README.md

---
title: SteelAI Module2 EAF Intelligence Explorer
emoji: 🚀
colorFrom: red
colorTo: green
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)**

#  SteelAI — EAF Intelligence Explorer (MODEX)

An industrial-grade AI demonstrator for Electric Arc Furnace (EAF) analytics at
**Steel Authority of India Limited (MODEX)** — combining synthetic metallurgical datasets,
automated feature engineering, and AutoML ensembles with SHAP explainability.

###  Key Highlights
- Generates full synthetic EAF datasets (~3000 rows × 200+ features)
- Supports ensemble AutoML across RandomForest, XGBoost, LightGBM, CatBoost, etc.
- Performs Optuna-based hyperparameter tuning per family
- Uses meta-stacking (Ridge) and SHAP explainability
- Includes “Recommended Target Variables” and Business Impact framing
- Features annotated bibliography with direct research paper links

###  Logging & Reproducibility
- All generated CSVs, JSONs, and logs are stored under `./logs/`
- Each session in Hugging Face ephemeral environment appends new timestamps
- Users can download artifacts directly via the **Download Saved Files** tab

 *Ephemeral note*: Data and models are cleared when the Space rebuilds,  
but logs persist for the current runtime session.

---

###  Credits
Developed as part of SteelAI MODEX initiative for AI-driven metallurgy R&D.