PACT allows researchers to test CCS technology physically, but further testing is required to ensure the same technology will be effective on an industrial scale. This is made possible by developing process models and computational fluid dynamics (CFD) models that allows researchers to upscale their findings, test various scenarios, and optimize process conditions so they can be confident that the technology is robust enough for large scale industrial application.
Algorithms are also being developed to simplify and speed up the computational models used in CCS research. This is done through creating metamodels, which once complete, will be used to analyse how various carbon capture technologies can impact the UK energy system as a whole, and by policy makers as a tool to inform their strategic decisions about how CCS works at a system level.
The complexity of process models accurately simulating Post-Combustion Capture (PCC) systems prohibits their direct inclusion into a system wide assessment. Here, we will produce robust, accurate reduced order models (ROMs) for PCC technologies using solvents and solid adsorbents to bridge this gap and represent the dynamic operation and performance of the capture technologies under operating conditions within the larger energy system model. ROMs will be developed in three stages: i) Consultation on the required quantities of interest (QoI) required as outputs, e.g. capture level/rate, specific energy consumption, CAPEX, OPEX and design parameters whichcan be ignored; ii) A Global Sensitivity Analysis to quantify the parameter effects on QoIs and rank their importance and decide which parameters can be removed. These methods have the benefit of being dimensionally nested, meaning that the remaining parameter sample sets can be re-used in the fitting of the ROMs even once variables are removed; iii) The ROMs will be polynomial chaos expansions or Kriging/Gaussian processes that can easily be entered into other codes. They will be developed initially using the sample set at the previous step and then improved by adaptively enriching the sample set to include information where error exists in the parameter space, either due to sparsity of sample points or rapid changes in the underlying model. The University of Sheffield will benefit from detailed dynamic models for PCC based on chemical absorption using the benchmark solvent MEA that have been developed in gPROMS, validated, scaled up and published. These detailed models will be reused for this project, with potential resimulation where necessary. The ROMs will feed forward into Systems WP CAB1.