| | --- |
| | 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. |
