Development and Performance Evaluation of an Eco-Friendly Rotary Drum Roasting Machine for Maggot Processing Using Biomass Energy
Bibliographic record
Abstract
The burgeoning interest in maggot-based feed necessitates cost-effective production methods.Traditional roasting techniques for converting fresh maggots into fish feed pellets, predominantly reliant on liquefied petroleum gas (LPG) burners, impose substantial operational costs.Addressing this challenge, the current study introduces a biomass-fueled roasting machine, engineered to reduce energy expenditure and enhance environmental sustainability.Fabricated in a Yogyakarta-based workshop, the ecofriendly roaster features a stainless steel rotary drum (500 mm diameter, 1000 mm length, 3 mm thickness) actuated by a 2 HP electric motor at 28 rotations per minute.The machine's performance was evaluated through temperature profiling, roasting rate, useful heat generation, specific energy consumption (SEC), and overall effectiveness.These parameters were assessed during the roasting of 10 kg and 30 kg batches of fresh maggots over a two-hour period, utilizing teak firewood as the biomass energy source.The empirical findings indicated an optimized performance at the 30 kg capacity, with a roasting rate of 2.86 g/s, useful heat delivery of 62168.30kJ, an effectiveness of 0.69, and an SEC of 12798.78kJ/kg water evaporated.The introduction of this roasting machine not only simplifies the maggot pellet production process but also diminishes investment costs.More critically, it encourages the utilization of biomass waste, slashes operational expenses for maggot farmers, and aligns with the principles of sustainable agriculture.This study underscores the potential of integrating biomass-based technologies within the aquaculture feed industry, promoting both economic and environmental benefits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".