{"product_id":"automatic-control-of-bioprocesses-hardback-9781848210257","title":"Automatic Control of Bioprocesses (Hardback) 9781848210257","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAutomatic Control of Bioprocesses\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eDenis Dochain (Edited by), D Dochain (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781848210257, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 27 June 2008\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e248 pages\u003cbr\u003e24.1 x 16.3 x 2.1 cm, 0.499 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eGiving an overview of the challenges in the control of bioprocesses, this comprehensive book presents key results in various fields, including: dynamic modeling; dynamic properties of bioprocess models; software sensors designed for the on-line estimation of parameters and state variables; control and supervision of bioprocesses.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eChapter 1. What are the Challenges for the Control of Bioprocesses? 11\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eDenis DOCHAIN\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1. Introduction . 11\u003c\/p\u003e \u003cp\u003e1.2. Specific problems of bioprocess control 12\u003c\/p\u003e \u003cp\u003e1.3. A schematic view of monitoring and control of a bioprocess 12\u003c\/p\u003e \u003cp\u003e1.4. Modeling and identification of bioprocesses: some key ideas 13\u003c\/p\u003e \u003cp\u003e1.5. Software sensors: tools for bioprocess monitoring 14\u003c\/p\u003e \u003cp\u003e1.6. Bioprocess control: basic concepts and advanced control15\u003c\/p\u003e \u003cp\u003e1.7. Bioprocess monitoring: the central issue 15\u003c\/p\u003e \u003cp\u003e1.8. Conclusions 16\u003c\/p\u003e \u003cp\u003e1.9. Bibliography 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Dynamic Models of Biochemical Processes: Properties of Models 17\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eOlivier BERNARD and Isabelle QUEINNEC\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1. Introduction 17\u003c\/p\u003e \u003cp\u003e2.2. Description of biochemical processes 18\u003c\/p\u003e \u003cp\u003e2.2.1. Micro-organisms and their use 18\u003c\/p\u003e \u003cp\u003e2.2.2. Types of bioreactors 19\u003c\/p\u003e \u003cp\u003e2.2.3. Three operating modes 19\u003c\/p\u003e \u003cp\u003e2.3. Mass balance modeling 21\u003c\/p\u003e \u003cp\u003e2.3.1. Introduction 21\u003c\/p\u003e \u003cp\u003e2.3.2. Reaction scheme 21\u003c\/p\u003e \u003cp\u003e2.3.3. Choice of reactions and variables 23\u003c\/p\u003e \u003cp\u003e2.3.4. Example 1 23\u003c\/p\u003e \u003cp\u003e2.4. Mass balance models 24\u003c\/p\u003e \u003cp\u003e2.4.1. Introduction 24\u003c\/p\u003e \u003cp\u003e2.4.2. Example 2 24\u003c\/p\u003e \u003cp\u003e2.4.3. Example 3 25\u003c\/p\u003e \u003cp\u003e2.4.4. Matrix representation 25\u003c\/p\u003e \u003cp\u003e2.4.4.1. Example 2 (continuation) 26\u003c\/p\u003e \u003cp\u003e2.4.4.2. Example 1 (continuation) 26\u003c\/p\u003e \u003cp\u003e2.4.5. Gaseous flow 27\u003c\/p\u003e \u003cp\u003e2.4.6. Electroneutrality and affinity constants 27\u003c\/p\u003e \u003cp\u003e2.4.7. Example 1 (continuation) 28\u003c\/p\u003e \u003cp\u003e2.4.8. Conclusion 29\u003c\/p\u003e \u003cp\u003e2.5. Kinetics 30\u003c\/p\u003e \u003cp\u003e2.5.1. Introduction 30\u003c\/p\u003e \u003cp\u003e2.5.2. Mathematical constraints 30\u003c\/p\u003e \u003cp\u003e2.5.2.1. Positivity of variables 30\u003c\/p\u003e \u003cp\u003e2.5.2.2. Variables necessary for the reaction 31\u003c\/p\u003e \u003cp\u003e2.5.2.3. Example 1 (continuation) 31\u003c\/p\u003e \u003cp\u003e2.5.2.4. Phenomenological knowledge 31\u003c\/p\u003e \u003cp\u003e2.5.3. Specific growth rate 32\u003c\/p\u003e \u003cp\u003e2.5.4. Representation of kinetics by means of a neural network 34\u003c\/p\u003e \u003cp\u003e2.6. Validation of the model 35\u003c\/p\u003e \u003cp\u003e2.6.1. Introduction 35\u003c\/p\u003e \u003cp\u003e2.6.2. Validation of the reaction scheme 35\u003c\/p\u003e \u003cp\u003e2.6.2.1. Mathematical principle 35\u003c\/p\u003e \u003cp\u003e2.6.2.2. Example 4 36\u003c\/p\u003e \u003cp\u003e2.6.3. Qualitative validation of model 37\u003c\/p\u003e \u003cp\u003e2.6.4. Global validation of the model 39\u003c\/p\u003e \u003cp\u003e2.7. Properties of the models 39\u003c\/p\u003e \u003cp\u003e2.7.1. Boundedness and positivity of variables 39\u003c\/p\u003e \u003cp\u003e2.7.2. Equilibrium points and local behavior 40\u003c\/p\u003e \u003cp\u003e2.7.2.1. Introduction 40\u003c\/p\u003e \u003cp\u003e2.8. Conclusion 42\u003c\/p\u003e \u003cp\u003e2.9. Bibliography 43\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. Identification of Bioprocess Models 47\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eDenis DOCHAIN and Peter VANROLLEGHEM\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1. Introduction 47\u003c\/p\u003e \u003cp\u003e3.2. Structural identifiability 48\u003c\/p\u003e \u003cp\u003e3.2.1. Development in Taylor series 49\u003c\/p\u003e \u003cp\u003e3.2.2. Generating series 50\u003c\/p\u003e \u003cp\u003e3.2.3. Examples for the application of the methods of development in series 50\u003c\/p\u003e \u003cp\u003e3.2.4. Some observations on the methods for testing structural identifiability 51\u003c\/p\u003e \u003cp\u003e3.3. Practical identifiability 52\u003c\/p\u003e \u003cp\u003e3.3.1. Theoretical framework 52\u003c\/p\u003e \u003cp\u003e3.3.2. Confidence interval of the estimated parameters 54\u003c\/p\u003e \u003cp\u003e3.3.3. Sensitivity functions 55\u003c\/p\u003e \u003cp\u003e3.4. Optimum experiment design for parameter estimation (OED\/PE) 57\u003c\/p\u003e \u003cp\u003e3.4.1. Introduction 57\u003c\/p\u003e \u003cp\u003e3.4.2. Theoretical basis for the OED\/PE 59\u003c\/p\u003e \u003cp\u003e3.4.3. Examples 61\u003c\/p\u003e \u003cp\u003e3.5. Estimation algorithms 63\u003c\/p\u003e \u003cp\u003e3.5.1. Choice of two datasets 63\u003c\/p\u003e \u003cp\u003e3.5.2. Elements of parameter estimation: least squares estimation in the linear case 64\u003c\/p\u003e \u003cp\u003e3.5.3. Overview of the parameter estimation algorithms 65\u003c\/p\u003e \u003cp\u003e3.6. A case study: identification of parameters for a process modeled for anaerobic digestion 68\u003c\/p\u003e \u003cp\u003e3.6.1. The model 69\u003c\/p\u003e \u003cp\u003e3.6.2. Experiment design 70\u003c\/p\u003e \u003cp\u003e3.6.3. Choice of data for calibration and validation 70\u003c\/p\u003e \u003cp\u003e3.6.4. Parameter identification 71\u003c\/p\u003e \u003cp\u003e3.6.5. Analysis of the results 75\u003c\/p\u003e \u003cp\u003e3.7. Bibliography 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. State Estimation for Bioprocesses 79\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eOlivier BERNARD and Jean-Luc GOUZÉ\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1. Introduction 79\u003c\/p\u003e \u003cp\u003e4.2. Notions on system observability 80\u003c\/p\u003e \u003cp\u003e4.2.1. System observability: definitions 80\u003c\/p\u003e \u003cp\u003e4.2.2. General definition of an observer 81\u003c\/p\u003e \u003cp\u003e4.2.3. How to manage the uncertainties in the model or in the output 83\u003c\/p\u003e \u003cp\u003e4.3. Observers for linear systems 84\u003c\/p\u003e \u003cp\u003e4.3.1. Luenberger observer 85\u003c\/p\u003e \u003cp\u003e4.3.2. The linear case up to an output injection 86\u003c\/p\u003e \u003cp\u003e4.3.3. Local observation of a nonlinear system around an equilibrium point 86\u003c\/p\u003e \u003cp\u003e4.3.4. PI observer 87\u003c\/p\u003e \u003cp\u003e4.3.5. Kalman filter 87\u003c\/p\u003e \u003cp\u003e4.3.6. The extended Kalman filter 89\u003c\/p\u003e \u003cp\u003e4.4. High gain observers 89\u003c\/p\u003e \u003cp\u003e4.4.1. Definitions, hypotheses 89\u003c\/p\u003e \u003cp\u003e4.4.2. Change of variable 90\u003c\/p\u003e \u003cp\u003e4.4.3. Fixed gain observer 91\u003c\/p\u003e \u003cp\u003e4.4.4. Variable gain observers (Kalman-like