Pressure swing adsorption (PSA) is a cyclic adsorption process for gas separation and purification, and can be used in a variety of industrial applications, for example, hydrogen purification and dehydration. PSA is, due to its low operational cost and its ability to efficiently separate CO2 from flue gas, a promising candidate for post-combustion carbon capture in power plants, which is an important link in the Carbon Capture and Storage technology chain. PSA offers many design possibilities, but to optimise the performance of a PSA system over a wide range of design choices, by experimental means, is typically too costly, in terms of time and resources required. To address this challenge, computer experiments are used to emulate the real system and to predict the performance. The system of PDAEs that describes the PSA process behaviour is however typically computationally expensive to simulate, especially as the cyclic steady state condition has to be met. Over the past decade, significant progress has been made in computational strategies for PSA design, but more efficient optimisation procedures are needed. One popular class of optimisation methods are the Evolutionary algorithms (EAs). EAs are however less efficient for computationally expensive models. The use of surrogate models in optimisation is an exciting research direction that allows the strengths of EAs to be used for expensive models. A surrogate based optimisation (SBO) procedure is here developed for the design of PSA systems. The procedure is applicable for constrained and multi-objective optimisation. This SBO procedure relies on Kriging, a popular surrogate model, and is used with EAs. The main application of this work is the design of PSA systems for CO2 capture. A 2- bed/6-step PSA system for CO2 separation is used as an example. The cycle configuration used is sufficiently complex to provide a challenging, multi-criteria example.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:626610 |
| Date | January 2014 |
| Creators | Beck, J. H. |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://discovery.ucl.ac.uk/1429983/ |
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