Abstract
The limitations of existing methods for reactor network synthesis, including the more robust stochastic optimisation based methods, to cope with complex reaction schemes involving highly non-linear kinetics and multiple reactions, requires a novel approach to the problem. This paper uses knowledge derived from fundamental kinetic information to compose design rules representing the dominant design trends that lead to high system performance. This is the basis of a customised optimisation algorithm that features rule-based move selection to guide optimisation towards the most promising spaces, achieving more effective knowledge-based decision making. Results show optimal solutions obtained for an illustrative example agree with published literature whilst achieving better convergence compared to standard stochastic optimisation-based methods.
| Original language | English |
|---|---|
| Pages (from-to) | 331-336 |
| Number of pages | 6 |
| Journal | Computer Aided Chemical Engineering |
| Volume | 18 |
| Issue number | C |
| DOIs | |
| Publication status | Published - 2004 |
| Externally published | Yes |
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Keywords
- data mining
- optimisation
- reactor network
- synthesis
ASJC Scopus subject areas
- Chemical Engineering(all)
- Computer Science Applications
Cite this
Knowledge-driven reactor network synthesis and optimisation. / Ashley, Victoria M.; Linke, Patrick.
In: Computer Aided Chemical Engineering, Vol. 18, No. C, 2004, p. 331-336.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Knowledge-driven reactor network synthesis and optimisation
AU - Ashley, Victoria M.
AU - Linke, Patrick
PY - 2004
Y1 - 2004
N2 - The limitations of existing methods for reactor network synthesis, including the more robust stochastic optimisation based methods, to cope with complex reaction schemes involving highly non-linear kinetics and multiple reactions, requires a novel approach to the problem. This paper uses knowledge derived from fundamental kinetic information to compose design rules representing the dominant design trends that lead to high system performance. This is the basis of a customised optimisation algorithm that features rule-based move selection to guide optimisation towards the most promising spaces, achieving more effective knowledge-based decision making. Results show optimal solutions obtained for an illustrative example agree with published literature whilst achieving better convergence compared to standard stochastic optimisation-based methods.
AB - The limitations of existing methods for reactor network synthesis, including the more robust stochastic optimisation based methods, to cope with complex reaction schemes involving highly non-linear kinetics and multiple reactions, requires a novel approach to the problem. This paper uses knowledge derived from fundamental kinetic information to compose design rules representing the dominant design trends that lead to high system performance. This is the basis of a customised optimisation algorithm that features rule-based move selection to guide optimisation towards the most promising spaces, achieving more effective knowledge-based decision making. Results show optimal solutions obtained for an illustrative example agree with published literature whilst achieving better convergence compared to standard stochastic optimisation-based methods.
KW - data mining
KW - optimisation
KW - reactor network
KW - synthesis
UR - http://www.scopus.com/inward/record.url?scp=77955623779&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77955623779&partnerID=8YFLogxK
U2 - 10.1016/S1570-7946(04)80121-4
DO - 10.1016/S1570-7946(04)80121-4
M3 - Article
AN - SCOPUS:77955623779
VL - 18
SP - 331
EP - 336
JO - Computer Aided Chemical Engineering
JF - Computer Aided Chemical Engineering
SN - 1570-7946
IS - C
ER -