observer) 91\u003c\/p\u003e \u003cp\u003e4.4.5. Example: growth of micro-algae 92\u003c\/p\u003e \u003cp\u003e4.5. Observers for mass balance-based systems 94\u003c\/p\u003e \u003cp\u003e4.5.1. Introduction 94\u003c\/p\u003e \u003cp\u003e4.5.2. Definitions, hypotheses 96\u003c\/p\u003e \u003cp\u003e4.5.3. The asymptotic observer 96\u003c\/p\u003e \u003cp\u003e4.5.4. Example 98\u003c\/p\u003e \u003cp\u003e4.5.5. Improvements 99\u003c\/p\u003e \u003cp\u003e4.6. Interval observers 101\u003c\/p\u003e \u003cp\u003e4.6.1. Principle 102\u003c\/p\u003e \u003cp\u003e4.6.2. The linear case up to an output injection 103\u003c\/p\u003e \u003cp\u003e4.6.3. Interval estimator for an activated sludge process 105\u003c\/p\u003e \u003cp\u003e4.6.4. Bundle of observers 107\u003c\/p\u003e \u003cp\u003e4.7. Conclusion 110\u003c\/p\u003e \u003cp\u003e4.8. Appendix: a comparison theorem 111\u003c\/p\u003e \u003cp\u003e4.9. Bibliography 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. Recursive Parameter Estimation 115\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eDenis DOCHAIN\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1. Introduction 115\u003c\/p\u003e \u003cp\u003e5.2. Parameter estimation based on the structure of the observer 116\u003c\/p\u003e \u003cp\u003e5.2.1. Example: culture of animal cells 116\u003c\/p\u003e \u003cp\u003e5.2.2. Estimator based on the structure of the observer 117\u003c\/p\u003e \u003cp\u003e5.2.3. Example: culture of animal cells (continued) 119\u003c\/p\u003e \u003cp\u003e5.2.4. Calibration of the estimator based on the structure of the observer: theory 119\u003c\/p\u003e \u003cp\u003e5.2.5. Calibration of the estimator based on the structure of the observer: application to the culture of animal cells 124\u003c\/p\u003e \u003cp\u003e5.2.6. Experimental results 127\u003c\/p\u003e \u003cp\u003e5.3. Recursive least squares estimator 129\u003c\/p\u003e \u003cp\u003e5.4. Adaptive state observer 133\u003c\/p\u003e \u003cp\u003e5.4.1. Generalization 138\u003c\/p\u003e \u003cp\u003e5.5. Conclusions 140\u003c\/p\u003e \u003cp\u003e5.6. Bibliography 141\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. Basic Concepts of Bioprocess_Control 143\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eDenis DOCHAIN and Jérôme HARMAND\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1. Introduction 143\u003c\/p\u003e \u003cp\u003e6.2.1. Biological system dynamics 144\u003c\/p\u003e \u003cp\u003e6.2.2. Sources of uncertainties and disturbances of biological systems 146\u003c\/p\u003e \u003cp\u003e6.3. Stability of biological processes 147\u003c\/p\u003e \u003cp\u003e6.3.1. Basic concept of the stability of a dynamic system 147\u003c\/p\u003e \u003cp\u003e6.3.2. Equilibrium point 148\u003c\/p\u003e \u003cp\u003e6.3.3. Stability analysis 149\u003c\/p\u003e \u003cp\u003e6.4. Basic concepts of biological process control 150\u003c\/p\u003e \u003cp\u003e6.4.1. Regulation and tracking control 150\u003c\/p\u003e \u003cp\u003e6.4.2. Strategy selection: direct and indirect control 151\u003c\/p\u003e \u003cp\u003e6.2. Bioprocess control: basic concepts 144\u003c\/p\u003e \u003cp\u003e6.4.3. Selection of synthesis method 152\u003c\/p\u003e \u003cp\u003e6.5. Synthesis of biological process control laws 153\u003c\/p\u003e \u003cp\u003e6.5.1. Representation of systems 153\u003c\/p\u003e \u003cp\u003e6.5.2. Structure of control laws 154\u003c\/p\u003e \u003cp\u003e6.6. Advanced control laws 160\u003c\/p\u003e \u003cp\u003e6.6.1. A nonlinear PI controller 160\u003c\/p\u003e \u003cp\u003e6.6.2. Robust control 162\u003c\/p\u003e \u003cp\u003e6.7. Specific approaches 165\u003c\/p\u003e \u003cp\u003e6.7.1. Pulse control: a dialog with bacteria 165\u003c\/p\u003e \u003cp\u003e6.7.2. Overall process optimization: towards integrating the control objectives in the initial stage of bioprocess design 167\u003c\/p\u003e \u003cp\u003e6.8. Conclusions and perspectives 170\u003c\/p\u003e \u003cp\u003e6.9. Bibliography 