Titanium Dioxide Content Soft Sensor Development for Pilot‐Scale Ilmenite Electric Arc Furnace Using <scp>BiLSTM</scp> and <scp>BiGRU</scp> Recurrent Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Electric arc furnaces (EAFs) are central to various metallurgical processes for melting and upgrading ore. These furnaces use a significant amount of energy and consumables to operate, which suggests substantial potential for gains in operational efficiency. In this study, we propose a proof of concept for estimating a pilot‐scale ilmenite smelting electric arc furnace critical quality variable: the titanium dioxide content of the slag bath. This quality variable is estimated using a soft sensor based on a data‐driven machine learning (ML) model. The proposed ML model is trained using EAF sidewall temperatures, electric power, ore charge, and reducing agent charge values. To account for the nonlinear and dynamic nature of the semibatch process, models based on long short‐term memory (LSTM) and gated recurrent unit (GRU) neural network architectures are tested and evaluated. A systematic hyperparameter tuning approach allowed obtaining good estimation performance with an MSE of 0.23, an RMSE of 0.48, and an R2 of 0.78.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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