Techno-Economic Trade-Off between Battery Storage and Ice Thermal Energy Storage for Application in Renewable Mine Cooling System
Bibliographic record
Abstract
This paper performs a techno-economic assessment in deploying solar photovoltaics to provide energy to a refrigeration machine for a remote underground mine. As shallow deposits are rapidly depleting, underground mines are growing deeper to reach resources situated at greater depths. This creates an immense challenge in air-conditioning as the heat emissions to mine ambient increases substantially as mines reach to deeper levels. A system-level design analysis is performed to couple PV with a refrigeration plant capable of generating 200 tonne of ice per day to help to mitigate this issue. Generated ice can directly be used in cooling deep underground mines via different types of direct heat exchangers. State-of-the-art technology is used in developing the model which aims to decrease the size and cost of a conventional refrigeration system run on a diesel generator. Costs associated with deploying a solar system are computed as per the recent market value. Energy savings, carbon emissions reduction, and net annual savings in employing the system are quantified and compared to a diesel-only scenario. In addition, two different energy storage strategies: an ice storage system and a battery storage system, are compared. A detailed economic analysis is performed over the life of the project to obtain the net cash flow diagram, payback period, and cumulative savings for both systems. Moreover, a sensitivity analysis is proposed to highlight the effect of solar intensity on solar system size and the area required for installment. The study suggests that the use of solar PV in mine refrigeration applications is technically feasible and economically viable depending on the sun-peak hours of the mine location. Additionally, the economics of deploying an ice storage system compared to the battery storage system has a better payback period and more cumulative savings.
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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 | 0.000 |
| 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".