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By Jacek Jezowski, Jan Thullie

The nineteenth ecu Symposium on machine Aided technique Engineering includes papers offered on the nineteenth eu Symposium of laptop Aided procedure Engineering (ESCAPE 19) held in Cracow, Poland, June 14-17, 2009.The break out sequence serves as a discussion board for scientists and engineers from academia and to debate growth completed within the sector of CAPE. * CD-ROM that accompanies the publication comprises all learn papers and contributions * overseas in scope with visitor speeches and keynote talks from leaders in technological know-how and * offers papers masking the newest learn, key most sensible components and advancements in desktop aided procedure engineering (CAPE)

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Chem. , vol. 21, pp. 427-432 A. W. Westerberg, 1991, Process Engineering, Perspectives in Chemical Engineering, Research and Education, C. K. ), Advances in Chemical Engineering, vol. 16, Academic Press, Boston T. , Kravanja and S. Kravanja, 2008, MINLP optimization of a single-storey industrial steel building. Stroj. , vol. 54(10), pp. 707-724 19th European Symposium on Computer Aided Process Engineering – ESCAPE19 J. JeĪowski and J. V. All rights reserved. 37 Integration of chemical product development, process synthesis, and operation optimization Xiuxi Li, Yun Chen, and Yu Qian* School of Chemical Engineering, South China University of Technology, Guangzhou, 510640, China.

Novak-Pintariþ and Z. Kravanja, 2007, A multilevel MINLP methodology for the approximate stochastic synthesis of flexible chemical processes, Rev. chim. ), vol. 58(4), pp. 378391 M. Ropotar and Z. Kravanja, 2008, Translation of variables for different applications in process synthesis, AIChE Annual Meeting, Philadelphia, PA, November 16-21, 2008, Conference proceedings: 1908- 2008, New York: AIChE M. Ropotar, Z. -M. Reneaume and Z. Kravanja, 2009, MINLP synthesis of reactive distillation using a disjunctive, hybrid model, sent to PSE’09, Salvador-Bahia-Brazil, August 16–20, 2009.

For problems with a large number of uncertain parameters a strategy for the approximate stochastic optimization was developed (Novak-Pintariþ and Kravanja, 2004). Rather than integrating the objective value over ranges of uncertain parameters to obtain the expected objective value, the expected objective function is approximated at a special single point – the central basic point. In this way, the exhaustive calculation at numerous Gaussian quadrature points is avoided and the model is significantly reduced.

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