9783642067921 - Modelling and Optimization of Biotechnological Processes: Artificial Intelligence Approaches: 15 (Studies in Computational Intelligence) de Chen, Lei Zhi Zhi; Nguan... (13 resultados)

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Taschenbuch. Condición: Neu. Modelling and Optimization of Biotechnological Processes | Artificial Intelligence Approaches | Lei Zhi Chen (u. a.) | Taschenbuch | Studies in Computational Intelligence | viii | Englisch | 2010 | Springer | EAN 9783642067921 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Mostindustrialbiotechnologicalprocessesareoperatedempirically.Oneof the major di culties of applying advanced control theories is the highly nonlinear nature of the processes. This book examines approaches based on arti cial intelligencemethods,inparticular,geneticalgorithmsandneuralnetworks,for monitoring, modelling and optimization of fed-batch fermentation processes. The main aim of a process control is to maximize the nal product with minimum development and production costs. This book is interdisciplinary in nature, combining topics from biotechn- ogy, arti cial intelligence, system identi cation, process monitoring, process modelling and optimal control. Both simulation and experimental validation are performed in this study to demonstrate the suitability and feasibility of proposed methodologies. An online biomass sensor is constructed using a - current neural network for predicting the biomass concentration online with only three measurements (dissolved oxygen, volume and feed rate). Results show that the proposed sensor is comparable or even superior to other sensors proposed in the literature that use more than three measurements. Biote- nological processes are modelled by cascading two recurrent neural networks. It is found that neural models are able to describe the processes with high accuracy. Optimization of the nal product is achieved using modi ed genetic algorithms to determine optimal feed rate pro les. Experimental results of the corresponding production yields demonstrate that genetic algorithms are powerful tools for optimization of highly nonlinear systems. Moreover, a c- bination of recurrentneural networks and genetic algorithms provides a useful and cost-e ective methodology for optimizing biotechnological processes.

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Condición: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Mostindustrialbiotechnologicalprocessesareoperatedempirically.Oneofthe major di?culties of applying advanced control theories is the highly nonlinear nature of the processes. This book examines approaches based on arti?cial intelligencemethods,inparticular,geneticalgorithmsandneuralnetworks,for monitoring, modelling and optimization of fed-batch fermentation processes. The main aim of a process control is to maximize the ?nal product with minimum development and production costs. This book is interdisciplinary in nature, combining topics from biotechn- ogy, arti?cial intelligence, system identi?cation, process monitoring, process modelling and optimal control. Both simulation and experimental validation are performed in this study to demonstrate the suitability and feasibility of proposed methodologies. An online biomass sensor is constructed using a - current neural network for predicting the biomass concentration online with only three measurements (dissolved oxygen, volume and feed rate). Results show that the proposed sensor is comparable or even superior to other sensors proposed in the literature that use more than three measurements. Biote- nological processes are modelled by cascading two recurrent neural networks. It is found that neural models are able to describe the processes with high accuracy. Optimization of the ?nal product is achieved using modi?ed genetic algorithms to determine optimal feed rate pro?les. Experimental results of the corresponding production yields demonstrate that genetic algorithms are powerful tools for optimization of highly nonlinear systems. Moreover, a c- bination of recurrentneural networks and genetic algorithms provides a useful and cost-e?ective methodology for optimizing biotechnological processes.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Mostindustrialbiotechnologicalprocessesareoperatedempirically.Oneofthe major di culties of applying advanced control theories is the highly nonlinear nature of the processes. This book examines approaches based on arti cial intelligencemethods,inparticular,geneticalgorithmsandneuralnetworks ,for monitoring, modelling and optimization of fed-batch fermentation processes. The main aim of a process control is to maximize the nal product with minimum development and production costs. This book is interdisciplinary in nature, combining topics from biotechn- ogy, arti cial intelligence, system identi cation, process monitoring, process modelling and optimal control. Both simulation and experimental validation are performed in this study to demonstrate the suitability and feasibility of proposed methodologies. An online biomass sensor is constructed using a - current neural network for predicting the biomass concentration online with only three measurements (dissolved oxygen, volume and feed rate). Results show that the proposed sensor is comparable or even superior to other sensors proposed in the literature that use more than three measurements. Biote- nological processes are modelled by cascading two recurrent neural networks. It is found that neural models are able to describe the processes with high accuracy. Optimization of the nal product is achieved using modi ed genetic algorithms to determine optimal feed rate pro les. Experimental results of the corresponding production yields demonstrate that genetic algorithms are powerful tools for optimization of highly nonlinear systems. Moreover, a c- bination of recurrentneural networks and genetic algorithms provides a useful and cost-e ective methodology for optimizing biotechnological processes. 132 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Mostindustrialbiotechnologicalprocessesareoperatedempirically.Oneofthe major di?culties of applying advanced control theories is the highly nonlinear nature of the processes. This book examines approaches based on arti?cial intelligencemethods,inparticular,.

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Condición: New. PRINT ON DEMAND pp. 132.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Mostindustrialbiotechnologicalprocessesareoperatedempirically.Oneofthe major di culties of applying advanced control theories is the highly nonlinear nature of the processes. This book examines approaches based on arti cial intelligencemethods,inparticular,geneticalgorithmsandneuralnetworks,for monitoring, modelling and optimization of fed-batch fermentation processes. The main aim of a process control is to maximize the nal product with minimum development and production costs. This book is interdisciplinary in nature, combining topics from biotechn- ogy, arti cial intelligence, system identi cation, process monitoring, process modelling and optimal control. Both simulation and experimental validation are performed in this study to demonstrate the suitability and feasibility of proposed methodologies. An online biomass sensor is constructed using a - current neural network for predicting the biomass concentration online with only three measurements (dissolved oxygen, volume and feed rate). Results show that the proposed sensor is comparable or even superior to other sensors proposed in the literature that use more than three measurements. Biote- nological processes are modelled by cascading two recurrent neural networks. It is found that neural models are able to describe the processes with high accuracy. Optimization of the nal product is achieved using modi ed genetic algorithms to determine optimal feed rate pro les. Experimental results of the corresponding production yields demonstrate that genetic algorithms are powerful tools for optimization of highly nonlinear systems. Moreover, a c- bination of recurrentneural networks and genetic algorithms provides a useful and cost-e ective methodology for optimizing biotechnological processes.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 132 pp. Englisch.