Unified correlation for overall gas hold‐up in bubble column reactors for various gas–liquid systems using hybrid genetic algorithm‐support vector regression technique
Bibliographic record
Abstract
Abstract The objective of this study is to collect the data on overall gas hold‐up (∈G) for bubble column reactors handling various gas–liquid systems and further develop a unified data‐driven model for the estimation of the same. In this work, around 3300 experimental points for ∈G have been collected from 85 open sources spanning the years 1963–2008. The data‐driven model for overall gas hold‐up has been established using hybrid Genetic Algorithm‐Support Vector Regression (GA‐SVR)‐based methodology. In the present study, GA has been used for nonlinear rescaling of the parameters. These exponentially scaled parameters are subsequently subjected for SVR training. The technique is an extension of conventional SVR technique, showing relatively enhanced results. The proposed hybrid model is based on various prominent design and operating parameters (15 in number) which includes superficial gas velocity, superficial liquid velocity, gas density, molecular weight of gas, sparger type, sparger hole diameter, number of sparger holes, liquid viscosity, liquid density, liquid surface tension, ionic strength of liquid, operating temperature, operating pressure, liquid height, and the column diameter. The estimations made by the SVR‐based unified model for ∈G shows an excellent agreement with actual values with estimation accuracy of 98.5% and % AARE of 9.32%. For ease in applicability and ready reference of the practicing engineers, the hybrid GA‐SVR‐based model in the form of software and the entire database for ∈G has been uploaded on the link http://www.esnips.com/web/UICT‐NCL .
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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 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".