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