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Record Nr. |
UNINA9910132204903321 |
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Autore |
Tebbani Sihem |
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Titolo |
CO2 biofixation by microalgae : modeling, estimation and control / / Sihem Tebbani [and four others] |
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Pubbl/distr/stampa |
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London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014 |
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©2014 |
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ISBN |
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1-118-98445-5 |
1-118-98447-1 |
1-118-98446-3 |
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Descrizione fisica |
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1 online resource (191 p.) |
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Collana |
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Focus : Bioengineering and Health Science Series, , 2051-249X |
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Disciplina |
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Soggetti |
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Microalgae - Biotechnology |
Carbon dioxide - Metabolism |
Carbon sequestration |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Cover; Title Page; Copyright; Contents ; Introduction; Chapter 1. Microalgae; 1.1. Definition; 1.2. Characteristics; 1.3. Uses of microalgae; 1.3.1. Nutrition; 1.3.2. Pharmaceuticals; 1.3.3. Cosmetics; 1.3.4. Energy; 1.3.5. Environmental field; 1.4. Microalgae cultivation systems; 1.4.1. Open systems; 1.4.2. Closed systems: photobioreactors; 1.5. Factors affecting algae cultivation; 1.5.1. Light; 1.5.2. Temperature; 1.5.3. pH; 1.5.4. Nutrients; 1.5.5. Medium salinity; 1.5.6. Agitation; 1.5.7. Gas-liquid mass transfer; 1.6. Conclusion; Chapter 2. Co2 Biofixation |
2.1. Selection of microalgae species2.1.1. Photosynthetic activity; 2.1.2. CO2 concentrating mechanism "CCM"; 2.1.3. Choice of the microalgae species; 2.2. Optimization of the photobioreactor design; 2.3. Conclusion; Chapter 3. Bioprocess Modeling; 3.1. Operating modes; 3.1.1. Batch mode; 3.1.2. Fed-batch mode; 3.1.3. Continuous mode; 3.2. Growth rate modeling; 3.2.1. General models; 3.2.2. Droop's model; 3.2.3. Models dealing with light effect; 3.2.4. Model dealing with carbon effect; 3.2.5. Models of the simultaneous influence of |
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several parameters; 3.2.6. Choice of growth rate model |
3.3. Mass balance models3.4. Model parameter identification; 3.5. Example: Chlorella vulgaris culture; 3.5.1. Experimental set-up; 3.5.2. Modeling; 3.5.3. Parametric identification; 3.6. Conclusion; Chapter 4. Estimation of Biomass Concentration; 4.1. Generalities on estimation; 4.2. State of the art; 4.3. Kalman filter; 4.3.1. Principle; 4.3.2. Discrete Kalman filter; 4.3.3. Discrete extended Kalman filter; 4.3.4. Kalman filter settings; 4.3.5. Example; 4.4. Asymptotic observer; 4.4.1. Principle; 4.4.2. Example; 4.5. Interval observer; 4.5.1. Principle; 4.5.2. Example |
4.6. Experimental validation on Chlorella vulgaris culture4.7. Conclusion; Chapter 5. Bioprocess Control; 5.1. Determination of optimal operating conditions; 5.1.1. Optimal operating conditions; 5.1.2. Optimal set-point; 5.2. Generalities on control; 5.3. State of the art; 5.4. Generic Model Control; 5.4.1. Principle; 5.4.2. Advantages and disadvantages; 5.4.3. Example; 5.5. Input/output linearizing control; 5.5.1. Principle; 5.5.2. Advantages and disadvantages; 5.5.3. Example; 5.6. Nonlinear model predictive control; 5.6.1. Principle; 5.6.2. Nonlinear Model Predictive Control |
5.6.3. Advantages and disadvantages5.6.4. Example; 5.7. Application to Chlorella vulgaris cultures; 5.7.1. GMC law performance; 5.7.2. Performance of the predictive control law; 5.8. Conclusion; Conclusion; Bibliography; Index |
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Sommario/riassunto |
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Due to the consequences of globa l warming and significant greenhouse gas emissions, several ideas have been studied to reduce these emissions or to suggest solut ions for pollutant remov al. The most promising ideas are reduced consumption, waste recovery and waste treatment by biological systems. In this latter category, studies have demonstrated that the use of microalgae is a very promising solution for the biofixation of carbon dioxide. In fact, these micro-organisms are able to offset high levels of CO2 thanks to photosynthesis. Microalgae are also used in various fields (food industr |
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2. |
Record Nr. |
UNINA9910254317403321 |
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Autore |
Yu Qiang |
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Titolo |
Neuromorphic Cognitive Systems : A Learning and Memory Centered Approach / / by Qiang Yu, Huajin Tang, Jun Hu, Kay Tan Chen |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (XIV, 172 p.) |
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Collana |
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Intelligent Systems Reference Library, , 1868-4394 ; ; 126 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Artificial intelligence |
Neurosciences |
Computational Intelligence |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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Introduction -- Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons -- A Spike-Timing Based Integrated Model for Pattern Recognition -- Precise-Spike-Driven Synaptic Plasticity for Hetero Association of Spatiotemporal Spike Patterns -- A Spiking Neural Network System for Robust Sequence Recognition -- Temporal Learning in Multilayer Spiking Neural Networks Through Construction of Causal Connections -- A Hierarchically Organized Memory Model with Temporal Population Coding -- Spiking Neuron Based Cognitive Memory Model. |
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Sommario/riassunto |
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This book presents neuromorphic cognitive systems from a learning and memory-centered perspective. It illustrates how to build a system network of neurons to perform spike-based information processing, computing, and high-level cognitive tasks. It is beneficial to a wide spectrum of readers, including undergraduate and postgraduate students and researchers who are interested in neuromorphic computing and neuromorphic engineering, as well as engineers and professionals in industry who are involved in the design and applications of neuromorphic cognitive systems, neuromorphic sensors |
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and processors, and cognitive robotics. The book formulates a systematic framework, from the basic mathematical and computational methods in spike-based neural encoding, learning in both single and multi-layered networks, to a near cognitive level composed of memory and cognition. Since the mechanisms for integrating spiking neurons integrate to formulate cognitive functions as in the brain are little understood, studies of neuromorphic cognitive systems are urgently needed. The topics covered in this book range from the neuronal level to the system level. In the neuronal level, synaptic adaptation plays an important role in learning patterns. In order to perform higher-level cognitive functions such as recognition and memory, spiking neurons with learning abilities are consistently integrated, building a system with encoding, learning and memory functionalities. The book describes these aspects in detail. |
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