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Record W2219668343 · doi:10.1139/cgj-2015-0453

Modelling long-term hydraulic conductivity behaviour of zero valent iron column tests for permeable reactive barrier design

2015· article· en· W2219668343 on OpenAlexvenueno aff
Nicola Moraci, Domenico Ielo, Stefania Bilardi, Paolo S. Calabrò

Bibliographic record

VenueCanadian Geotechnical Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsnot available
FundersMinistero dell’Istruzione, dell’Università e della Ricerca
KeywordsHydraulic conductivityCorrosionZerovalent ironPrecipitationExpansiveMaterials scienceGeotechnical engineeringConductivityMechanicsMetallurgyEnvironmental scienceChemistryGeologyComposite materialSoil scienceSoil waterAdsorptionCompressive strengthPhysics

Abstract

fetched live from OpenAlex

In this paper, a numerical model to simulate the hydraulic conductivity reduction observed during long-term laboratory column tests is proposed. The column tests are carried out to study dissolved heavy metals removal by using granular zero valent iron (ZVI). The proposed model is also used to analyse the main causes of hydraulic conductivity reduction observed during laboratory column tests. Expansive iron corrosion, precipitation of reaction products, and gas formation are the processes considered in the proposed model. Numerical simulation results show that to reproduce hydraulic behaviour of the experimental systems, the change of pores geometry due to expansive iron corrosion and precipitation of reaction products, which determines a possible stoppage of gas bubbles, should be considered. Furthermore, model results show that only a small percentage of the iron available is corroded during the tests (from 0.4% to 1.9%). According to the model, the average diameter of gas bubbles that better fit the experimental results varies between 0.16 and 0.19 mm. While assuming gas absence (or its possible escape), higher values of iron corrosion rate should be considered to fit the experimental results.

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)0.000
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 score0.295
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.051
GPT teacher head0.248
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 teacher head, 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

Citations72
Published2015
Admission routes1
Has abstractyes

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