Production of biodispersants for oil spill remediation in Harsh environment using glycerol from the conversion of fish oil to biodiesel
Bibliographic record
Abstract
Oil and gas operations have moved from conventional petroleum reserves to unconventional petroleum reserves such as remote offshore, deep Ocean and the Arctic. The management of oil spills is especially challenging due to these conditions. Oil spills are typically due to vessels accidents, tanker discharges, wells, offshore platforms, drilling wastes, or release of refined petroleum products and their by-products, heavier fuels and the spill of any waste oil. Oil spills impact human, plants and wild life including birds, fish and mammals, and therefore the response strategies must attempt to minimize the impact to multiple receptors. In Arctic environments, traditional mitigation and response to oil spill are less effective due to low water/air temperatures, ice cover, and other environmental conditions. Dispersants are a common response method; however there are issues with respect to toxicity and effectiveness of chemically derived dispersants. Biologically derived surfactants and dispersants, produced by naturally occurring bacteria, have some advantages including rapid biodegradation and low toxicity over the synthetic surfactants. However, large scale production is limited because of high costs associated with growth medium and operations. Cost effective production of biosurfactants could be achieved by using industrial wastes and by-products as media/substrate, thereby decreasing expensive medium costs and reducing the environmental impacts of the wastes. In this study the feasibility of glycerol, derived from the conversion of waste fish oil to biodiesel, as an effective carbon source for the production of biodispersants by indigenous Rhodococcus erythropolis and Bacillus subtilis strain is investigated Glycerol, a tribasic alcohol, is a byproduct of the biodiesel production process. Biodiesel is produced via the transesterification reaction of triglycerides in oils or fats and waste oils, with alcohols, in the presence of a homogeneous catalyst (chemical or enzymatic). In general, for every 10 kg of biodiesel produced approximately 1 kg of crude glycerol. As the production of biodiesel increases so will crude glycerol. The glycerol market is a saturated market already, and therefore any alternative market for this byproduct is advantageous to the larger scale production of biodiesel production. The waste stream ability to produce biosurfactant by indigenous Bacillus subtilis and Rhodococcus erythropolis strains will be determined. The cultivations will be performed in 250 mL flasks containing 50 ml medium at room temperature, and stirred in a rotary shaker at 30 C and 200 rpm for 3-4 days. Biosurfactant productivity will be evaluated by surface tension measurement and emulsification index (E24) determination as response variables. The produced biodispersants would have the ability to be used as an effective method to minimize the impacts of spilled oils in offshore Newfoundland and Labrador.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".