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Integration of ontology and knowledge-based optimization in process synthesis applications

Cecelja, F, Kokossis, A and Du, D (2011) Integration of ontology and knowledge-based optimization in process synthesis applications In: 21st European Symposium on Computer Aided Process Engineering (ESCAPE-21), 2010-05-29 - 2011-06-01, Chalkidiki, GREECE.

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Previous research has shown that knowledge-based optimization models in process synthesis applications are more robust in both providing final outputs and improving computational performance. This expands this approach by implementing a general knowledge models which in turn enables interpretation of solutions so that non-experts understand detailed procedures of optimization. To this end, an automatic ontology based optimization system that links rule-based optimization model and ontology has been introduced for the purpose to both improve optimization performance and to present new extracted knowledge at optimization run-time. A benchmark reactor network design synthesis case is studied for comparison of performance.The concomitant results show that not only can ontology-based optimization system improve robustness of solutions and computational performance, but also it enables a more accurate understanding of the process synthesis procedures and presents extracted knowledge in a decent format.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions : Surrey research (other units)
Authors :
Kokossis, A
Du, D
Editors :
Pistikopoulos, E. N.
Georgiadis, M. C.
Kokossis, A. C.
Date : 1 January 2011
Copyright Disclaimer : Copyright © 2011 Elsevier B.V. All rights reserved.
Uncontrolled Keywords : Science & Technology, Technology, Computer Science, Interdisciplinary Applications, Engineering, Chemical, Engineering, Industrial, Computer Science, Engineering, stochastic optimization, knowledge models, ontology, process synthesis, high throughput, REACTOR NETWORKS
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 12:12
Last Modified : 26 Mar 2020 17:16

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