Advanced Energy Modelling and Life Cycle Assessment of Indoor Agriculture
Bibliographic record
Abstract
This thesis investigates the agricultural greenhouse sector in a cold climate, which requires a large amount of natural gas for supplying the substantial heating demands. The heating demand of these structures is calculated, and potential sustainable design methods are implemented to reduce the reliance on carbon-based fuels. Assessment of the environmental impacts of a bell pepper greenhouse in Southwestern Ontario, Canada heated by natural gas was studied. A life cycle assessment (LCA) method is employed to scrutinize the bell pepper greenhouse, pinpointing areas that need improvement. It was concluded that Global Warming (GW) is the significant environmental hazard among other environmental categories (3.87e-2 kg ??2-Eq). It should be noted, the main contributor to global warming is the natural gas being used as the heating resource (3.2e-2 kg ??2-Eq). The analysis is extended to explore the deployment of solar energy as an alternative source for heating. Solar energy is found to be a superior alternative in terms of emissions. Furthermore, in order to integrate solar energy into the energy supplying systems of the greenhouses, a hybrid Solar Thermal/Photovoltaic-Battery Energy Storage (ST/PV-BES) system is modeled. Evaluation of the best configuration of photovoltaic (PV) and solar thermal (ST) modules, and battery energy storage (BES) size to have the minimum Levelized Cost of Energy (LCOE) was conducted. It is proved that the system is economically optimized. Moreover, to improve operational efficiency and reduce the energy demand of commercial greenhouses, parametric optimization of major growing environment variables including cladding material and window to wall ratio as well as the characteristics of the solar thermal model elements such as hot water tank capacity and heat exchanger effectiveness was carried out. It is demonstrated that the best greenhouse configuration which is a system with 80% window area and 20% brick wall area in both lower nodes and upper nodes results in heating and cooling demand energy reduction without significantly compromising the solar energy absorption. This scenario leads to increasing system performance from 36% to 39%. It is also concluded that doubling the tank capacity improves system performance from 36% to 43% and changing the heat exchanger effectiveness has minor impacts on the 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".