Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Carlos André Muñoz López"'
Publikováno v:
ACS Omega, Vol 9, Iss 24, Pp 25678-25693 (2024)
Externí odkaz:
https://doaj.org/article/f69985087847495ab32189b934c44f2f
Publikováno v:
Frontiers in Chemical Engineering, Vol 3 (2021)
The highly competitive nature of the chemical industry requires the optimisation of the design and exploitation of (bio-)chemical processes with respect to multiple, often conflicting objectives. Genetic algorithms are widely used in the context of m
Externí odkaz:
https://doaj.org/article/7b8b5704a2624f4aa730a3fb26e4f8a6
Publikováno v:
Frontiers in Chemical Engineering, Vol 2 (2020)
Processing data that originates from uneven, multi-phase batches is a challenge in data-driven modeling. Training predictive and monitoring models requires the data to be in the right shape to be informative. Only then can a model learn meaningful fe
Externí odkaz:
https://doaj.org/article/ebcc3f2b29c74da79c9b491fba4b2fcc
Autor:
Ourania Misiou, Monika Polanska, Sholeem Griffin, Simen Akkermans, Vasilis P. Valdramidis, Lydia Katsini, Satyajeet Bhonsale, Carlos André Muñoz López, Styliani Roufou, Jan Van Impe, Konstantinos Koutsoumanis
Publikováno v:
Trends in Food Science and Technology
Background: Food systems are both affecting and being affected by climate change. Anticipated effects of climate change on microbial food safety are both direct (e.g., on microbial prevalence) and indirect (e.g., increased risk of floods on water mic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c9f19d133dc1b6673e796bb6d9ba571
https://lirias.kuleuven.be/handle/123456789/679789
https://lirias.kuleuven.be/handle/123456789/679789
Publikováno v:
IFAC-PapersOnLine. 53:11722-11728
The large scale production of active pharmaceutical ingredients (APIs) is traditionally accomplished via batch processes. Nevertheless, their inherent complexity has limited the development and application of models for the processes as well as the u
Publikováno v:
Frontiers in Chemical Engineering, Vol 3 (2021)
Frontiers in Chemical Engineering
Frontiers in Chemical Engineering
The highly competitive nature of the chemical industry requires the optimisation of the design and exploitation of (bio-)chemical processes with respect to multiple, often conflicting objectives. Genetic algorithms are widely used in the context of m
Publikováno v:
Frontiers in Chemical Engineering, Vol 2 (2020)
Processing data that originates from uneven, multi-phase batches is a challenge in data-driven modeling. Training predictive and monitoring models requires the data to be in the right shape to be informative. Only then can a model learn meaningful fe
Publikováno v:
29th European Symposium on Computer Aided Process Engineering (ESCAPE29), 16-19 June, 2019, Eindhoven, The Netherlands
A sustainable chemical process operation often requires optimality with respect to multiple conflicting objectives as economic, societal and environmental aspects need to be addressed. Multi-objective optimisation aims at solving such problems. Asing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd46adf01bc97c84deef9eed883283cb
https://doi.org/10.1016/b978-0-12-818634-3.50103-x
https://doi.org/10.1016/b978-0-12-818634-3.50103-x
Autor:
Philippe Nimmegeers, Carlos André Muñoz López, Filip Logist, Lorenzo Cabianca, Dries Telen, Jan Van Impe
Publikováno v:
Computers and chemical engineering
© 2017 The (bio)chemical process industry is under an increasing pressure due to smaller margins and increasing societal and legislative demands for a sustainable future. In this context model-based optimization contributes to the solution because i
Publikováno v:
Processes
Volume 7
Issue 9
Processes, Vol 7, Iss 9, p 562 (2019)
Volume 7
Issue 9
Processes, Vol 7, Iss 9, p 562 (2019)
Spray drying is a key unit operation used to achieve particulate products of required properties. Despite its widespread use, the product and process design, as well as the process control remain highly empirical and depend on trial and error experim