Voltage control for membrane capacitive de-ionization cell for higher energy efficiency in salt removal
Bibliographic record
Abstract
Membrane Capacitive DeIonization (MCDI) cells have proven to be advantageous in water desalination and ions removal. Therefore, the time has come to introduce an alternative water purification technique to reduce the global water shortage. MCDI is known to be environmentally friendly, energy efficient and economical. Besides its reduced energy footprint, recent applications underline the regenerated energy during the desorption phase, which makes the MCDI as a potential cleaner energy source. Thus, a large number of scientific publications addressing problems and enhancing the performance of an MCDI have been published. In this paper, we have developed a simple and inexpensive method to control the adsorption voltage of the cells. So, the ion adsorption/desorption mechanisms of the MCDI will be controlled by a variable charging voltage applied to the cell.The entire response of controlled MCDI integrated model was created and he simulated results were compared with the experimental ones in order to validate the results. Accordingly, the controller parameters were tuned using the genetic algorithm optimization technique, based on the integral time absolute error criterion. Furthermore, the experimental results reveal that the control of the cell had increased the salt retention by 50%, the quantity of removed salt by the energy unit was improved by 10%, and the cell energy ratio from 28% to 32%.
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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 | 0.001 | 0.000 |
| 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".