README.md

metadata

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.

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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.

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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)

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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

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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

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How to Run Locally

pip install -r requirements.txt
streamlit run app.py

Then open the local URL shown in your terminal (typically http://localhost:8501).

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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

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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

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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

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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

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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
Rourkela, India

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Why Rourkela Matters

Rourkela — India’s steel and metallurgy hub — provides the perfect ecosystem for AI pilot collaborations among academia, consulting, and industry.

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© TenderMatcher.Tech — AI & Digital Intelligence for Metallurgy
Read More

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.

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Credits

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