The characterization of biodiesel wash water and the potential for microbial remediation
Bibliographic record
Abstract
Biodiesel is a fuel produced from vegetable oils or other lipids that can be substituted for petroleum diesel in many internal combustion engines. Substitution of biodiesel for petroleum diesel has the potential to reduce green house gas emissions, decrease dependence on fossil fuels, add value to agriculture products and localize energy production. The production of biodiesel is a straight forward process and the scale of production varies from backyard brewers producing twenty litres at a time, to large industrial operations which produce thousands of litres. Biodiesel production in Ontario will see a great expansion in the next few years. Amendments to the Clean Air Act in 2006 incorporate a mandate that 2 % renewable content be blended into all diesel fuel by 2012. Since biodiesel is the primary fuel blended with petroleum diesel, the production of biodiesel in Canada will need to increase approximately five-fold from today's capacity. Raw biodiesel must be refined and one of the most common approaches is water washing, in which clean water is passed through the biodiesel. Water is an excellent medium for neutralizing residual catalyst, as well as removing residual methanol and glycerol. However, the resulting biodiesel wash water (BWW) is high in organics and cannot be disposed of in municipal waste streams. Biodiesel wash water from several laboratory and industrial biodiesel production facilities was characterized. The lab produced BWW chemical oxygen demand (COD) levels were 150,000 ± 20,000 mg/L and total solids content averaged 11,170 ± 600 mg/L of which the majority was total dissolved solids. Soap content averaged 7,900 ± 800 mg/L and a high pH near 10 was commonly seen. The industrial samples had higher levels of COD (754,200 ± 162,600 mg/L) and solids (328,900 ± 24,300 mg/L again mostly containing dissolved solids). Soap content was typically 778,100 ± 306,500 mg/L, and pH ranged from very alkaline (10 ± 0.4) to very acidic (1.1 ± 0.2). Biodiesel wash water nutrient content was very limited, with typical C:N:P of 2,430:0.7:1. A wide range of contaminant concentrations in BWW was common and dependent on the details of the production process. Biological remediation of BWW using algae and fungi was investigated. Algal and fungal environmental isolates from near Ridgetown ON, and known stock isolates were also screened for their ability to grow in industrial and lab BWW. Out of fifty six algal isolates, only a few grew in very dilute BWW. Growth was inhibited by residual methanol and soap in the BWW. In contrast, nineteen of the fifty-eight fungal isolates grew better than controls in dilute BWW. When grown in 10% BWW diluted in potato dextrose broth (PDB), several fungal isolates were successful in lowering COD by 30 to 40%. Mycoremediation was found to have potential for treating BWW before disposal and the use of fungal biomass for secondary bioenergy or bioproduct applications would improve the feasibility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".