Superstructure model for the simultaneous design and optimization of the PVSA process cycle
Bibliographic record
Abstract
<title>Abstract</title> The performance of any adsorptive separation process depends on two factors: the adsorbent media and the PVSA process cycle. The pool of adsorbents for any separation has grown exponentially over the past decade with the advent of metal-organic chemistry. There are potentially multiple PVSA process cycle pathways that can be chosen for the gas separation. To achieve the full potential of a given adsorbent, the operating conditions of the process cycle need to be optimized; this is computationally challenging. Traditionally, the performance of a small set of user-defined PVSA process cycles is chosen for optimization. In this work, we present a superstructure model to simultaneously design and optimize the PVSA process cycle. To highlight the potential of such an approach, we present different case studies related to separating CO2 from a mixture of CO2 and N2 as it is a well-studied and currently relevant separation system. The superstructure model presented in the work covers over two dozen possible PVSA cycle configurations. The framework is also shown to be scalable for adsorbent evaluation by using novel optimization strategies to effectively search the large input search region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".