The service delivers experimental development, benchmarking, and validation of custom industrial machine learning models, time series forecasting, and computer vision for process monitoring and predictive maintenance. Operating in the Biosolutions Lab, we engineer deep learning algorithms for biological growth rate prediction, automated camera foam overflow detection, and multivariate sensor anomaly detection for early equipment failure warning. The purpose is to help biomanufacturing enterprises and process industry SMEs cut unplanned downtime by up to 30 percent, prevent batch contamination, and unlock real time AI decision support.
Documented insights into data patterns and process anomalies with reported model results and recommendations that can form the basis for future optimization, monitoring, or implementation of AI-based solutions.
The service is delivered as an experimental development and analysis process including clarification of data and use case, development and testing of relevant machine learning models in the Biosolutions Lab, and delivery of documented results and recommendations for further use or optimization.
Biosolutions, Biotechnological processes, Process monitoring, Predictive maintenance, Other emerging and enabling technologies