Improved Reverse Electro-Dialysis Using Membranes With Integrated Spacers
Bibliographic record
Abstract
Reverse Electro-Dialysis, RED, utilises the energy of mixing between two solutions of different salinity by allowing ionic current to pass through the membranes and the two solutions such that cations are transport to the cathode and anions to the anode. [1–4.] The ionic current is converted to electronic current by red-ox reactions at the cathode and the anode. The membranes applied in this process are ionic selective, traditionally of uniform thickness and separated by a non-conductive spacer [5, 6]. Traditionally, non-conductive spacers have been deployed as eddy promoters and membrane spacers in salinity difference power extraction systems, such as Pressure Retarded Osmosis (PRO) and Reverse Electro-Dialysis (RED). For RED, traditional spacers inhibit parts of the ionic current paths in the fluid compartments and magnify the pressure drop imposed by the fluid flow between the membranes. [6] In a strive to lower the pressure drop in the fluid flow compartment and to increase the conductive region between the membranes, it is suggested to manufacture membranes with new shapes and profiles. [6] By modeling transport of mass and momentum in different geometries, spacing, mixing and active membrane area can be optimised with respect to increasing the power extraction. Such work has previously been done for traditional, i.e. non-electrochemical, flow in spacer separated membrane systems. A classical, approach has been to study submerged and non-submerged non-conductive spacer rods in fluid flow between two parallel plates (membranes) for Reynolds numbers (Re) from 50 and upwards. [7–17] This work discusses how spacers united with the reactant surface (membrane) will affect the mixing and the pressure drops of RED systems with Re numbers between 1 and 100, the expected operational Re number range for RED [6, 18, 19]. This is essential for the power production of RED. For a process converting renewable energy present in nature, such as RED, optimising these parameters is detrimental for the exergy yield. In going from a laboratory scale with a 10 × 10 cm2 cross sectional membrane to a large scale of 100 × 100 cm2, the Reynolds number (Re) increases from 10 to 100 simply because the volume flow is proportional to the flow length. Since it is within this range that eddies starts to get promoted by spacers, different mixing properties is expected exist when comparing laboratory and industrial scaled RED systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".