The use of artificial neural network (ANN) in dry flue gas desulphurization modelling: Levenberg–Marquardt (LM) and Bayesian regularization (BR) algorithm comparison
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Bibliographic record
Abstract
Abstract This research project aims to investigate the efficacy of artificial neural networks (ANN) in mapping dry flue gas desulphurization (DFGD). Bayesian regularization (BR) and Levenberg–Marquardt (LM) training algorithms were used for DFGD modelling. The input layer feed data contained diatomite to Ca(OH) 2 ratio, hydration time, hydration temperature, sulphation temperature, and inlet gas concentration, while the output layer metadata were sorbent conversion and sulphation responses. The hyperbolic tangent ( tansig) , sigmoid ( logsig) , and linear ( purelin ) activation functions were compared to ascertain the best network learning model. The number of hidden layer cells also varied between 7 and 10, given the existence of multiple output feed data. BR and LM performance evaluation was based on coefficient of determination ( R 2 ), root mean square error (RMSE), and mean square error (MSE) mathematical analysis. BR was a superlative training tool compared to LM, with lower RMSE and MSE values. The goodness of fit data for both techniques was close to unity, clarifying that ANN using BR and LM tools can be used to predict DGFD outcome. The shrinking core model was used to analyze the desulphurization reaction and concluding the chemical reaction was the reaction controlling step.
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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 | 0.001 | 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 it