Experimental and analytical study on the performance of novel design of efficient two‐stage electrostatic precipitator
Bibliographic record
Abstract
This study presents a novel design of high‐efficiency two‐stage electrostatic precipitator (ESP). For its design, different electrode configurations were tested and the corresponding onset and breakdown voltages were measured and compared. Based on findings, an optimal arrangement was then defined and a novel design of a two‐stage ESP prototype, using needle electrodes instead of long wires utilised in conventional ESPs, is realised. For comparison, a single‐stage ESP was also built. The influence of various parameters on the performances of both two‐stage and single‐stage ESPs was evaluated numerically using 2D modelling and compared with the experimental ones for the same dimensions of laboratory‐scale ESP. The numerical simulation was implemented using COMSOL Multiphysics package that uses finite element method (FEM) solver. The main investigated parameters are the electric potential, electric field distribution and collection efficiency under the loading conditions as a function of air flow velocity, magnitude and polarity of voltage, ESP geometry design (size, shape and number of discharge electrodes). It is shown that the collection efficiency of this novel ESP increases when decreasing air flow velocity; and its effectiveness is higher when using negative ionisation polarity. Also, the collection efficiency of this ESP could greatly improve the existing ESPs under the same applied physical conditions.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".