@book{
 title = {Robust Design of Chemical Processes Based on a One-Shot Sparse Polynomial Chaos Expansion Concept},
 type = {book},
 year = {2017},
 source = {Computer Aided Chemical Engineering},
 keywords = {chemical processes,least angle regression,optimization,polynomial chaos expansion,robust design,uncertainty},
 pages = {613-618},
 volume = {40},
 publisher = {Elsevier},
 city = {Amsterdam},
 id = {76e31659-3bd9-3bfb-ab53-c845faf9b044},
 created = {2021-10-26T13:36:48.934Z},
 file_attached = {false},
 profile_id = {46206c9e-d69a-378c-9478-6dd168f65080},
 group_id = {cc302413-c146-306b-8008-abbf67f3b420},
 last_modified = {2021-10-26T13:36:48.934Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Xie2017b},
 private_publication = {false},
 abstract = {The application of robust model-based design concepts for complex chemical processes is limited due to the repeated cpu-intensive uncertainty quantification step for any new tested process design configuration. Therefore, an efficient One-Shot Sparse Polynomial Chaos Expansion (OS2-PCE) based process design framework is introduced in this work. The key idea is to define the process design variables as uncertain quantities as well and, in consequence, they become an integral part of the robust optimization routine. Moreover, by utilizing the sparsity feature of the PCE approach, the implementation of a least angle regression (LAR) concept leads to a significant reduction in computational costs. The overall performance of the novel OS2-PCE approach is illustrated by a robust process design study of a jacketed tubular reactor. In comparison to state-of-the-art concepts, the proposed framework shows promising results in terms of efficiency and robustness.},
 bibtype = {book},
 author = {Xie, Xiangzhong and Schenkendorf, René and Krewer, Ulrike},
 doi = {10.1016/B978-0-444-63965-3.50104-5}
}

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