Evaluasi Kehandalan Reaktor Biogas Skala Rumah Tangga di Daerah Istimewa Yogyakarta dengan Metode Analisis Fault Tree
Bibliographic record
Abstract
Biogas technology is one of the solutions for improving sanitation, environment, economy and energy conservation especially for smallholder farmers who are applying mixed crop and livestock farming.Indonesia Domestic Biogas Programme (BIRU) has been implemented in DIY since 2009.However, the household digesters that operate effectively only accounts for less than 50% of the total existing digesters in 2017.These problems should be identified and analyzed for more effective implementation and efficient operation of small-sized biogas system in the future.This research applied fault tree analysis (FTA) method to identify failures and evaluated their effects on the operation of small-sized biogas based on processes, physical component, and human factor point of view.Fourty-one sets of BIRU biogas were selected and sampled using stratified purposive random sampling method.Nineteen minimal cut set and three subsystems were defined, which included process failures, infrastructure failures, and human errors.The fault probabilities of the three subsystems were found to be 0.79; 0.59; and 0.96, respectively.It implied that human error gave the highest probability of errors, followed by process failure, while the physical structure of the reactor had been sufficiently well controlled.This study suggested that careful selection on prospective users should be conducted prior to installation, to ensure the motivation of the users in maintaining the reactor in good conditions.Besides, trainings and assistance system are also required to improve the skills of the user to maintain the performance of their reactor.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 0.001 |
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".