Influence of the Construction Materials Properties of the Biodigester on the Biogas Production and Electricity Generated by the Slaughterhouse Waste
Bibliographic record
Abstract
The objective of this study was to carry out a comparative analysis of the influence of the properties of the construction materials of the biodigester based on three different materials (steel, plastic PVC, and concrete) to predict the rate of biogas production from the anaerobic digestion of slaughterhouse waste. The input parameters were substrate temperature, ambient temperature, biogas temperature, biodigester temperature, specific biogas production rate, and material properties. A thermal model was developed using MATLAB® software to predict biogas production, with readily available input data for an unheated, uninsulated, and partially buried biodigester. The results obtained showed that the temperatures and the average daily biogas productions were higher for the steel biodigester (1.5 ± 0.12 m3/day at 36 ± 2℃) than those produced from the PVC (1.3 ± 0.1 m3/day at 31 ± 1.5℃) and concrete (1.2 ± 0.05 m3/day at 27 ± 2℃) biodigesters. Moreover, the production of electricity for a steel biodigester (14.64 kWh) was found to be greater than that produced from the PVC (12.81 kWh) and concrete (10.98 kWh) biodigesters. The results showed that the properties of the construction materials of the digester had a significant influence on the temperature and production of biogas, and therefore on the production of electricity. On the other hand, among the three materials studied, steel was the material, which yielded the best results. The proposed model gave rRMSE values between 7.4 and 8.3% and R2 between 0.92 and 0.96.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".