuric acid is an important process because of its many applications and its use as a mitigation strategy for SO 2 . Sulfuric acid plant reactors have been the focus of many studies, and thus there has been very limited work in the literature that has analyzed complete sulfuric acid plants. In this work, the flowsheet for an industrial-scale sulfuric acid plant with scrubbing tower is presented. The model was developed in Aspen Plus V8.8, and it was validated using historical data from an actual industrial plant. A sensitivity analysis was carried out followed by optimization using two alternative objective functions: maximization of plant profitability or productivity. The optimization was extended to consider uncertainty in key operating and economic parameters. The results show that changes could be made in the current optimal operating condition of the plant to improve the annual profit of the process.

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: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.101
GPT teacher head0.309
Teacher spread0.209 · 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

Citations17
Published2018
Admission routes2
Has abstractyes

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