Design of process synthesis experiments with a novel distributed algorithm using grids and knowledge-based optimization

Du, D; Cecelja, F; Kokossis, A

dc.contributor.author	Du, D	en
dc.contributor.author	Cecelja, F	en
dc.contributor.author	Kokossis, A	en
dc.date.accessioned	2014-03-01T02:51:59Z
dc.date.available	2014-03-01T02:51:59Z
dc.date.issued	2009	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35788
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-77951694356&partnerID=40&md5=f472d7a59d01c72873e345cfaa9737fc	en
dc.subject.other	Computing resource	en
dc.subject.other	Current optimization	en
dc.subject.other	Distributed algorithm	en
dc.subject.other	Distributed optimization	en
dc.subject.other	Grids technology	en
dc.subject.other	High-throughput	en
dc.subject.other	High-throughput synthesis	en
dc.subject.other	Industrial environments	en
dc.subject.other	Integrated applications	en
dc.subject.other	Local optima	en
dc.subject.other	Optimization system	en
dc.subject.other	Process synthesis	en
dc.subject.other	Reactor network	en
dc.subject.other	Sequential optimization	en
dc.subject.other	Stochastic optimization algorithm	en
dc.subject.other	Algorithms	en
dc.subject.other	Convergence of numerical methods	en
dc.subject.other	Design	en
dc.subject.other	Grid computing	en
dc.subject.other	Knowledge based systems	en
dc.subject.other	Synthesis (chemical)	en
dc.subject.other	Simulated annealing	en
dc.title	Design of process synthesis experiments with a novel distributed algorithm using grids and knowledge-based optimization	en
heal.type	conferenceItem	en
heal.publicationDate	2009	en
heal.abstract	Stochastic optimization algorithms have been developed for decades to offer ways to solve process synthesis problems. However complicated high-throughput applications, which require large amount of components and parameters and serial procedures in many steps, may encounter difficulties with current optimization algorithms, such as slow convergence or being trapped into local optimum. Distributed optimization algorithms, such as the simulated annealing (SA) Cascade, have been invented and introduced for solving large scale process synthesis problems through breaking down long sequential optimization process into smaller and parallel sections by applying grid computing. On the other hand, large amount of idle computing resources, which are widely distributed throughout the world, makes it possible to enable integrated applications and the design of distributed experiments in industrial environments. The paper aims at the design of an efficient high-throughput synthesis infrastructure for seeking higher performance of reactor network design applications with the assist of grids technologies and an attempt to build a self-supervised optimization system on the basis of knowledge-based optimization.	en
heal.journalName	AIChE Annual Meeting, Conference Proceedings	en