Update README.md

README.md

@@ -12,9 +12,155 @@ short_description: Part of the TenderMatcher.Tech AI & Digital Intelligence
 license: other
 ---
 
-# Welcome to Streamlit!
+---
+title: "SteelAI Module #2 — Blast Furnace & EAF Data Intelligence Explorer"
+emoji: 🏭
+colorFrom: blue
+colorTo: gray
+sdk: streamlit
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# 🏭 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/<your-username>/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.
+
+---
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+© **TenderMatcher.Tech** — *AI & Digital Intelligence for Metallurgy*  
+Contact: **singhn9@gmail.com**
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).