Augmented engineering digital tools for cluster-based CCUS design (AECCS)

Augmented engineering digital tools for cluster-based CCUS design (AECCS) - (£100K project funded by IDRIC Flexible funding scheme) aims to embed engineering digitalization tools for a cost-effective process design of cluster-based Carbon Capture, Utilization and Storage (CCUS) technologies. To deliver the UK Net Zero targets on time, at pace and scale, we need to transition from a conventional knowledge-based process design to a data-driven design which embraces all the possibilities that data science and digitalization have to offer. This approach will accelerate the successful decarbonisation of the UK Industrial Clusters as it will help laying the foundations for cost-effective process design and control of CCUS projects. The ultimate objective of the project is to provide more accurate investment costs estimations as well as risk management strategies for safe and continuous solvent-based carbon capture operation. Recent advances in Machine Learning (ML) for data analysis offer engineers unique opportunities to use large amount of data generated in well-established chemical engineering industrial processes. We will develop an Augmented Engineering Review (AER) tool based on plants design data with accurate cost-models for design recommendation, hazard and operability assessments of carbon capture plants. This tool will extend our value-chain platform, developed in this project and PrISMa project. Heriot-Watt University is partnering with Solverlo Ltd. to further extend its proprietary AER tool for the design of amine-based carbon capture processes.