Optimization of liquid state bioconversion process for microbial treatment of domestic wastewater sludge
Bibliographic record
Abstract
The evaluation of cheap carbon sources (co-substrate) and process conditions for microbial treatment of domestic wastewater sludge (DWS) was investigated using a liquid state bioconversion (LSB) laboratory scale process. Six cheap carbon sources were used: wheat flour (WF), rice flour (RF), corn flour (CF), sago starch (SS), cassava starch (CS), and commercial sugar (S). Process conditions such as co-substrate(s) concentration, temperature, initial pH, and inoculum size were optimized in terms of dry filter cake (DFC) production, removal of total suspended solids (TSS), turbidity (optical density against distilled water, 660 nm), and chemical oxygen demand (COD) in supernatant. The results obtained indicated that sludge containing wheat flour as a carbon source was a better co-substrate for microbial growth, with significant utilization for minimizing the dissolved and suspended materials in sludge. The optimized parameters for fungal treatment of DWS obtained were 1.52% (w/w) of WF concentration, temperature of 3335°C, initial pH of 4.55.5, and inoculum size of 23% v/w. Effective results for optimum values were recoded at 4 d of microbial treatment.Key words: liquid state bioconversion, domestic wastewater sludge, process conditions, cheap carbon sources, microbial treatment.
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 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".