Computational Intelligence Techniques for Bioprocess by M. C. Nicoletti, L. C. Jain, R. C. Giordano (auth.), Maria PDF

By M. C. Nicoletti, L. C. Jain, R. C. Giordano (auth.), Maria do Carmo Nicoletti, Lakhmi C. Jain (eds.)

ISBN-10: 3642018874

ISBN-13: 9783642018879

ISBN-10: 3642018882

ISBN-13: 9783642018886

This study e-book provides using computational intelligence paradigms in bioprocess comparable initiatives particularly modeling, supervision, tracking and keep an eye on, diagnostic, studying and optimization, with purposes to numerous parts. geared toward researchers, practitioners and graduate scholars, it's going to additionally function a textual content for complex classes in chemical engineering, bioinformatics and biotechnology, and for desktop scientists attracted to bioprocesses. Chapters are self-contained and lots of of them comprise purposes to real-world difficulties: - Computational Intelligence recommendations as instruments for Bioprocess Modelling, Optimization, Supervision and regulate; - software program Sensors and their functions in Bioprocess; - tracking of Bioprocesses: Mechanistic and Data-driven techniques; - Novel Computational tools for Modeling and keep watch over in Chemical and Biochemical procedure platforms; - Computational Intelligence ideas for Supervision and analysis of organic Wastewater therapy structures; - Multiobjective Genetic Algorithms for the Optimisation of Wastewater remedy strategies; - info Reconciliation utilizing Neural Networks for the selection of KLa; - A Computational clever established strategy for the advance of a minimum outlined Medium: program to Human Interleukin-3 creation by means of treptomyces lividans sixty six; - Bioprocess Modelling for studying version Predictive keep an eye on (L-MPC); - functionality tracking and Batch to Batch regulate of Biotechnological procedures; - Modelling of Biotechnological strategies - an process in response to man made Neural Networks.

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Additional info for Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control

Sample text

Differential Evolution: A Practical Approach to Global Optimization. Springer, Berlin (2005) 92. : A hybrid method of differential evolution with application to optimal control problems of a bioprocess system. In: Proc. of The IEEE Conference on Evolutionary Computation, Anchorage, pp. 627–631 (1998) 93. : Estimation of Monod model parameters by hybrid differential evolution. Bioprocess and Biosystems Engineering 24(2), 109–113 (2001) 94. : Multiobjective parameter estimation problems of fermentation processes using a high ethanol tolerance yeast.

Biomass concentration estimations in different initial conditions 40 H. 3 Summary In this section, a recursive estimator of state variables (biomass, substrate, ethanol and dissolved oxygen concentrations, and the fermenter volume) using two observed variables (the substrate and dissolved oxygen concentration) implemented in a fedbatch yeast fermentation process based on the use of the extended Kalman filter is developed and tested. The state estimates are accurate, and the EKF has confirmed its ability to incorporate the effects of measurement and modeling noises.

The Kalman filter is the optimal state estimator for a linear system when a model for the system together with the knowledge of certain stochastic properties of measurement and disturbance noises are available. The EKF is an adaptation to the nonlinear case of the linear Kalman filter. The EKF method optimally tries to estimate the state of the system by assuming that, (1) the behavior of the system is described by a non-linear model; and (2) the mean and the covariance of the measurement errors are known.

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Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control by M. C. Nicoletti, L. C. Jain, R. C. Giordano (auth.), Maria do Carmo Nicoletti, Lakhmi C. Jain (eds.)

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