Zobrazeno 1 - 6
of 6
pro vyhledávání: '"C C Diana López"'
Publikováno v:
Computers & Chemical Engineering. 94:104-116
The adaptive input design (also called online redesign of experiments) for parameter estimation is very effective for the compensation of uncertainties in nonlinear processes. Moreover, it enables substantial savings in experimental effort and greate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::175e240001c61fc558f3dccef05af001
https://doi.org/10.1016/j.compchemeng.2016.07.009
https://doi.org/10.1016/j.compchemeng.2016.07.009
Publikováno v:
Computers & Chemical Engineering. 77:24-42
Discrete ill-posed problems are often encountered in engineering applications. Still, their sound analysis is not yet common practice and difficulties arising in the determination of uncertain parameters are typically not assigned properly. This cont
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55b369a6babe51bd60fcc8f87aa1b743
https://doi.org/10.1016/j.compchemeng.2015.03.002
https://doi.org/10.1016/j.compchemeng.2015.03.002
Publikováno v:
Industrial & Engineering Chemistry Research. 52:12993-13005
This work presents the mathematical formulation of a nonlinear programming (NLP) model which optimizes simultaneously crude oil blending and operating conditions for a system of several crude oil distillation units (CDUs) at a Colombian refinery. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f07f991a1d9645d38df032a1c35b4553
https://doi.org/10.1021/ie4000344
https://doi.org/10.1021/ie4000344
Publikováno v:
Biotechnology Progress. 29:1064-1082
In this work, a methodology for the model-based identifiable parameter determination (MBIPD) is presented. This systematic approach is proposed to be used for structure and parameter identification of nonlinear models of biological reaction networks.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f83810792d26fbaca747474869aa7d39
https://doi.org/10.1002/btpr.1753
https://doi.org/10.1002/btpr.1753
Models are prone to errors, often due to uncertain parameters. For optimization under uncertainty, the larger the amount of uncertain parameters, the higher the computational effort and the possibility of obtaining unrealistic results. In this contri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70c9b34253c6bea3e2598b2fe448ccfc
https://depositonce.tu-berlin.de/handle/11303/12551
https://depositonce.tu-berlin.de/handle/11303/12551
Considering uncertainty is crucial for the decision making process in chemical engineering. However, when working in optimization under uncertainty a systematic selection of relevant uncertain parameters is required. In this contribution, an algorith
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e920d080e0e975876b23d62e8394acf3