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Record W2155214270 · doi:10.1080/09593332508618392

Lignor™ Process for Acidic Rock Drainage Treatment

2004· article· en· W2155214270 on OpenAlexaboutno aff
J. Ming Zhuang, T. Walsh

Bibliographic record

VenueEnvironmental Technology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsnot available
Fundersnot available
KeywordsLignosulfonatesLimeChemistryPulp and paper industrySewage treatmentWaste managementEnvironmental chemistryEnvironmental engineeringLigninEnvironmental scienceMaterials scienceMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

The process using lignosulfonates for acidic rock drainage (ARD) treatment is referred to as the Lignor process. Lignosulfonates are waste by-products produced in the sulfite pulping process. The present study has shown lignosulfonates are able to protect lime from developing an external surface coating, and hence to favor its dissociation. Further, the addition of lignosulfonates to ARD solutions increased the dotting and settling rate of the formed sludge. The capability of lignosulfonates to form stable metal-lignin complexes makes them very useful in retaining metal ions and thus improving the long-term stability of the sludge against leaching. The Lignor process involves metal sorption with lignosulfonates, ARD neutralization by lime to about pH 7, pH adjustment with caustic soda to 9.4 - 9.6, air oxidation to lower the pH to a desired level, and addition of a minimum amount of FeCl3 for further removal of dissolved metals. The Lignor process removes all concerned metals (especially Al and Mn) from the ARD of the Britannia Mine (located at Britannia Beach, British Columbia, Canada) to a level lower than the limits of the B.C. Regulations. Compared with the high-density sludge (HDS) process, the Lignor process has many advantages, such as considerable savings in lime consumption, greatly reduced sludge volume, and improved sludge stability.

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 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 score0.057
Threshold uncertainty score0.868

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)0.0010.001

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 head0.008
GPT teacher head0.240
Teacher spread0.233 · 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

Citations4
Published2004
Admission routes1
Has abstractyes

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