Design and analysis of a hybrid powered reverse osmosis water system for use in a remote location in Newfoundland
Bibliographic record
Abstract
Remote communities such as McCallum, Newfoundland and Labrador, face critical challenges in accessing clean water and reliable electricity. Persistent water shortages, combined with lead contamination from naturally occurring soil, have rendered conventional solutions ineffective. Additionally, the community lacks grid access and depends entirely on diesel generators, which are expensive, emission-intensive, and often unreliable. To address these issues, this thesis presents the design, simulation, and implementation of a hybrid-powered reverse osmosis (RO) water treatment system. The work is structured in three phases. First, an optimal hybrid energy system (HES) was developed using HOMER Pro software, combining photovoltaic panels, a wind turbine, battery storage, and a small DC diesel generator for backup. This system reduced net present cost by over 70% compared to diesel-only operation and achieved a 98.8% renewable energy fraction, significantly cutting greenhouse gas (GHG) emissions. Second, dynamic simulations in MATLAB/Simulink validated system stability and reliable power delivery to the RO unit under varying environmental conditions. Third, a low-cost and low-power SCADA system was implemented using LoRa-enabled ESP32 modules for long-range communication and a local MQTT broker with web-based FUXA for visualization and monitoring. This architecture supports real-time monitoring and two-way control without relying on internet or cellular connectivity. The complete system was tested in the lab under various operational scenarios, confirming its ability to deliver clean, reliable, and low-emission power. This thesis offers a scalable and replicable model for resilient energy infrastructure in off-grid, resource-constrained communities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".