CO2 Biofixation by Microalgae

Modeling, Estimation and Control

Inbunden, Engelska, 2014

AvSihem Tebbani,Rayen Filali,Filipa Lopes,Didier Dumur,Dominique Pareau

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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.).

Produktinformation

Utgivningsdatum2014-07-01
Mått163 x 241 x 20 mm
Vikt481 g
FormatInbunden
SpråkEngelska
Antal sidor192
FörlagISTE Ltd and John Wiley & Sons Inc
ISBN9781848215986

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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.

Introduction ixChapter 1 Microalgae 11.1 Definition 11.2 Characteristics 21.3 Uses of microalgae 31.4 Microalgae cultivation systems 101.5 Factors affecting algae cultivation 141.6 Conclusion 21Chapter 2 CO2 Biofixation 232.1 Selection of microalgae species 252.2 Optimization of the photobioreactor design 312.3 Conclusion 32Chapter 3 Bioprocess Modeling 333.1 Operating modes 333.2 Growth rate modeling 373.3 Mass balance models 473.4 Model parameter identification 493.5 Example: Chlorella vulgaris culture 513.6 Conclusion 63Chapter 4 Estimation of Biomass Concentration 654.1 Generalities on estimation 654.2 State of the art 684.3 Kalman filter 724.4 Asymptotic observer 804.5 Interval observer 844.6 Experimental validation on Chlorella vulgaris culture 984.7 Conclusion 101Chapter 5 Bioprocess Control 1035.1 Determination of optimal operating conditions 1045.2 Generalities on control 1065.3 State of the art 1085.4 Generic Model Control 1105.5 Input/output linearizing control 1145.6 Nonlinear model predictive control 1195.7 Application to Chlorella vulgaris cultures 1325.8 Conclusion 144Conclusion 147Bibliography 153Index 173