Hybrid semi-parametric modeling in process systems engineering: Past, present and future

• Type: Article in International Scientific Journal
• Date: 2014-01-10

Authors

Moritz Von Stosch (Author) FEUP; Rui Oliveira (Author) FEUP; Joana Peres (Author) FEUP; Sebastião Feyo de Azevedo (Author) FEUP

Journal

Title: Computers and Chemical Engineering

Vol. 60 No. JAN 10 2014

Pages: 86-101

ISSN: 0098-1354

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 41 Citations

Scopus - 112 Citations

INSPEC

Scientific classification

FOS: Engineering and technology > Chemical engineering

Associated Projects

POCI/BIO/56571/2004 - Automação Avançada de Processos Biológicos Baseada em Sistemas Híbridos do Tipo Caixa Cinzenta

Other information

Authenticus ID: P-008-E6E

DOI: 10.1016/j.compchemeng.2013.08.008

Abstract (EN): Hybrid semi-parametric models consist of model structures that combine parametric and nonparametric submodels based on different knowledge sources. The development of a hybrid semi-parametric model can offer several advantages over traditional mechanistic or data-driven modeling, as reviewed in this paper. These advantages, such as broader knowledge base, transparency of the modeling approach and cost-effective model development, have been widely recognized, not only in academia but also in the industry. In this paper, the most common hybrid semi-parametric modeling and parameter identification techniques are revisited. Applications in the areas of (bio)chemical engineering for process monitoring, control, optimization, scale-up and model-reduction are reviewed. It is outlined that the application of hybrid semi-parametric techniques does not automatically lead into better results but that rational knowledge integration has potential to significantly improve model-based process operation and design.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16