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  • 1. Bachinger, T.
    et al.
    Mandenius, Carl-Fredrik
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för fysik, kemi och biologi, Teknisk biologi.
    Physiologically motivated monitoring of fermentation processes by means of an electronic nose2001Inngår i: Chemical Engineering & Technology, ISSN 0930-7516, E-ISSN 1521-4125, Vol. 24, nr 7, s. 33-42Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    An on-line approach of non-invasive monitoring of the physiological changes in fermentation processes is presented. In yeast batch and bacterial fed-batch fermentations it is shown that metabolic state changes can be revealed using an electronic nose. The transient responses of the gas sensors to the changes in the composition of the volatiles emitted from the cell cultures during fermentation are used to retrieve a semi-quantitative representation of the physiological state of the cultures. With the sensor responses of the electronic nose it is shown that physiological variables such as rates of growth, substrate uptake and product formation can be depicted. The non-invasive method thus seems as a pertinent alternative to conventional bioreactor monitoring methods.

  • 2.
    Bruening, Simone
    et al.
    University of Appl Science Bremen, Germany.
    Gerlach, Inga
    Linköpings universitet, Institutionen för fysik, kemi och biologi. Linköpings universitet, Tekniska fakulteten. University of Appl Science Bremen, Germany.
    Poertner, Ralf
    Hamburg University of Technology, Germany.
    Mandenius, Carl-Fredrik
    Linköpings universitet, Institutionen för fysik, kemi och biologi, Teknisk biologi. Linköpings universitet, Tekniska fakulteten.
    Hass, Volker C.
    Furtwangen University, Germany.
    Modeling Suspension Cultures of Microbial and Mammalian Cells with an Adaptable Six-Compartment Model2017Inngår i: Chemical Engineering & Technology, ISSN 0930-7516, E-ISSN 1521-4125, Vol. 40, nr 5, s. 956-966Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Process models can be used for model-based control strategies, but model development is a time-consuming and laborious task. To reduce the modeling effort, a new structured compartment model was developed, which may easily be adapted to different cultivation processes. The proposed six-compartment model was used to describe the time courses of cultivations of bacteria, yeast, fungi, and mammalian cell lines, namely, Escherichia coli,Lactobacillus delbrueckii, Saccharomyces cerevisiae, Cyathus striatus, and a hybridoma mammalian cell line. The model can describe the time courses of important state variables and can be adapted to the cultivation processes by parameterization. This reduces the modeling effort for a new process significantly.

  • 3.
    Mandenius, Carl-Fredrik
    Linköpings universitet, Institutionen för fysik, kemi och biologi, Teknisk biologi. Linköpings universitet, Tekniska högskolan.
    Design of Monitoring and Sensor Systems for Bioprocesses by Biomechatronic Methodology2012Inngår i: Chemical Engineering & Technology, ISSN 0930-7516, E-ISSN 1521-4125, Vol. 35, nr 8, s. 1412-1420Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Biomechatronic design methodology is discussed for the design of monitoring and sensor systems for biotechnological processes. The bioprocess monitoring systems are examples of complex biomechatronic systems between electronic, mechanical, and biological subsystems, and where in particular the biological parts, through active microbial or cellular components, influence the design solutions in a complex manner. The biomechatronic approach facilitates configuration of the monitoring and sensor systems with production and quality needs in focus, and can by that lead to more efficient design solutions.

  • 4. Skoglund, A.
    et al.
    Brundin, Anders
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för fysik, kemi och biologi, Teknisk biologi.
    Mandenius, Carl-Fredrik
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för fysik, kemi och biologi, Teknisk biologi.
    Comparison between linear and nonlinear prediction models for monitoring of a paperboard machine2002Inngår i: Chemical Engineering & Technology, ISSN 0930-7516, E-ISSN 1521-4125, Vol. 25, nr 2, s. 197-202Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Data from a paperboard machine were used to compare the performance of linear partial least squares (PLS) and nonlinear feed-forward neural network (FFNN) modeling of a continuous process. Fifteen selected variables were used as input parameters to the models, while the quality class of the manufactured product was the output response. The models were validated with external data different to those used in the design of the models. Evaluation with root mean square error of prediction (RMSEP) showed that the FFNN models were better for prediction than the PLS models. For monitoring, however, the PLS models detected deviations from normal settings in the paperboard machine more sensitively than the FFNN models. It is suggested that these findings have general relevance to other continuous processes in manufacturing industries too.

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