170\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. Adaptive Linearizing Control and Extremum-Seeking Control of Bioprocesses 173\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eDenis DOCHAIN, Martin GUAY, Michel PERRIER and Mariana TITICA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1. Introduction 173\u003c\/p\u003e \u003cp\u003e7.2. Adaptive linearizing control of bioprocesses 174\u003c\/p\u003e \u003cp\u003e7.2.1. Design of the adaptive linearizing controller 174\u003c\/p\u003e \u003cp\u003e7.2.2. Example 1: anaerobic digestion 176\u003c\/p\u003e \u003cp\u003e7.2.2.1. Model order reduction 177\u003c\/p\u003e \u003cp\u003e7.2.2.2. Adaptive linearizing control design 179\u003c\/p\u003e \u003cp\u003e7.2.3. Example 2: activated sludge process 183\u003c\/p\u003e \u003cp\u003e7.3. Adaptive extremum-seeking control of bioprocesses 188\u003c\/p\u003e \u003cp\u003e7.3.1. Fed-batch reactor model 189\u003c\/p\u003e \u003cp\u003e7.3.2. Estimation and controller design 191\u003c\/p\u003e \u003cp\u003e7.3.2.1. Estimation equation for the gaseous outflow rate y 191\u003c\/p\u003e \u003cp\u003e7.3.2.2. Design of the adaptive extremum-seeking controller 192\u003c\/p\u003e \u003cp\u003e7.3.2.3. Stability and convergence analysis 195\u003c\/p\u003e \u003cp\u003e7.3.2.4. A note on dither signal design 196\u003c\/p\u003e \u003cp\u003e7.3.3. Simulation results 197\u003c\/p\u003e \u003cp\u003e7.4. Appendix: analysis of the parameter convergence 202\u003c\/p\u003e \u003cp\u003e7.5. Bibliography 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8. Tools for Fault Detection and Diagnosis 211\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eJean-Philippe STEYER, Antoine GÉNOVÉSI and Jérôme HARMAND\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1. Introduction 211\u003c\/p\u003e \u003cp\u003e8.2. General definitions 212\u003c\/p\u003e \u003cp\u003e8.2.1. Terminology 212\u003c\/p\u003e \u003cp\u003e8.2.2. Fault types 213\u003c\/p\u003e \u003cp\u003e8.3. Fault detection and diagnosis 214\u003c\/p\u003e \u003cp\u003e8.3.1. Methods based directly on signals 215\u003c\/p\u003e \u003cp\u003e8.3.1.1. Hardware redundancy 215\u003c\/p\u003e \u003cp\u003e8.3.1.2. Specific sensors 216\u003c\/p\u003e \u003cp\u003e8.3.1.3. Comparison of thresholds 217\u003c\/p\u003e \u003cp\u003e8.3.1.4. Spectral analysis 217\u003c\/p\u003e \u003cp\u003e8.3.1.5. Statistical approaches 218\u003c\/p\u003e \u003cp\u003e8.3.2. Model-based methods 218\u003c\/p\u003e \u003cp\u003e8.3.2.1. Parity space 219\u003c\/p\u003e \u003cp\u003e8.3.2.2. Observers 220\u003c\/p\u003e \u003cp\u003e8.3.2.3. Parametric estimation 221\u003c\/p\u003e \u003cp\u003e8.3.3. Methods based on expertise 222\u003c\/p\u003e \u003cp\u003e8.3.3.1. AI models 223\u003c\/p\u003e \u003cp\u003e8.3.3.2. Artificial neural networks 224\u003c\/p\u003e \u003cp\u003e8.3.3.3. Fuzzy inference systems 225\u003c\/p\u003e \u003cp\u003e8.3.4. Choice and combined use of diverse methods 227\u003c\/p\u003e \u003cp\u003e8.4. Application to biological processes 227\u003c\/p\u003e \u003cp\u003e8.4.1. “Simple” biological processes 228\u003c\/p\u003e \u003cp\u003e8.4.2. Wastewater treatment processes 229\u003c\/p\u003e \u003cp\u003e8.5. Conclusion 231\u003c\/p\u003e \u003cp\u003e8.6. Bibliography 232\u003c\/p\u003e \u003cp\u003e\u003ci\u003eList of Authors 239\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eIndex 241\u003c\/i\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Biology, life sciences [\u003ca title=\"See our other books on Biology, life sciences\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Biology,%20life%20sciences%20%5BPS%5D%22\"\u003ePS\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand New","offer_id":52449373585688,"sku":"9781848210257","price":122.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781848210257.jpg?v=1785197120","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/automatic-control-of-bioprocesses-hardback-9781848210257","provider":"Freshly Printed Books","version":"1.0","type":"link"}