Economic viability of a canadian biodigester for power generation in dairy farming
Bibliographic record
Abstract
This study aimed to analyze the economic viability of implementing and using a Canadian biodigester for power generation in a milk production system. Specifically, we intended to estimate the generated power production, the total production cost (TC), the total operating cost (TOC), and the effective operating cost (EOC) of 1 kW of power generated, and estimate the break-even point of the power produced. The research was carried out on a farm located in southern Minas Gerais (Brazil) from January to December 2017. Three scenarios were analyzed through the tree-point estimation (most likely, optimistic, and pessimistic). Scenario 1 considered the use of power for the acclimatization of free-stall barns during 13 hours day-1 plus the use of 50% of the produced biofertilizer. Scenario 2 considered the use of power during 13 hours day-1 for the acclimatization of free-stall barns and milking parlor, which means an increase in power consumption estimated at 10% compared to scenario 1, plus the use of 75% of the produced biofertilizer. Moreover, scenario 3 considered the use of power for the acclimatization of free-stall barns during 6.5 hours day-1 plus 25% of the produced biofertilizer. All scenarios considered the amount charged per kWh by the Companhia Energética de Minas Gerais (CEMIG). Monte Carlo simulations were carried out with minimum acceptable rates of return ranging from zero to 50%. Power generation was economically viable in all scenarios, with positive net present value (NPV), internal rates of return above the minimum acceptable rate of return, simple and discounted payback below the 10-year horizon, and satisfactory benefit-cost ratios. The EOC values of 1 kWh of power were estimated at R$ 0.1990, R$ 0.1791, and R$ 0.3308 for scenarios 1, 2, and 3, respectively, whereas the mean total cost (TC) was R$ 0.5618 (±0.21) considering all scenarios, above the purchase values at CEMIG, which would be R$ 0.50. The TC value was R$ 99,804.42 for scenarios 1 and 2, and R$ 92,424.09 for scenario 3, with a minimum acceptable rate of return of 8.50, while the TOC values were R$ 69,486.62 in scenarios 1 and 2, and R$ 62,229.66 in scenario 3. The amount of power generated in all scenarios was higher than the break-even point. All Monte Carlo simulation models showed positive NPV values, indicating that there is a high probability of being above expectations.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".