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--- |
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title: SteelAI Module2 EAF Intelligence Explorer |
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emoji: 🚀 |
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colorFrom: red |
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colorTo: green |
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sdk: docker |
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app_port: 8501 |
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tags: |
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- streamlit |
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pinned: false |
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short_description: Part of the TenderMatcher.Tech AI & Digital Intelligence |
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license: other |
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persistent_storage: 1Gi |
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--- |
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# SteelAI Module #2 — Blast Furnace & EAF Data Intelligence Explorer |
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**Part of the TenderMatcher.Tech AI & Digital Intelligence for Metallurgy Suite** |
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Ready-to-deploy **Streamlit + SHAP application** for **energy and yield optimization** in |
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**Blast Furnace** and **Electric Arc Furnace (EAF)** operations. |
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--- |
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## Objective |
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Predict and optimize key furnace variables such as: |
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- `furnace_temp` |
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- `tap_temp` |
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- `offgas_co`, `offgas_co2`, `o2_probe_pct` |
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- `arc_power`, `energy_efficiency`, `yield_ratio` |
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This module simulates a **complete furnace data intelligence environment** with ensemble modeling, SHAP explainability, and physics-informed feature engineering. |
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--- |
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## Core Features |
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- Synthetic EAF dataset generator (3,000+ records) |
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- Derived physical proxies: |
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- `carbon_proxy`, `oxygen_utilization`, `slag_foaming_index` |
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- PCA and clustering for operating modes |
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- Ensemble regression (Linear, RF, GB, ExtraTrees) |
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- SHAP explainability for model transparency |
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- Business framing & annotated bibliography for metallurgy ML |
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- Fully local synthetic data generation (no external upload needed) |
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--- |
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## Use Case Alignment (SteelAI Framework) |
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| # | Use Case | Alignment | Description | |
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|---|-----------|------------|--------------| |
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| **2** | Blast Furnace / EAF Data Intelligence | **Primary (100%)** | Furnace temperature, gas chemistry, and power-density modeling for yield & energy optimization | |
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| 7 | AI-Driven Alloy Design Tool | Partial | Shares compositional features (`chemical_C`, `chemical_Mn`, etc.) | |
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| 8 | Predictive Maintenance Framework | Partial | Includes rolling, lag, and vibration signals for maintenance AI | |
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--- |
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## Example Targets |
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- Predict **`furnace_temp`** from operational data |
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- Analyze **feature importance** with SHAP plots |
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- Quantify business value: |
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- 5–8% yield improvement |
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- 3–5% energy cost reduction per ton |
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--- |
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## How to Run Locally |
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```bash |
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pip install -r requirements.txt |
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streamlit run app.py |
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``` |
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Then open the local URL shown in your terminal (typically http://localhost:8501). |
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--- |
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## Deploy on Hugging Face |
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1. Create a Space → choose **SDK: Streamlit** |
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2. Upload: |
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- `app.py` |
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- `requirements.txt` |
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- `README.md` |
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3. Hugging Face automatically installs dependencies and builds your Space. |
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Your app will be live at: |
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``` |
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https://huggingface.co/spaces/singhn9/SteelAI_Module2_EAF_Intelligence_Explorer |
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``` |
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--- |
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## Business Value Snapshot |
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| Dimension | Improvement | Impact | |
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|------------|-------------|---------| |
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| Productivity | +8–15% throughput | Higher process stability | |
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| Energy efficiency | 3–8% reduction | Lower cost per ton | |
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| Quality control | 10–15% better rejection precision | Fewer off-grade batches | |
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| R&D cycle | 25–35% faster property correlation | Shorter design-to-validation loop | |
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| Predictive reliability | 7–10% OEE gain | Reduced downtime | |
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--- |
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## Generative AI & Industrial Innovation |
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Beyond process modeling, **TenderMatcher.Tech** extends AI into Generative domains: |
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- **Knowledge-Graph Assistant** — links research papers, alloy data & insights |
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- **Chat-based Technical Advisor** — LLM-powered metallurgical Q&A |
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- **Generative Report Builder** — auto-creates lab summaries & dashboards |
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--- |
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## AI–ServiceNow Integration Mapping |
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| # | AI Module | ServiceNow Integration | Business Value | |
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|---|------------|------------------------|----------------| |
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| 1 | Steel Property Prediction | QMS, Predictive Intelligence | QA traceability & ISO/BIS compliance | |
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| 2 | Blast Furnace Intelligence | OT IntegrationHub, EHS | Closed-loop efficiency alerts | |
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| 3 | Microstructure Classifier | KM, AI Search | Metallography knowledge base | |
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| 4 | Surface Defect Detection | FSM, Predictive Intelligence | Real-time defect case auto-routing | |
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| 5 | Corrosion/Fatigue Prediction | ORM, Asset Mgmt | Predictive asset health | |
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| 6 | Energy Optimizer | Sustainability Mgmt | ESG-linked savings reporting | |
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| 7 | Alloy Design Tool | Innovation Mgmt, KM | R&D portfolio tracking | |
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| 8 | Predictive Maintenance | AIOps, FSM, CMDB | 10–12% downtime reduction | |
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--- |
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## About Naval Singh |
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**Naval Singh** — Digital Transformation Advisor |
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Specializing in AI, analytics, and industrial systems. |
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Focused on production-grade decision systems for metallurgy, mining & manufacturing. |
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**singhn9@gmail.com** |
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[LinkedIn](https://linkedin.com/in/navalsingh9) |
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**Rourkela, India** |
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--- |
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## Why Rourkela Matters |
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Rourkela — India’s steel and metallurgy hub — provides the perfect ecosystem for AI pilot collaborations among academia, consulting, and industry. |
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--- |
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© **TenderMatcher.Tech** — *AI & Digital Intelligence for Metallurgy* |
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**[Read More](https://tendermatcher.tech/ai-metallurgy)** |
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# SteelAI — EAF Intelligence Explorer (MODEX) |
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An industrial-grade AI demonstrator for Electric Arc Furnace (EAF) analytics at |
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**Steel Authority of India Limited (MODEX)** — combining synthetic metallurgical datasets, |
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automated feature engineering, and AutoML ensembles with SHAP explainability. |
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### Key Highlights |
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- Generates full synthetic EAF datasets (~3000 rows × 200+ features) |
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- Supports ensemble AutoML across RandomForest, XGBoost, LightGBM, CatBoost, etc. |
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- Performs Optuna-based hyperparameter tuning per family |
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- Uses meta-stacking (Ridge) and SHAP explainability |
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- Includes “Recommended Target Variables” and Business Impact framing |
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- Features annotated bibliography with direct research paper links |
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### Logging & Reproducibility |
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- All generated CSVs, JSONs, and logs are stored under `./logs/` |
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- Each session in Hugging Face ephemeral environment appends new timestamps |
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- Users can download artifacts directly via the **Download Saved Files** tab |
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*Ephemeral note*: Data and models are cleared when the Space rebuilds, |
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but logs persist for the current runtime session. |
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--- |
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### Credits |
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Developed as part of SteelAI MODEX initiative for AI-driven metallurgy R&D. |
