| Description |
1 online resource |
| Contents |
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 species -- 2.1.1. 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 several parameters -- 3.2.6. Choice of growth rate model -- 3.3. Mass balance models -- 3.4. 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 |
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4.5.2. Example -- 4.6. Experimental validation on Chlorella vulgaris culture -- 4.7. 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 disadvantages -- 5.6.4. 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 |
| Summary |
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.) |
| Notes |
Print version record |
| Subject |
Microalgae -- Biotechnology
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Carbon dioxide -- Metabolism
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Carbon sequestration
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Carbon dioxide -- Metabolism
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Carbon sequestration
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Microalgae -- Biotechnology
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| Form |
Electronic book
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| ISBN |
1306958350 |
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9781306958356 |
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9781118984468 |
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1118984463 |
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9781118984475 |
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1118984471 |
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