Bibliographic record
Abstract
The steel industry has faced environmental and economic challenges in the past few decades. As a result, cost-effective and robust process control models are in great need to maximize operational efficiency while ensuring product quality. This thesis proposes a hybrid method to model the oxygen steelmaking and continuous casting processes, which combines metallurgical knowledge and data-driven techniques such as machine learning.In the first part of the thesis, a process control model was established for the Basic Oxygen Furnace endpoint carbon, phosphorus, and temperature based on production data collected from Tata Steel India. To begin with, the importance of individual operating parameters was examined with respect to endpoint values by using Pearson’s correlation analysis, mutual information, stepwise regression, random forest, and principal component analysis. Three subsets of optimal features were selected for each endpoint, and an equal number of artificial neural networks were established, tuned, and validated. Secondly, theoretical models based on mass and energy balance were formulated based on operating parameters. Finally, the proposed hybrid model was established via exchanging inputs and outputs among theoretical and neural network models. The hybrid model results suggest that the prediction accuracies are improved with the application of machine learning algorithms. In addition, incorporating theoretical frameworks benefits the hybrid model with enhanced generalization. With the established hybrid model, a graphical user interface software was developed for operators to implement in production. In the second part of the thesis, a time-series deep learning model was established to monitor the nozzle clogging phenomenon in continuous casting based on production data of four steel grades collected from Stelco, Lake Erie Works. Firstly, a clogging index was formulated based on process parameters to indicate the severity of clogging buildup within the nozzle quantitatively. With this index, long short-term memory networks were developed to predict future clogging index and monitor the casting process. The models can predict clogging events and erosion incidents during the continuous casting for all steel grades. With the implementation of this model in production, operators can take corrective actions to reduce the frequency of clogging and improve process efficiency.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".