Digital Capabilities for Chemical Processes

CPI can offer a range of digital capabilitie relevant to bioprocessing systems. Examples include - Computational Fluid Dynamics modelling for process design, process analytical testing, sensors, process models, and building hybrid models from collected data and bioprocess kinetics . We can also offer economic viability, techno-economic, and life-cycle analyses at varied levels of complexity depending upon the needs of the process and project.

Priority areas where CPI are interested in offering and building capability are:

•Data Acquisition, Connectivity and Sensing (covering activities from data capture through to pre-processing for analysis).

•Data Analytics, Modelling and Visualisation (covering most activities related to data processing, modelling and presentation).

•Automation and Scale-up (covering activities related to digital technologies that enable rapid and efficient progression of proof of concepts).

•AI (covering emerging themes outside the classic data science/ML remit, such as GenAI).

•Cross-cutting Capabilities (covering activities that span across individual themes and/or can be regarded foundational/enabling technologies).

•Host strain Development: AI and Machine Learning for Strain Design (including Predictive Modelling, Genomic Data Integration, Synthetic Biology Tools, Metabolic Flux Analysis (MFA) and High-throughput Screening (HTS)), Digital Twins for Strain Behaviour Prediction and Data Management Platforms.

•Upstream/Fermentation: Advanced Process Monitoring and Control (including Soft Sensors, Sensor Integration and Digital Twins), Predictive Modelling and Optimization and Resource Optimization & Sustainability, building predictive models from fermentation data at different scales using federated learning approaches.

•Downstream processing: Predictive Models for Process Efficiency (Use AI/ML models to optimize parameters for e.g. filtration, chromatography, centrifugation or drying; resource usage monitoring; and waste reduction).

•AI-driven Tech Transfer Models to predict performance variations when transferring processes between different scales or facilities and standardize process conditions across equipment and sites.

•Process Engineering Digital Twins for Scale-up for the entire bioprocess (host strain → upstream → downstream) to simulate scale-up from lab to pilot to production.