2015
DOI: 10.1002/jctb.4642
|View full text |Cite
|
Sign up to set email alerts
|

Solid‐state fermentation process model reparametrization procedure for parameters estimation using particle swarm optimization

Abstract: BACKGROUND Solid‐state fermentation is a well‐known bioprocess. The development of a solid‐state fermentation reactor faces difficulties in controlling temperature gradients inside the bed. To understand this behavior, a good phenomenological model must be used. Furthermore, the model parameters must be obtained by a reliable and robust parameters estimation procedure, since often the model parameters are not available in the literature. RESULTS The heuristic particle swarm optimization (PSO) method was used t… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
6
0

Year Published

2018
2018
2024
2024

Publication Types

Select...
4

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(6 citation statements)
references
References 24 publications
0
6
0
Order By: Relevance
“…These models were selected because only significant variables were used. Silveira, Mazutti, and Salau (2016) have reported the usefulness of reparametrized models as a mathematical tool for precisely and reliably performing modeling using fewer parameters, providing more degrees of freedom for statistical analysis and good precision of the results.…”
Section: Discussionmentioning
confidence: 99%
“…These models were selected because only significant variables were used. Silveira, Mazutti, and Salau (2016) have reported the usefulness of reparametrized models as a mathematical tool for precisely and reliably performing modeling using fewer parameters, providing more degrees of freedom for statistical analysis and good precision of the results.…”
Section: Discussionmentioning
confidence: 99%
“…The optimum parameter value is obtained when the NLLS objective function of this work is minimized, i.e. when the differences between experimental ( X exp ) and simulated ( X mod ) data are minimized, as given by Eqn (16): []normalminf()Xj=Xj,expiXj,modi2i=0,,normalNY1j=1,normalNE=j=1NEi=0normalNY1Fobj0.25em[]Xj(),ji0.25em …”
Section: Methodsmentioning
confidence: 99%
“…[25][26][27][28] The optimum parameter value is obtained when the NLLS objective function of this work is minimized, i.e. when the differences between experimental (X exp ) and simulated (X mod ) data are minimized, as given by Eqn (16): [29][30][31][32][33][34] […”
Section: Methodsmentioning
confidence: 99%
“…To avoid local minima convergence, we used a Particle Swarm Optimization algorithm (PSO, particleswarm function of MATLAB software) [53,54] to initially search for the most promising region for the global minimum of the Least Squares Objective Function. After obtaining a solution using the particleswarm algorithm, the results were assumed as initial guesses for a Levenberg-Marquardt algorithm (lsqnonlin function of MATLAB software) [55][56][57] as a form of final refinement of the obtained solution [58,59].…”
Section: Adjusting the Data And Selecting The Appropriate Modelmentioning
confidence: 99%