Evaluation of wood biomass utilization for the greenhouse industry in British Columbia
Bibliographic record
Abstract
The Canadian greenhouse industry is challenged by high operating costs as the natural gas price has been increasing and fluctuating over the past few years. Natural gas is the primary energy used by the greenhouse industry to generate heat and carbon dioxide (CO₂) to enhance the crop productivity. There are concerns about the global warming effect caused by natural gas usage as it is a non-renewable energy. Therefore, the greenhouse industry is considering the use of wood biomass, mainly wood pellets and wood residue, to decrease the natural gas demand. However, the long-term economic value, the air quality emission, and the long-term resource availability are the main barriers for the industry to convert into wood biomass boiler. The main objectives of this study are, to evaluate the economic feasibility of using wood biomass for the greenhouse heating application, to analyze the associated impacts of the technical and economical changes, and to determine the optimal biomass mix with the consideration of emission limits and resources availability constraints. Specific case studies will be considered in this research to achieve the mentioned objectives. The results of the techno-economic analysis showed positive net present value (NPV) for the four cases considered: using wood pellets or wood residue boiler, with or without an electrostatic precipitator (ESP), to generate portion of heat demand for a greenhouse. Although the decision making would be affected by the price changes and the size of a greenhouse, a positive NPV was determined from a pure economic view point. Wood biomass combustion could reduce over 3,000 tonnes of CO₂ equivalent greenhouse gases annually. Sensitivity analyses indicated that wood biomass attractiveness would increase with higher natural gas prices or larger energy contributions from wood biomass. The optimization study suggests a feasible biofuels mix for the case study of a 2 ha flower greenhouse and a 7.5 ha vegetable greenhouse. The model was solved considering the inclusion of an ESP system to ensure the air quality limits were satisfied. The optimal result was compared to an existing 2 ha flower greenhouse and revealed almost 20% reduction on the total fuel cost per year.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".