Towards predictive models for organic solvent nanofiltration

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InCollection{GOEBEL:2018:ESCAPE,

• author = "Rebecca Goebel and Tobias Glaser and Ilka Niederkleine and Mirko Skiborowski",
• title = "Towards predictive models for organic solvent nanofiltration",
• booktitle = "28th European Symposium on Computer Aided Process Engineering",
• editor = "Anton Friedl and Jiri J. Klemes and Stefan Radl and Petar S. Varbanov and Thomas Wallek",
• series = "Computer Aided Chemical Engineering",
• publisher = "Elsevier",
• volume = "43",
• pages = "115--120",
• year = "2018",
• keywords = "genetic algorithms, genetic programming, organic solvent nanofiltration, model identification, data-driven approach, prediction",
• ISSN = "1570-7946",
• DOI = "doi:10.1016/B978-0-444-64235-6.50022-X",
• URL = "http://www.sciencedirect.com/science/article/pii/B978044464235650022X",
• abstract = "Organic solvent nanofiltration (OSN) is a promising technology for an energy-efficient separation of organic mixtures. However, due to the lack of suitable models that allow for a quantitative prediction of the separation performance in different chemical systems OSN is rarely considered during conceptual process design. The feasibility of OSN is usually determined by means of an experimental screening of different membranes. Further experiments are conducted for a selected membrane in order to determine membrane specific parameters for a model-based description of the separation performance for a specific mixture. Obviously, this classical approach is experimentally demanding. The effort in identifying a suitable membrane in the first step could be significantly reduced if a theoretical evaluation of the separation performance was possible. The current article proposes an automatic method for the determination of a suitable predictive model for a given membrane, taking into account a limited set of experimental data. Specially, the rejection of different solutes in a specific solvent is modeled based on a set of physical and chemical descriptors. The proposed approach is based on a combination of genetic programming and global deterministic optimization, allowing for the identification of innovative models, including nonlinear parameter regression. The predictive capability of the generated models is validated on a separate data set. The identified models were able to predict the rejection of different components in the considered case studies with a deviation from the experimental values below 5percent",
• keywords = "genetic algorithms, genetic programming, organic solvent nanofiltration, model identification, data-driven approach, prediction",

}

Genetic Programming entries for Rebecca Goebel Tobias Glaser Ilka Niederkleine Mirko Skiborowski

Citations