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Record W4390846953 · doi:10.5004/dwt.2023.29963

Optimization of a lithium assay method in oil fields brines by using experimental design methodology and its validation by the accuracy profile approach

2023· article· en· W4390846953 on OpenAlexaff
Kais Djebali, Anissa Somrani, Nizar Dabbech, Moufida Borni, Ghaïth Hamdaoui, Ramzi Zarrougui, Ahmed Hichem Hamzaoui

Bibliographic record

VenueDesalination and Water Treatment · 2023
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsLithium (medication)ChromatographyDesign methodsComputer scienceChemistryEngineeringMechanical engineeringBiology

Abstract

fetched live from OpenAlex

ABSTRACT To cover the excessive demand of lithium compounds, especially for the manufacture of vehicle batteries, several extraction technologies from natural resources and mainly geothermal brines are applied. These brines are distinguished by the complexity of its chemical composition. So, the quantification of lithium amount by spectroscopic methods will be disturbed by some interferences due to the cohabitation of major cations and anions. The originality of this investigated work is to optimize, by using the response surface methodology, and to validate, by the application of the accuracy profile approach, the analytical method for lithium assay by flame emission spectroscopy. After identification of the major ions in the oilfield brine sample (Ca, Mg, Na, K and Cl), a specificity test is applied, which reveals the presence of chemical interferences. For this reason, a spiked synthetic solution by a lithium standard (20 mg/L) is prepared for each run of the Doehlert design. Four studied factors, which are the major cations ([Ca 2+ ] ( X 1 ), [Mg 2+ ] ( X 2 ), [Na + ] ( X 3 ) and [K + ] ( X 4 )), are used to build this design and to determine their effects on the lithium recovery yield response ( Y (%)). As resulted outcomes, a polynomial valid and predictive model is statistically qualified and used to plot the iso-responses curves of the chosen response. Consequently, the optimal conditions are determined by: [Ca 2+ ] ( X 1 ) = 1.7 mol/L, [Mg 2+ ] ( X 2 ) = 0.02 mol/L, [Na + ] ( X 3 ) = 0.36 mol/L and [K + ] ( X 4 ) = 0.01 mol/L, which correspond to 64.128, 2.43, 60 and 2 g/L, respectively and the theoretical lithium recovery yield is nearby 98% in five-fold diluted composition. Moreover, the validation by the accuracy profile approach is performed after the robustness test. The resulted β-expectation tolerance interval is within the acceptability limits, which means that the analytical method, used for lithium assay by flame emission spectroscopy, is valid and the response variances are almost constant (homoscedasticity) over the predefined range concentration (8–50 mg/L) and it can be used for routine analysis.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.560
Threshold uncertainty score0.253

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 head0.072
GPT teacher head0.320
Teacher spread0.248 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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