title	DCG Slag Viscosity Controller
emoji	🏭
colorFrom	red
colorTo	indigo
sdk	gradio
app_file	app.py
pinned	true
short_description	Real-time ML Viscosity & RPM Control for BF Slag DCG

🏭 Real-Time Slag Viscosity Prediction for Dry Centrifugal Granulation (DCG)

Mineral & Metallurgical Engineering · IIT (ISM) Dhanbad

A machine learning system that predicts blast furnace slag viscosity in real time and recommends disc RPM adjustments for Dry Centrifugal Granulation heat recovery. Built with 4 ML models, Bayesian hyperparameter tuning, SHAP explainability, PCA anomaly detection, and a Qwen2.5-7B LLM expert report panel.

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Hackathon Submission Links

• Video Demo: https://youtu.be/s66GEb0iviw
• LinkedIn Post: https://www.linkedin.com/posts/saurabh-gupta0962_dcg-slag-viscosity-controller-a-hugging-share-7472381303036805120-M-gt
• Hugging Face Space: https://huggingface.co/spaces/saurabh0962/dcg-slag-viscosity-controller

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Why This Problem Matters

In Dry Centrifugal Granulation (DCG), molten blast furnace slag at 1450–1550 °C is poured onto a spinning disc (1000–3000 RPM). The disc breaks the slag into fine droplets for waste-heat recovery — but only if the slag viscosity is in a narrow window:

Viscosity	What Happens	RPM Action
< 0.055 Pa·s	Slag too fluid → large irregular blobs	🔵 Reduce RPM
0.055 – 0.080 Pa·s	✅ Fine spherical granules — optimal heat transfer	🟢 Maintain RPM
> 0.080 Pa·s	Slag too viscous → fibres form, system clogs	🔴 Increase RPM

Viscosity changes every tap depending on temperature and chemistry. No commercial real-time control system exists globally. This project solves that with a deployed ML demo.

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How It Works

Input Features

Feature	Range	Physical Meaning
Temperature	1400 – 1600 °C	Tap temperature
Basicity (CaO/SiO₂)	0.8 – 1.4	Higher = less viscous (CaO breaks Si–O network)
Al₂O₃	8 – 18 wt%	Network former — raises viscosity
MgO	4 – 12 wt%	Network modifier — slightly reduces viscosity
Coke Rate	450 – 550 kg/t	Proxy for furnace heat input
Tap Time	0 – 90 min	Elapsed time since tap — slag cools over time
Optical Basicity Λ	0.55 – 0.75	Auto-computed from mole fractions (Duffy & Ingram 1976)

Optical Basicity is derived from the slag composition:

Λ = X_CaO × 1.00 + X_SiO₂ × 0.48 + X_Al₂O₃ × 0.60 + X_MgO × 0.78

where X values are mole fractions. Higher Λ → more basic → lower viscosity.

Viscosity Model (Data Generation)

Synthetic training data (5,000 points) is generated using the Urbain model:

η = A · exp(B / T_K)

where A and B are empirical functions of the slag oxide composition, with ±5% Gaussian noise to simulate real plant measurement variation.

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ML Pipeline

Four Models Trained and Compared

Model	Architecture	Expected CV R²
Random Forest	300 trees, max_depth=15	~0.97
XGBoost	300 estimators, lr=0.05, depth=6	~0.97–0.98
CatBoost	500 iterations, lr=0.05, depth=6	~0.97–0.98
Neural Network	64→32→16, BatchNorm + Dropout	~0.95–0.96

Note: Exact numbers depend on the random seed and training run. Run the notebook to see your actual results.

Bayesian Hyperparameter Tuning (Optuna)

The best model by test R² is automatically selected and tuned with 50 Optuna trials (TPE sampler, minimising 5-fold CV RMSE). The tuned model is what drives all predictions in the Gradio demo.

SHAP Explainability

SHAP (SHapley Additive exPlanations) values are computed on the tuned model:

• Beeswarm plot — how each feature affects every prediction
• Bar chart — global average feature importance (embedded in the demo)
• Dependence plots — how the top 2 features relate to viscosity individually

The SHAP bar chart is saved as plot_shap_bar.png and displayed live in the app.

PCA Anomaly Detector

A 5-component PCA is fitted on the training data. For each new input, the Mean Squared Reconstruction Error (MSRE) is computed. If MSRE > training mean + 3σ, the input is flagged as out-of-distribution and a warning is shown — the prediction is still made but marked as an extrapolation.

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Gradio Demo — How to Use

1. Adjust the sliders on the left panel to your current slag conditions. Optical Basicity Λ is auto-computed from your inputs — the slider is display only.
2. Click "Predict" to get viscosity predictions from all four models instantly. The Tuned Best Model drives the RPM recommendation.
3. Check the Data Quality panel — the PCA anomaly detector confirms whether your inputs are within the model's training range.
4. Click "Expert LLM Report" to get a Qwen2.5-7B metallurgist explanation. The LLM prompt includes your exact SHAP attribution values for this prediction, not just generic feature importance. Requires HF_TOKEN secret to be set. Falls back to a rule-based explanation if the API is unavailable.

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Repository Structure

├── app.py                        ← Gradio demo (loads saved models, runs UI)
├── requirements.txt              ← Python dependencies for the HF Space
├── README.md                     ← This file
├── dcg_slag_viscosity_ml.py      ← Training script (run on Colab)
├── dcg_slag_viscosity_ml.ipynb   ← Jupyter Notebook version for easy reading
│
└── [Generated after running Colab — uploaded to HF Space separately]
    ├── model_rf_reg.joblib
    ├── model_xgb_reg.joblib
    ├── model_cat_reg.joblib
    ├── model_nn_reg.keras
    ├── model_tuned_best.joblib   ← or .keras if NN wins
    ├── scaler.joblib
    ├── label_encoder.joblib
    ├── model_metadata.json
    ├── pca_detector.joblib
    ├── anomaly_config.json
    └── plot_shap_bar.png

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Running the Training Notebook

1. Open dcg_slag_viscosity_ml_final.py in Google Colab as a notebook.
2. Change SAVE_DIR = "./" to SAVE_DIR = "/content/" (line ~85).
3. Set Runtime → GPU (optional, speeds up Neural Network training).
4. Run All — takes approximately 20–30 minutes.
5. Download all .joblib, .keras, .json files and plot_shap_bar.png from the /content/ directory.

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References

1. Xin et al. (2025) — Bayesian-Optimized CatBoost + SHAP for BF slag viscosity. Achieved R²=0.9897, RMSE=1.0619, hit ratio 95.1%. Journal of Non-Crystalline Solids. https://doi.org/10.1007/s42243-025-01608-z

2. Zhang et al. (2025) — RF, GBRT, and ANN for BF slag performance prediction. R² consistently above 0.97 on the CaO–SiO₂–Al₂O₃–MgO system. Ironmaking & Steelmaking. https://doi.org/10.1177/03019233251353314

3. Chen et al. (2026) — Optical basicity as domain-knowledge feature for ML viscosity prediction. Validation error reduced to 8–15%. International Journal of Minerals, Metallurgy and Materials. https://doi.org/10.1007/s12613-025-3189-4

4. Shankar et al. (2020) — PCA-KNN model for BF slag viscosity, 99% accuracy. JOM, 72, 3687–3696. https://doi.org/10.1007/s11837-020-04360-9

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Mineral & Metallurgical Engineering · IIT (ISM) Dhanbad
Presented as Innovation 1 in a proposed DCG heat-recovery control system