A Survey of Computational and Physical Methods Applied to Solid‐State Fermentation

Lenz, J.; Hoefer, Moritz; Krasenbrink, J.-B.; Hoelker, U.

doi:10.1002/chin.200448273

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“…4 Lenz et al reports that process simulations and computer models will enable SSF scale-up, with parameters optimization and advanced process control. 5 Good assumptions are necessary to produce a model that predicts the experimental data, or process behavior, with great fidelity. 6 Also, optimum parameters are essential to maintain model accuracy in predicting experimental data.…”

Section: Introductionmentioning

confidence: 99%

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

Silveira

Mazutti

Salau

2015

J of Chemical Tech & Biotech

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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 to estimate the parameters of the reparametrized model through the minimization of a nonlinear‐square objective function, since it is less sensitive to initial guesses than derivative based methods. Statistical analyses were made to evaluate the parameter estimation quality. Also, simulations with the reparametrized model were performed. All the analyses have revealed the great accuracy of the proposed model, predicting with very small residuals (errors) several experimental data sets of a solid‐state fermentation process. CONCLUSION Parameter models reparametrization procedure can be a very useful mathematical tool to obtain accurate and reliable models with fewer parameters to be estimated, providing less parameters correlation and increasing the degrees of freedom to perform the statistical analysis. In addition, the particle swarm optimization was found to be a very robust and efficient parameter estimation method. © 2015 Society of Chemical Industry

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Section: Introductionmentioning

confidence: 99%