Robust design of catalysts using stochastic nonlinear optimization
Bibliographic record
Abstract
Computational methods for designing an optimal catalyst have recently been gaining more popularity in the fields of catalysis and reaction engineering of energy systems. However, in general, the problem in these approaches is that uncertainties present in process models should be handled correctly to achieve a robust design. To find the optimal design under these uncertainties, a stochastic optimization method can be employed. In this work, the optimal properties of a catalyst for ammonia decomposition to produce hydrogen are investigated, and uncertainties associated with the reactions and their parameters are modeled as exogenous uncertain variables which follow known probability distributions. The goal of this work is to find the optimal binding energies of the catalyst that maximize conversion of ammonia in a microreactor. Our stochastic optimization problem is nonlinear, and involves the expectation operator as well as integration in the objective function. To tackle this complex system, the expectation of conversion based on a sample average approximation (SAA) method is evaluated. However, the exponential increase in the number of samples to be considered with the number of uncertain parameters lead to severe computational problems when using all possible combinations of the uncertain parameters. To solve this, linearity analysis, together with partial least squares, is implemented to reduce the number of uncertain parameters. In the optimization step, a particle swarm optimization (PSO) is employed. The results indicate that the stochastic optimum shows higher conversion and different optimal binding energies than the deterministic optimum, and is a more robust solution.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".