BIOGAS PRODUCTION USING ANAEROBIC BIODIGESTER FROM CASSAVA STARCH EFFLUENT WITH RUMINANT BACTERIA AS BIOCATALYST
Bibliographic record
Abstract
IKMs’ factory activity in Margoyoso produces liquid and solid wastes. The possible alternative was to use the liquid effluent as biogas raw material. This study focuses on the used of urea, ruminant, yeast, microalgae, the treatment of gelled and ungelled feed for biogas production, pH control during biogas production using buffer Na2CO3, and feeding management in the semi-continuous process of biogas production that perform at ambient temperature for 30 days. Ruminant bacteria, yeast, urea, and microalgae was added 10 % (v/v), 0.08% (w/v), 0.04 % (w/v), 50 % (v/v) of mixing solution volume, respectively. The pH of slurry was adjusted with range 6.8-7.2 and was measured daily and corrected when necessary with Na2CO3. The total biogas production was measured daily by the water displacement technique. Biogas production from the ungelling and gelling mixture of cassava starch effluent, yeast, ruminant bacteria, and urea were 22,701 ml and 6,198 ml. Biogas production from ungelling mixture without yeast was 1,830 ml. Biogas production from ungelling mixture added by microalgae without yeast was 1,835 ml and that with yeast was 5,904 ml. Biogas production from ungelling mixture of cassava starch effluent, yeast, ruminant bacteria, and urea in semi-continuous process was 18,161 ml. Adding of microalgae as nitrogen source did not give significant effect to biogas production. Adding of yeast was very helpful to accelerate biogas production. The biogas production increased after adding of cassava starch effluent and yeast. Requirement of Na2CO3 to increase alkalinity of fermenting slurry depends on pH-value. Key Words: biogas cassava; C/N ratio; ruminant bacteria; semi-continuous biodigester; yeast
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".