Microbial Lipid Accumulation through Bioremediation of Palm Oil Mill Wastewater by <i>Bacillus cereus</i>
Bibliographic record
Abstract
The wastewater burden and increased energy demands have spurred interest in finding possible solutions that could concomitantly address both issues. Microbial lipid production, together with the bioremediation of wastewater, could be a promising solution to address both problems. In this context, the present study was designed to evaluate the potential of Bacillus cereus (B. cereus) for accumulating microbial lipids through the bioremediation of palm oil mill effluent (POME) in batch mode fermentation. Different dilutions of POME media (25%, 50%, 75%, and 100%) were used to determine the optimum POME concentration for enabling the maximum yield of biomass and maximum lipid production. The 50% (v/v) POME was found to have potential for the highest biomass growth (8.09 g/L) and lipid accumulation (1.46 g/L), with a lipid content of 18.04% (dry weight basis). B. cereus accumulated a higher biomass and lipid content than other bacterial strains, such as Rhodococcus opacus and Pseudomonas aeruginosa, under similar conditions. On the other hand, the degree of bioremediation was assessed by evaluating several wastewater parameters and determining the seed germination index (GI) of Vigna radiata. POME treated with B. cereus displayed higher GI values than the untreated samples due to the significant remediation of detrimental organics present in the POME. This finding was further confirmed by the substantial reduction in pollution load, particularly in chemical oxygen demand (COD) and biochemical oxygen demand (BOD) for 50% POME, thus demonstrating removal efficiencies of 79.35% and 72.65%, respectively. Therefore, the results of this study suggest that B. cereus cultivation in POME could be a promising technique for attaining higher biomass growth and lipid production in conjunction with the bioremediation of POME. The approach of achieving dual objectives that is utilized in the present study provides a novel strategy for palm oil millers.
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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".