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Record W2560818228 · doi:10.1002/cjce.22753

A soft sensor for the sulphur dioxide converter in an industrial smelter

2016· article· en· W2560818228 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSmeltingSulfur dioxideSoft sensorSulfurEnvironmental scienceCatalytic converterChemistryMetallurgyWaste managementMaterials scienceCatalysisInorganic chemistryEngineeringComputer science

Abstract

fetched live from OpenAlex

In metal productions from sulphide ores, sulphur dioxide (SO 2 ) is generated when the ore concentrate is smelted. To minimize emission of SO 2 to the atmosphere, sulphuric acid plants are used to convert SO 2 gas into sulphuric acid product in smelters. A SO 2 to sulphur dioxide (SO 3 ) converter, where SO 2 is oxidized to SO 3 with the help of catalyst, is the key unit in a sulphuric acid plant. For monitoring the converter, temperature of the reactor is extensively measured at various locations, but the concentration of SO 2 is barely measured or only measured at very limited points due to the difficulty and costs involved. In this paper, a soft sensor is developed to estimate the conversion ratio and concentration of SO 2 . The soft sensor is derived based on the steady state mass balance model of the converter and dynamic data analysis. From the proposed soft sensor, conversion ratio and concentration of SO 2 can be estimated from available industrial real‐time measurement.

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.

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 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 score0.461
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.013
GPT teacher head0.192
Teacher spread0.179 · 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