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Record W3111407233 · doi:10.1515/npprj-2020-0049

Concentrated sulfuric acid production from non-condensable gases and its effect on alkali and sulfur balances in pulp mills

2020· article· en· W3111407233 on OpenAlexaff
Andrés Mahecha‐Botero, Rohan Bandekar, Isabel M. C. L. Sêco, Igor Aksenov, C. Guy Cooper, Jon Foan, Kam Sirikan, Jim Wearing

Bibliographic record

VenueNordic Pulp & Paper Research Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsPROTO Manufacturing (Canada)NORAM (Canada)
Fundersnot available
KeywordsSulfuric acidPulp and paper industryPulp (tooth)Sulfur dioxideSulfurEnvironmental scienceWaste managementPapermakingMillProcess engineeringPaper millChemistryEngineering

Abstract

fetched live from OpenAlex

Abstract The pulp and paper industry often encounters challenges that require process improvements to remain competitive. These challenges may include the requirement to meet more stringent environmental regulations, stricter energy policies, or the need to improve product quality, increase production capacity and profitability. As a result, the pulp mills of today have to focus on becoming more efficient by possessing an effective chemical recovery system and reducing chemical losses. The high degree of closure is beneficial for environment, water consumption and mill economy but can upset the Na/S balance and increase the build-up of non-process elements in the system. Installing an acid plant to convert the sulfur containing Non Condensable Gases (NCG) into sulfuric acid will eliminate the NCG as a sulfur input to the recovery cycle, eliminate purchases of sulfuric acid, reduce caustic purchases, and produce additional steam that will positively impact the mill’s heat balance. This paper provides an overview of the OptimumAcid™ technology required to produce sulfuric acid in a pulp mill from NCG, presents some of the unique challenges related to feed variability, and discusses some of the technical features of NORAM’s sulfuric acid OptimumAcid™ process technology and equipment.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.040
GPT teacher head0.298
Teacher spread0.258 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2020
Admission routes1
Has abstractyes

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