Predicting cost of dairy farm-based biogas plants: A North American perspective
Bibliographic record
Abstract
Livestock manure and organic agriculture wastes are an environmental challenge because they contribute to climate change by emitting greenhouse gases. Converting these organic wastes to biogas and bioenergy is a sustainable solution. Farmers, investors, and governmental departments involved in developing on-farm biogas projects need an informed decision-making process to fund such projects. Thus, estimating the required initial investment for a farm-based biogas plant is crucial. This study aims to develop two methods to estimate the cost of farm-based biogas projects, determine their economic viability, and predict the cost of each part of the plant and its related risks. A database for farm-based biogas projects in Canada and the USA was established and analyzed before developing the models. First, six mathematical models were developed using linear regression to predict the capital cost, engineering and design, operation and maintenance, gross revenue, and net profit using Monte Carlo simulation. Second, the probability of cost of components is calculated. The marginal error of cost prediction in initial modeling is about 7% in total, and the economic viability of a biogas plant for a farm housing less than 300 cows is questionable.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".