Ai-driven digital twin for multiphase hydrodynamics & reactor scale-up (RPT-AI)

RPT-AI is an AI-enabled digital twin platform designed to measure, model, and optimize multiphase hydrodynamics in complex chemical reactors. It combines advanced tracer diagnostics, physical replication, and hybrid physics–AI modeling to create a validated virtual replica of real operating systems.

Why it matters Scale-up failures are often driven by poor understanding of mixing, residence time distribution (RTD), phase interaction, and mass/heat transfer. Traditional scale-up relies heavily on empirical correlations and conservative safety margins, increasing CAPEX, timelines, and technical risk. This is particularly critical in emerging fields such as plastic recycling (including pyrolysis), catalytic systems, and bioprocessing. RPT-AI reduces uncertainty, identifies performance bottlenecks, and accelerates commercialization.

How it works

1. Customers provide the reactor CAD geometry along with operating conditions (flow rates, temperature, pressure) and fluid/phase properties.
2. We reproduce the geometry, including internals, using rapid fabrication/3D printing to create an experimental model for controlled hydrodynamic testing.
3. Radioactive tracers are injected under representative conditions to directly measure RTD, mixing intensity, circulation patterns, phase distribution, bypassing, and dead zones.
4. Each experiment runs approximately 24 hours to capture steady-state and transient behavior. We can run one experiment per day and adjust geometry or operating parameters between runs, enabling rapid iteration and live optimization.
5. Processed data, hydrodynamic metrics, and actionable insights are delivered to the customer. These results feed a hybrid digital twin combining physics-based models and AI calibration to simulate scale-up scenarios and design alternatives.

Next steps Seeking industrial and research partners for pilot validation, reactor optimization, and scale-up support in recycling, chemical processing, and advanced materials.