Kailong Liu, Li, K orcid.org/0000-0001-6657-0522, Zhang, J et al. (1 more author) (2017) Modeling of Organic Rankine Cycle for waste heat recovery using RBF neural networks. In: Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI). 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 06-09 Dec 2016, Athens, Greece. IEEE ISBN 978-1-5090-4240-1
Abstract
The Organic Rankine Cycle (ORC) process is promised to significantly recycle the waste heat from medium and low temperature heat sources, achieve better performance to recover low grade waste heat than traditional waste heat recovery processes used in the industrial applications. An accurate ORC model is indispensable for the optimization and control of ORC systems. A new Radial Basis Function (RBF) modelling approach, which combines the node selection based on Fast Recursive algorithm (FRA) and non-linear parameters optimization using the PSO algorithm is proposed to model the ORC system. The experimental results verify that the resultant models can achieve high training accuracy and desirable generalization performance.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Waste heat; Heat recovery; Biological system modeling; Radial basis function networks; Cost function |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Nov 2019 16:35 |
Last Modified: | 05 Nov 2019 16:35 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ssci.2016.7849996 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:153022 |