active

ReactorTwin

Surrogate digital twin for industrial chemical reactors using neural ODEs and physics-informed neural networks.

GitHub

ReactorTwin creates fast, accurate surrogate models of chemical reactors that run 1000x faster than traditional CFD simulations while preserving physical constraints.

Architecture

Process Data (T, P, C, F)
    → Feature Engineering (residence time, Da, Pe)
    → Neural ODE Encoder (physics-constrained)
    → Latent State z(t)
    → Decoder: concentration, temperature, conversion profiles

Key Features

• Neural ODE reactor models — Learn reactor dynamics from process data
• Physics-informed training — Enforce mass balance, energy balance, thermodynamic constraints
• Real-time dashboard — Live visualization of reactor state with D3.js
• What-if analysis — Predict outcomes of operating condition changes
• Anomaly detection — Flag deviations from expected behavior
• Multi-reactor support — CSTR, PFR, batch, semi-batch

Tech Stack

• PyTorch + torchdiffeq for neural ODEs
• Cantera for thermodynamics
• Next.js + D3.js for the monitoring dashboard
• FastAPI + Docker for model serving