Process modelling of a novel acid-base flow battery based on bipolar-membranes
Bibliographic record
Abstract
The storage of electrical energy is crucial for a deeper penetration of renewable energies with intermittent nature, e.g. solar and wind energy. The Acid/Base Flow Battery (AB-FB) is a novel, sustainable, environmental-friendly storage technology with high energy density of the electrolyte solutions. The process is based on reversible electrodialytic techniques with bipolar membranes, which convert the electrical energy in the chemical energy associated to pH gradients and vice versa. The charge phase is a bipolar membrane electrodialysis process, while the discharge phase is a bipolar membrane reverse electrodialysis process. The stack consists of several repetitive units, called triplets, made up of an anion-exchange membrane, a bipolar membrane, and a cation-exchange membrane, separated by spacers forming the channels where the acid, base and salt solutions flow. This work presents a sensitivity analysis performed by an experimentally validated AB-FB process model. The model is built by a multi-scale approach, integrating four different dimensional scales (channel, triplet, stack, hydraulic circuit) within a comprehensive simulation tool with distributed parameters. The model was validated against experimental results collected under different operating conditions, showing a good agreement. A wide sensitivity analysis was performed in order to explore the behavior and performance of the AB-FB in different scenarios. The model outcome illustrates how stack geometry, operating conditions and battery flow layouts (e.g. open-loop vs closed-loop operations) can affect the process performance. By adopting some measures to tackle the shunt currents flowing via manifolds and taking thermodynamic advantages from open-loop operations, the round trip efficiency reached values up to 70%.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".