Premium
Making sense of parameter estimation and model simulation in bioprocesses
Author(s) -
SadinoRiquelme M. Constanza,
Rivas José,
Jeison David,
Hayes Robert E.,
DonosoBravo Andrés
Publication year - 2020
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.27294
Subject(s) - nonlinear regression , process (computing) , computer science , nonlinear system , statistical model , statistical hypothesis testing , regression analysis , industrial fermentation , estimation theory , mathematics , biological system , biochemical engineering , statistics , econometrics , engineering , chemistry , biology , physics , food science , quantum mechanics , fermentation , operating system
Most articles that report fitted parameters for kinetic models do not include meaningful statistical information. This study demonstrates the importance of reporting a complete statistical analysis and shows a methodology to perform it, using functionalities implemented in computational tools. As an example, alginate production is studied in a batch stirred‐tank fermenter and modeled using the kinetic model proposed by Klimek and Ollis (1980). The model parameters and their 95% confidence intervals are estimated by nonlinear regression. The significance of the parameters value is checked using a hypothesis test. The uncertainty of the parameters is propagated to the output model variables through prediction intervals, showing that the kinetic model of Klimek and Ollis (1980) can simulate with high certainty the dynamic of the alginate production process. Finally, the results obtained in other studies are compared to show how the lack of statistical analysis can hold back a deeper understanding about bioprocesses.