title: Integrated framework of nonlinear prediction and process monitoring for complex biological processes.
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.
authors.affiliation: Department of Chemical Engineering, School of Environmental Engineering and Science, POSTECH, San 31 Hyoja Dong, Pohang, 790-784, South Korea. ChangKyo.Yoo@biomath.ugent.be
authors.forename: Chang Kyoo
authors.initials: CK
authors.lastname: Yoo
authors.forename: In-Beum
authors.initials: IB
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D018407:Bacterial Physiological Phenomena; D019149:Bioreactors / Q000382:microbiology*; D003198:Computer Simulation; D008954:Models, Biological; D017711:Nonlinear Dynamics; D018511:Systems Integration