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El. knyga: CO2 Biofixation by Microalgae: Modeling, Estimation and Control

  • Formatas: EPUB+DRM
  • Išleidimo metai: 09-Jul-2014
  • Leidėjas: ISTE Ltd and John Wiley & Sons Inc
  • Kalba: eng
  • ISBN-13: 9781118984451
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  • Formatas: EPUB+DRM
  • Išleidimo metai: 09-Jul-2014
  • Leidėjas: ISTE Ltd and John Wiley & Sons Inc
  • Kalba: eng
  • ISBN-13: 9781118984451
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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 industry, fertilizers, biofuel, etc.). To obtain a n optimal C O2 sequestration us ing micr oal gae, their cul tivatio n has to be c arried ou t in a f avorable e nvironment, corresponding to optimal operating conditions (temperature, nutrients, pH, light, etc.). Therefore, microalgae are grown in an enclosure, i.e. photobioreactors, which notably operate in continuous mode. This type of closed reactor notably enables us to reduce culture contamination, to improve CO2 transfer and to better control the cultivation system. This last point involves the regulation of concentrations (biomass, substrate or by-product) in addition to conventional regulations (pH, temperature).
To do this, we have to establish a model of the system and to identify its parameters; to put in place estimators in order to rebuild variables that are not measured online (software sensor); and finally to implement a control law, in order to maintain the system in optimal conditions despite modeling errors and environmental disturbances that can have an influence on the system (pH variations, temperature, light, biofilm appearance, etc.).

Introduction ix
Chapter 1 Microalgae
1(22)
1.1 Definition
1(1)
1.2 Characteristics
2(1)
1.3 Uses of microalgae
3(7)
1.3.1 Nutrition
3(1)
1.3.2 Pharmaceuticals
4(1)
1.3.3 Cosmetics
4(1)
1.3.4 Energy
4(2)
1.3.5 Environmental field
6(4)
1.4 Microalgae cultivation systems
10(4)
1.4.1 Open systems
10(2)
1.4.2 Closed systems: photobioreactors
12(2)
1.5 Factors affecting algae cultivation
14(7)
1.5.1 Light
15(1)
1.5.2 Temperature
16(1)
1.5.3 pH
17(1)
1.5.4 Nutrients
18(2)
1.5.5 Medium salinity
20(1)
1.5.6 Agitation
20(1)
1.5.7 Gas--liquid mass transfer
21(1)
1.6 Conclusion
21(2)
Chapter 2 CO2 Biofixation
23(10)
2.1 Selection of microalgae species
25(6)
2.1.1 Photosynthetic activity
25(1)
2.1.2 CO2 concentrating mechanism "CCM"
26(1)
2.1.3 Choice of the microalgae species
27(4)
2.2 Optimization of the photobioreactor design
31(1)
2.3 Conclusion
32(1)
Chapter 3 Bioprocess Modeling
33(32)
3.1 Operating modes
33(4)
3.1.1 Batch mode
34(1)
3.1.2 Fed-batch mode
35(1)
3.1.3 Continuous mode
35(2)
3.2 Growth rate modeling
37(10)
3.2.1 General models
38(1)
3.2.2 Droop's model
39(1)
3.2.3 Models dealing with light effect
40(1)
3.2.4 Model dealing with carbon effect
41(1)
3.2.5 Models of the simultaneous influence of several parameters
42(3)
3.2.6 Choice of growth rate model
45(2)
3.3 Mass balance models
47(2)
3.4 Model parameter identification
49(2)
3.5 Example: Chlorella vulgaris culture
51(12)
3.5.1 Experimental set-up
51(3)
3.5.2 Modeling
54(3)
3.5.3 Parametric identification
57(6)
3.6 Conclusion
63(2)
Chapter 4 Estimation of Biomass Concentration
65(38)
4.1 Generalities on estimation
65(3)
4.2 State of the art
68(4)
4.3 Kalman filter
72(8)
4.3.1 Principle
72(1)
4.3.2 Discrete Kalman filter
73(2)
4.3.3 Discrete extended Kalman filter
75(2)
4.3.4 Kalman filter settings
77(1)
4.3.5 Example
78(2)
4.4 Asymptotic observer
80(4)
4.4.1 Principle
80(2)
4.4.2 Example
82(2)
4.5 Interval observer
84(14)
4.5.1 Principle
84(2)
4.5.2 Example
86(12)
4.6 Experimental validation on Chlorella vulgaris culture
98(3)
4.7 Conclusion
101(2)
Chapter 5 Bioprocess Control
103(44)
5.1 Determination of optimal operating conditions
104(2)
5.1.1 Optimal operating conditions
104(1)
5.1.2 Optimal set-point
104(2)
5.2 Generalities on control
106(2)
5.3 State of the art
108(2)
5.4 Generic Model Control
110(4)
5.4.1 Principle
110(2)
5.4.2 Advantages and disadvantages
112(1)
5.4.3 Example
113(1)
5.5 Input/output linearizing control
114(5)
5.5.1 Principle
114(2)
5.5.2 Advantages and disadvantages
116(1)
5.5.3 Example
117(2)
5.6 Nonlinear model predictive control
119(13)
5.6.1 Principle
119(2)
5.6.2 Nonlinear Model Predictive Control
121(5)
5.6.3 Advantages and disadvantages
126(1)
5.6.4 Example
127(5)
5.7 Application to Chlorella vulgaris cultures
132(12)
5.7.1 GMC law performance
135(4)
5.7.2 Performance of the predictive control law
139(5)
5.8 Conclusion
144(3)
Conclusion 147(6)
Bibliography 153(20)
Index 173
Sihem Tebbani is Associate Professor in the Automatic Control department at SUPELEC in Gif-sur-Yvette, France. Her research interests include modeling, estimation, optimization and control of bioprocesses, and more particularly of microalgae and bacteria cultures. Filipa Lopes is Associate Professor at LGPM, Ecole Centrale Paris, France. Her research interests are in the field of biological engineering with a focus on biofilms (biofouling, disinfection, dispersion, modeling) and bioprocess development (bacteria, microalgae) for wastewater treatment, high-value products and bio-energy production.

Rayen Filali has a PhD in Automatic Control obtained at SUPELEC in Gif-sur-Yvette, France. His PhD thesis, in the framework of a collaboration between SUPELEC and Ecole Centrale Paris, deals with the estimation and the robust control laws of microalgae cultures for the optimization of CO2 biological consumption.

Didier Dumur is Professor in the Automatic Control department at SUPELEC in Gif-sur-Yvette, France. His research interests cover theoretical and methodological aspects related to predictive control strategies and their application in multiple domains (robotics, bioprocesses, temperature control of buildings, etc.).

Dominique Pareau is Professor at LGPM and Director of the White Biotechnologies Chair of the Ecole Centrale Paris, France. Her research concerns chemical engineering and biotechnologies, from the understanding of microscopic phenomena to process design, by coupling modeling and experimentation, with applications in the fields of agroresources, microalgae, waste and effluent treatments.