The service tests, benchmarks, and validates hybrid AI models and physics informed neural networks (PINNs) combining mechanistic biokinetic equations with real time empirical sensor data for closed loop bioreactor process control. Conducted in the Biosolutions Lab, we evaluate digital twin models to optimize dissolved oxygen aeration, substrate feeding rates, and foam suppression while detecting contamination anomalies early. The purpose is to help biomanufacturing enterprises and fermentation SMEs de risk process scale up, reduce batch failure rates by up to 35 percent, and cut operational energy and raw material costs.
Validated models and technologies with documented results that can be used to reduce risk, improve scalability, and support further implementation in existing or future bioproduction processes.
The service is delivered as a test and validation process including analysis of process data, development or application of hybrid models, testing of real-time feedback and control functions, and documentation of model performance, process improvements, and deviation detection in the Biosolutions Lab.
Biosolutions