Simulating industrial bioreactor runs can now proceed 10,000 times faster through the newly introduced CLARA software toolbox. As detailed in the journal Chemical Engineering Science, an interdisciplinary research team at Graz University of Technology (TU Graz), led by Stefan Radl from the Institute of Process and Particle Technology, presented the framework to dismantle computational obstacles in large-scale process design.
Developing these massive setups requires extreme precision, as minute variations in temperature, pH, or dissolved oxygen dictate operational success or product failure. Automating the creation of reduced-order models removes the severe simulation bottlenecks that currently delay process scaling across industrial biotechnology. Up to now, industrial operators relied predominantly on computational fluid dynamics (CFD) for design and validation work, incurring massive computational delays that translate directly into heavy financial losses and prolonged time-to-market. In economic terms, traditional CFD workflows tie up valuable engineering resources for months, severely restricting the number of design iterations a company can afford during scale-up. Shifting to reduced-order models through automated machine learning eliminates this idle waiting time, cutting computational overhead from months to minutes and radically lowering the cost of compute infrastructure for industrial operators.
"Simulations that depict real processes over days or weeks might take months to calculate."
As Stefan Radl, the lead researcher at TU Graz, underscored, while traditional CFD modeling yields detailed flow dynamics, the time expenditure prevents iterative engineering cycles. As part of the university's DigiBioTech lead project, the researchers developed CLARA—short for CLustering AlgoRithm Austria—to replace slow numerical methods with unsupervised machine learning architectures.
Unsupervised Clustering for Flow Dynamics
The fundamental innovation lies in how CLARA compresses massive numerical datasets generated by physical fluid models. Millions of data points derived from shorter CFD simulations are processed through an automated, AI-supported clustering workflow. How does this mathematical reduction maintain the integrity of complex industrial multiphase flows?
The algorithm condenses those millions of points into a manageable structure of five to 50 interconnected zones, designated as compartments. What previously demanded weeks of manual tuning by specialized fluid dynamics engineers is now executed mathematically. The model preserves critical flow phenomena, retaining calculations for dead zones with inadequate mixing, high-shear areas, and interphase species transfers between gases and liquids. Calculations cover detailed reactions and mass transport efficiently even when solid, liquid, and gaseous streams mix simultaneously.
Solving Physical Mass Balance Inaccuracies
A central operational hurdle in simplified reactor modeling remains numerical drift: basic reduced models frequently break down under shifting biological kinetics, violating fundamental mass balances when growing organisms consume accelerating volumes of nutrients and gases. To prevent these distortions, the engineering team at TU Graz engineered specialized optimization algorithms that enforce constant mass conservation within the reduced-order models. Initial validations against analytical benchmarks and CFD datasets confirmed that physical accuracy remains intact despite the radical speedup.
"The potential applications of CLARA are diverse and range from reactors for CO2 capture to processes in the production of battery cells."
As Stefan Radl outlined, the resulting simulation speed enables real-time digital twins capable of controlling operations across biomass energy utilization, fermentation, and polymer synthesis. Deployments have moved beyond theoretical modeling: CLARA is already operating within a doctoral thesis at the COMET center BEST and at the university spinoff SimVantage. Graz University of Technology is releasing CLARA as an open-source tool within a public repository, incorporating current validated features while preparing a progressive release of subsequent platform developments.
What doesn’t add up
Where biotech operators once tied up engineering resources for months waiting on classic CFD simulations, the CLARA software package cuts those computing overheads down to minutes. The TU Graz team effectively automated weeks of manual calibration by letting unsupervised clustering map complex flows into 5 to 50 compartments. The resulting speedup trades raw physical fidelity for aggressive mathematical compression, relying on dedicated algorithms to stop numerical drift from breaking fundamental mass balances. Will a reduced model of up to 50 zones hold true for pH and dissolved oxygen levels once scaled from university simulations into commercial multiphase reactors?