HIGH-SOLID ANAEROBIC CO-DIGESTION OF FOOD WASTE AND DAIRY MANURE: A PILOT SCALE STUDY AT LOW-TO-MODERATE TEMPERATURE CONDITIONS
Bibliographic record
Abstract
Treating organic solid wastes economically is a challenge, predominantly in cold and high-altitude regions. Objective of this research was to determine the operating strategies to reduce the start-up phase of high-solid anaerobic digestion (HSAD) process and to improve the digestion of food waste (mainly fruits and vegetable wastes [FVW]) with or without animal manure in a low-cost AD system at 20-25°C. In addition, this study aimed to obtain the basic design criteria for starting up of scaled-up HSAD system using adapted liquid inoculum. Inoculum to feedstock ratio was varied from 6:1 to 3:1. The organic loading rate (OLR) expressed as volatile solids (VS) and operational cycle length was varied from 0.44 -2.1 KgVS Kginoculum-1 d-1 and 33 -14d, respectively. Obtained results show that methane (CH4) production from FVW was feasible at low-to-moderate temperature and specific methane yield of 0.4-0.6 L gVS-1 was observed even at high OLR. CH4 conversion rates and its quality were not affected, while maintaining the operational stability (e.g. no acidification or VFA accumulations). CH4 content reached over 60Ϻnd remained almost steady. Results also suggest that HSAD process at 25°C is comparatively efficient in saving heat energy and at the same time obtains the CH4 values close to mesophilic conditions. This means that the smaller size digester (in the case of HSAD) is preferred as there is no waste dilution involved and also suitable for cold countries. Using this concept, livestock producers can play a role in reducing GHG emissions while also earning C-offset credits.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".