Integrated framework of nonlinear prediction and process monitoring for complex biological processes
SCIE
SCOPUS
- Title
- Integrated framework of nonlinear prediction and process monitoring for complex biological processes
- Authors
- Yoo, CK; Lee, IB
- Date Issued
- 2006-10
- Publisher
- SPRINGER
- Abstract
- Bioprocesses and biosystems have nonlinear and multiple operation patterns depending on the influent loads, temperatures, the activity of microorganisms, and other factors. In this paper, an integrated framework of nonlinear modeling and process monitoring methods is developed for a complex biological process. The proposed method is based on modeling by fuzzy partial least squares (FPLS) and on process monitoring by a statistical decomposition, which is suitable for predicting and supervising a nonlinear biological process. Case studies in the bio-simulated process and industrial biological plant show that the proposed method can give superior prediction and monitoring performance in complex biological plants compared to other linear and nonlinear methods, since it can effectively capture the nonlinear causal relationship within the biosystem. This gives us the integrated framework that is able to both model and monitor the nonlinear bioprocess simultaneously.
- Keywords
- bioprocess monitoring; fault detection; fuzzy; integrated framework; multivariate statistical process control (MSPC); nonlinear modeling; systems engineering; PRINCIPAL COMPONENT ANALYSIS; NEURAL NETWORKS; MODELS; FUZZY
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/23825
- DOI
- 10.1007/S00449-006-0
- ISSN
- 1615-7591
- Article Type
- Article
- Citation
- BIOPROCESS AND BIOSYSTEMS ENGINEERING, vol. 29, no. 4, page. 213 - 228, 2006-10
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.