Isbn: 9783540306344 - modelling and optimization of biotechnological processes: artificial intelligence approaches: 15 (studies in computational intelligence, 15) (6 resultados)

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    • Idioma: Inglés

      Editorial: Springer, 2006

      354030634X / 9783540306344

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      Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections

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      EUR 116,42

      Envío por EUR 13,17 
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      Condición: New. In.

    • Idioma: Inglés

      Editorial: Springer, 2006

      354030634X / 9783540306344

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      Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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      Condición: Nuevo

      EUR 150,10

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      Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

    • Idioma: Inglés

      Editorial: Springer, 2006

      354030634X / 9783540306344

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      Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books

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      Condición: Usado - Como Nuevo

      EUR 204,28

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      Cantidad disponible: 1 disponibles

      Hardcover. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

    • Idioma: Inglés

      Editorial: Springer Berlin Heidelberg Jan 2006, 2006

      354030634X / 9783540306344

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      Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.

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      Condición: Nuevo

      EUR 106,99

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      Cantidad disponible: 2 disponibles

      Buch. 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.

    • Idioma: Inglés

      Editorial: Springer Berlin Heidelberg, 2006

      354030634X / 9783540306344

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      Librería: moluna, Greven, Alemaniamoluna

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      Condición: Nuevo

      EUR 92,27

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      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..

    • Idioma: Inglés

      Editorial: Springer, Springer Jan 2006, 2006

      354030634X / 9783540306344

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      Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000

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      Condición: Nuevo

      EUR 106,99

      Envío por EUR 60,00 
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      Cantidad disponible: 1 disponibles

      Buch. 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.