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Record W4402115737 · doi:10.2118/0924-0120-jpt

Rhamnolipids Enhance Bioremediation of Petroleum-Contaminated Soil

2024· article· en· W4402115737 on OpenAlexaboutno aff
Chris Carpenter

Bibliographic record

VenueJournal of Petroleum Technology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsnot available
Fundersnot available
KeywordsBioremediationPetroleumEnvironmental scienceContaminationSoil contaminationWaste managementPetroleum engineeringEnvironmental chemistryGeologyChemistrySoil waterEngineeringSoil scienceEcologyBiology

Abstract

fetched live from OpenAlex

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 215952, “Enhancing Bioremediation of Petroleum-Contaminated Soil Using Rhamnolipids: A Combined Laboratory and Field Study,” by Pan Ni, Lehigh University; Yonglin Ren, SPE, Stepan Oilfield Solutions; and Derick G. Brown, Lehigh University, et al. The paper has not been peer reviewed. _ Hydrocarbon spills can occur at various stages of the oil and gas exploration and production process. Treating these spills onsite to avoid more-expensive excavation and incineration processes would be beneficial. The study outlined in the complete paper aims to optimize the use of rhamnolipid biosurfactants for enhancing the bioremediation of hydrocarbon-contaminated soil. The goal of this work was to explore the effects of rhamnolipid application on hydrocarbon degradation rate under both laboratory and field conditions and to examine the effects of this treatment on the indigenous soil microorganism population. Introduction The authors write that, to the best of their knowledge, previous research studies on rhamnolipid-based soil remediation focused on either laboratory or field tests and that comparison between laboratory-scale and field-scale tests is overlooked but necessary for practical applications. Materials and Methods Materials and Chemicals. The soil used for this work (23 kg for the laboratory test, 10 metric tons for the field test) was obtained from a contaminated site in Longford Mills, Canada. The soil properties are presented in Table 1 of the complete paper. The rhamnolipid stock solution featured 9.8 wt% rhamnolipid blends in deionized water. Another commercial remediation agent (enzyme-based) was used in the field test to compare with the performance of rhamnolipid. Ammonium chloride (NH4Cl) and potassium hydrogen phosphate (K2HPO4) also were used along with sodium hydroxide (NaOH). Experimental Setup. Both laboratory and field tests were ex-situ tests using simulated soil piles. The laboratory test was conducted for 193 days. For the laboratory experiments, an aerobic/anaerobic respirometer system equipped with a low-temperature incubator was used to monitor the in-situ oxygen uptake inside eight reactors (Fig. 1). Each reactor contained 250 g of soil with rhamnolipid doses of 0, 0.1, 0.5, and 1 g/kg. The temperature was set to 25°C. Inside each reactor was placed 10 mL of 4-M NaOH in a 20-mL glass beaker to adsorb the produced CO2. A parallel set of eight reactors was prepared and operated under the same conditions. The nutrients of nitrogen and phosphorous (NH4Cl and K2HPO4) were added at specific times based on the oxygen uptake rate monitored by the respirometer. To avoid the possible inhibition of the microbes in the soil as a result of high ionic strength (greater than 160 mM) in the soil pore water, NH4Cl and K2HPO4 were added so that the molar concentration in the soil pore water did not exceed 100 mM. The field test is being conducted in Longford Mills, Ontario, Canada, and is ongoing at the time of writing. For the field test, 10 metric tons of soil were mixed to achieve homogeneity before the preparation of the soil piles. Five different treatment conditions were examined using duplicate soil piles (10 piles total), with each consisting of 1 metric ton of soil. Nutrient addition was similar to that used in the laboratory tests. Temperature and moisture were monitored every few days at five points in each soil pile, and the pH was monitored once every month at five points.

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 categoriesInsufficient payload (model declined to judge)
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 score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.224
Teacher spread0.221 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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