ie501298b_si_001.pdf (1.37 MB)
HIPPO: An Iterative Reparametrization Method for Identification and Calibration of Dynamic Bioreactor Models of Complex Processes
journal contribution
posted on 2014-12-03, 00:00 authored by Benjamín
J. Sánchez, Daniela
C. Soto, Héctor Jorquera, Claudio A. Gelmi, José R. Pérez-CorreaUnstructured,
dynamic bioreactor models of complex processes usually
possess many nonidentifiable, insensitive, or statistically nonsignificant
parameters. However, an exhaustive search to find a reduced set of
identifiable parameters is computationally demanding. We developed
a heuristic iterative procedure for parameter optimization (HIPPO),
generic and free of symbolic mathematical manipulations, to help bioprocess
engineers to find and estimate a set of significant parameters, thus
obtaining reliable models. This methodology includes local sensitivity,
significance and identifiability analysis, metaheuristic optimization,
and fitting performance assessments. We tested this method with two
bioreactor models: a microalgal fed-batch bioreactor (MFB) 12-parameter
model and a solid substrate fermentation (SSF) 14-parameter model.
Applying this procedure, we were able to find a MFB reparametrization
with 4 fitting parameters after 479 iterations and a SSF reparametrization
with 6 fitting parameters after 918 iterations. In both cases, all
fitting parameters were statistically significant and accurately estimated
and models had a better fit than the originally fitted 12- and 14-parameter
models. The heuristic was programmed using MATLAB and is freely available
to the research community (http://www.systemsbiology.cl/tools/).