TECHNO-ECONOMIC ASSESSMENT OF ANAEROBIC DIGESTION SYSTEMS FOR AGRI-FOOD WASTES
Bibliographic record
Abstract
Activities in the Fraser Valley region of British Columbia generate some 3 million tonnes of agricultural and food wastes annually, 85% of which are estimated to be readily available for anaerobic digestion. The economic benefits of anaerobic digestion include: power and heat generation, biogas upgrading, and further processing of the residues to produce compost or animal bedding. An Anaerobic Digestion (AD) Calculator has been developed to assist users in their decision making process of investing in AD facilities. One objective is to classify the many currently available and feasible technology options into several major types of AD systems. Another objective is to construct kinetic and economic models for analyzing these systems. The calculator was developed, keeping in mind that it should be relatively simple yet providing fair estimation on biogas yield, digester volume, capital cost and annual income. Factors such as the degradability of wastes with different compositions and different operating parameters are taken into consideration. Economic assessment of alternative AD systems and biogas utilization options was performed. After the model was calibrated and validated, a fictitious 450-cows dairy farm located in the Fraser Valley was used for performing overall technical and economic feasibility analyses. Calculations were performed for two reactor configurations - continuous stirred tank and mixed plug flow, with different hydraulic retention times (HRTs). For a mixture of 80% dairy manure and 20% off-farm food waste, the computed results indicate that, a mixed plug flow AD system with HRT of 25 days has the best system performance.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".