Marrying Materials and Processes: A Superstructure Inspired Optimization Approach For Pressure Swing Adsorption Based Carbon Dioxide Capture Processes
Bibliographic record
Abstract
The performance of an adsorption-based separation process is dictated by the choices of the solid sorbent and the process configuration. Often screening of materials and process configuration is performed using digital twins that mimic a real adsorption process. In typical studies, either several materials are screened for a specific process configuration to find the best candidate or the performance of several process configurations is evaluated for a specific material. However, it has long been suggested that to truly maximize the potential of a given material, it should be "married" to processes. In this work, we address the "marriage" of materials and processes through three dedicated goals. First, to develop a modeling framework for an all-encompassing pressure swing adsorption cycle composed of several process configurations. Second, to develop an optimization framework, drawing inspiration from superstructures, to select the optimal process configuration from the all-encompassing cycle to reach a given process target. Third, to highlight the importance and relevance of such an approach that enables each material to truly maximize its potential, by varying both the process configuration and the corresponding operating conditions. To address these goals, we have developed a computational framework composed of a process model and a process optimizer. Subsequently, using this computational framework, we have evaluated the performance of several real and hypothetical materials. Our computational studies led to two key outcomes, namely, (1) to employ an integrated material-process optimization approach to maximize the true potential of any material when screening for a given application and when evaluating the performance under different feed conditions; and (2) not to generalize the observations regarding the best process configuration from one material to every other material.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".