Near Field Resonant Capacitive Heating of Water
Bibliographic record
Abstract
Abstract Remediation of water from tailings is a critical and challenging issue in many industries, particularly in oil sand refineries and waste water treatment. Mining of the Canadian oil sands, also one of the largest known crude petroleum reserve in the world, is done to extract bitumen by the processes like hot water extraction, resulting in numerous amounts of tailings (large ponds). Techniques that can achieve contaminant separation over large surface areas/volumes, with low energy input, and at a low economic investment are of primary interest. Hence, such techniques can be applied for the manufacturing industries with the similar concerns. In this paper, a di-electric heating method is presented that combines electrical standing wave voltage amplification with porous interface (carbon foam) materials to intensify water evaporation. The system targets the di-electric loss tangent of the aqueous solution accompanied with porous interface by operating at a high voltage and frequency in the low megahertz (1–5 MHz). The non-uniform charge distributions across the interface material enables distributed heat localization at the air-water/material boundary, therefore avoiding bulk heating. The most important parameters determining efficiency of capacitive heating are determining the resonant frequency of material in contact with the heating end of helix, and properties of the interface material. Different porous configurations treated for the application were applied as interface layers and the experimental results demonstrate an 80% increase in evaporation rates compared to solar and natural heating. The combination of electrical heating with reduced heat losses results in accelerated vapor generation. Hence, results showcase heat localization at the interface, electric field at the heating boundary and energy requirements for the mentioned scenario. The proposed method offers a promising solution to localize heat over a large area by application of low-cost porous materials and high voltage/high frequency electrical resonators for use in water treatment, remediation, and go further with distillation applications for water reclamation.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".