Comparative study of artificial intelligence based multi‐modelling approach and optimization of photoreactor
Bibliographic record
Abstract
Abstract This study presents a generic methodology for modelling and optimizing a reactor with complex and poorly understood kinetics. Here, a photoreactor is considered which performs photodegradation of sodium oxalate salt in spent Bayer liquor. Multiple data‐driven modelling methods, including artificial neural networks (ANN), genetic programming (GP), hybrid genetic programming‐grey wolf optimization (GP‐GWO), and multi‐gene genetic programming (MGGP), were used to model the reactor's performance based on experimental data. The input parameters considered for modelling were initial solution pH, power of the lamp, total organic carbon, and catalyst loading. The models were evaluated based on their predictability, explainability, complexity, and adherence to the reactor's phenomenology. The MGGP model was found to be the most effective and was used to generate surface plots showing the parity between experimental results and model predictions. Additionally, the MGGP model was optimized using GWO to determine the process conditions that maximize the reaction rate.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".