A survey of computational and physical methods applied to solid-state fermentation

Lenz, J.; Höfer, Milan; Krasenbrink, J.-B.; Hölker, Udo

doi:10.1007/s00253-004-1592-8

Cited by 26 publications

(8 citation statements)

References 95 publications

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“…Comparando los resultados del RMSE mediante las RNA y el modelo logístico para la concentración de proteína, se pueden observar que estos valores son muy similares de la misma manera si se compara el RMSE para la concentración de ergosterol lo cual indica que los dos métodos podrían predecir el comportamiento del crecimiento para el Pleurotus pulmonarius en FES empleando tusa de maíz como sustrato. Tal como se mencionó en la introducción, uno de los grandes inconvenientes y retos que enfrenta la FES es la medición y modelación del crecimiento; las RNA aparecen como herramientas para modelar el crecimiento en FES, sobre todo por lo complicado que es el fenómeno o cuando poco se sabe del mismo, tal como sucedió al inicio de este trabajo (Thomas et al, 2013;Zhang et al, 2012Lenz et al, 2004. Ejemplo de implementación de RNA en fermentaciones es el trabajo realizado por Desai y colaboradores (2008) arrojando valores de RMSE de 0,11 y 0,73.…”

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Análisis de la Medición de la Biomasa en Fermentación en Estado Sólido empleando el Modelo Logístico y Redes Neuronales

Oviedo

Casas

Valencia³

et al. 2014

Inf. tecnol.

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ResumenEn este trabajo, se llevó a cabo el análisis del crecimiento del Pleurotus pulmonarius en tusa de maíz, empleando redes neuronales artificiales y el modelo logístico. La biomasa se cuantificó a través de las concentraciones de proteína (método Kjeldahl) y ergosterol (cromatografía líquida de alta resolución). Los datos obtenidos fueron analizados con los programas R y Matlab. El mejor r 2 ajustado del modelo logístico fue de 0,9937 en la concentración de proteína para el análisis ensayo por ensayo. Las redes neuronales artificiales, obtuvieron sumatorias medias de los errores cuadráticos de 0,017 y 11,394 para las concentraciones de proteína y ergosterol respectivamente. Los resultados muestran que el modelo logístico y las redes neuronales artificiales son herramientas útiles para modelar en fermentación sólida. Los mejores ajustes fueron para la concentración de proteína. Palabras clave: fes, rna, biomasa, modelo logístico, Pleurotus pulmonarius Analysis of Biomass Measurement in Solid-State Fermentation using Neural Networks and a Logistic Model AbstractIn this work, the analysis of the growth of Pleurotus pulmonarius in corn cob, using artificial neural networks and a logistic model was carried out. Biomass is quantified through the concentrations of protein (Kjeldahl method) and ergosterol (High performance liquid chromatography). The data obtained were analyzed with the Matlab and R programs. The best adjusted r 2 of the logistic model was 0.9937 in the concentration for test by test protein analysis. For the artificial neural network model the root mean square error was 0.017 for the concentrations of protein and 11.394 for ergosterol. The results show that the logistic model and the artificial neural network model are useful tools for modeling solid fermentation. The best results were found for the concentration of protein.

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Section: Resultados Con Las Rnaunclassified

Análisis de la Medición de la Biomasa en Fermentación en Estado Sólido empleando el Modelo Logístico y Redes Neuronales

Oviedo

Casas

Valencia³

et al. 2014

Inf. tecnol.

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“…Lenz et al . reports that process simulations and computer models will enable SSF scale‐up, with parameters optimization and advanced process control …”

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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“…Recently, mathematical models and computer simulations have significantly advanced our understanding of the biophysical mechanisms underlying these phenomena [5] and bioreactor design [6]. Several models that can be used to describe, optimize, and scale up the growth of microorganisms in SSF are shown in Table 1 [6].…”

Section: Introductionmentioning

confidence: 99%

Heat and Mass Transfer Measurements for Tray-Fermented Fungal Products

Jou

2011

Int J Thermophys

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In this study, heat and mass transfer in static tray fermentation, which is widely used in solid-state fermentation (SSF) to produce fungal products, such as enzymes or koji, is investigated. Specifically, kinetic models of transport phenomena in the whole-tray chamber are emphasized. The effects of temperature, moisture, and humidity on microbial growth in large-scale static tray fermentation are essential to scale-up SSF and achieve uniform fermentation. In addition, heat and mass transfer of static tray fermentation of Trichoderma fungi with two tray setups-traditional linen coverings and stacks in a temperature-humidity chamber is examined. In both these setups, the following factors of fermentation were measured: air velocity, air temperature, illumination, pH, carbon dioxide (CO 2 ) concentration, and substrate temperature, and the effects of bed height, moisture of substrate, and relative humidity of air are studied. A thin (1 cm) bed at 28 • C and 95 % relative humidity is found to be optimum. Furthermore, mixing was essential for achieving uniform fermentation of Trichoderma fungi. This study has important applications in large-scale static tray fermentation of fungi.

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A survey of computational and physical methods applied to solid-state fermentation

Cited by 26 publications

References 95 publications

Análisis de la Medición de la Biomasa en Fermentación en Estado Sólido empleando el Modelo Logístico y Redes Neuronales

Análisis de la Medición de la Biomasa en Fermentación en Estado Sólido empleando el Modelo Logístico y Redes Neuronales

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

Heat and Mass Transfer Measurements for Tray-Fermented Fungal Products

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