IPIS — Integrated Process Intelligence System

A hybrid digital-twin-backed framework integrating soft sensors, predictive maintenance, and real-time optimization for chemical process manufacturing.

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What this is

IPIS is an open-source process intelligence framework that integrates three modules into a closed-loop decision system:

1. Soft Sensor — real-time prediction of hard-to-measure quality variables
2. Predictive Maintenance — anomaly detection and remaining useful life estimation
3. Real-Time Optimization (RTO) — constrained setpoint recommendations

All three modules sit on top of a digital twin layer (first-principles physics models) that provides baselines, constraints, and surrogate training data.

Why it exists

Three documented gaps in industrial AI for process manufacturing:

• Cross-process generalization — published models don't transfer between plants with different topologies
• Grade-transition robustness — models degrade during operating regime shifts
• Proof-of-concept to production gap — 87% of industrial AI projects fail at deployment

IPIS addresses all three with one architecture, validated across heterogeneous benchmark datasets.

Architecture (high level)

        DIGITAL TWIN LAYER (DWSIM + GEKKO + CoolProp)
                            │
        ┌───────────────────┼───────────────────┐
        ↓                   ↓                   ↓
   Soft Sensor    Predictive Maintenance      RTO
   (Module 1)        (Module 2)         (Module 3)
        │                   │                   │
        └───────────────────┴───────────────────┘
                            ↓
                OPERATIONAL STATE BUS
                (MQTT + InfluxDB)
                            ↓
              OPERATOR DASHBOARD + API
              (Streamlit + FastAPI)

Project status

Module	Status	Target
Module 1 — Soft Sensor	🚧 In progress (Phase 1A)	16–20 weeks
Module 2 — Predictive Maintenance	⏳ Planned	After Module 1
Module 3 — RTO	⏳ Planned	After Module 2
Integration (full IPIS)	⏳ Planned	After Module 3

Quick start

# Clone
git clone https://github.com/beebzy-droid/IPIS.git
cd IPIS

# Create environment (Python 3.11+)
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate

# Install in editable mode with dev dependencies
pip install -e ".[dev]"

# Verify installation
pytest tests/unit -v

# Download datasets
python scripts/download_datasets.py --all

Documentation

• System architecture
• Architecture Decision Records (ADRs)
• Module 1 specification

Tech stack

• ML: PyTorch, scikit-learn, XGBoost, River (online learning), MAPIE (conformal)
• Physics: DWSIM, CoolProp, GEKKO
• Infrastructure: OPC-UA (asyncua), MQTT (Mosquitto), InfluxDB, FastAPI, Streamlit
• MLOps: MLflow, Hydra, Docker, GitHub Actions, pytest

Citation

If you use this work in research, please cite:

@software{busico_ipis_2026,
  author = {Busico, Bien},
  title = {IPIS: Integrated Process Intelligence System},
  year = {2026},
  url = {https://github.com/beebzy-droid/IPIS}
}

License

MIT — see LICENSE.

Author

Bien Busico — Process Engineer | Chemical Engineering × AI/ML × Industry 4.0