Bioreaction Engineering: Modeling and Control

ISBN-13: 9783642641039 / Angielski / Miękka / 2011 / 604 str.

Karl Schugerl; K. -H Bellgardt

Opis

The book is intended to present various examples for reactor and process modeling and control as well as for metabolic flux analysis and metabolic design at an ad vanced level. In Part A, General principles and techniques with regard to reactor and process models, process control, and metabolic flux analysis are presented. In addition the accuracy, precision, and reliability of the measured data are discussed which are ex tremely important for process modeling and control. A virtual bioreactor system is presented as well, which can be used for the training of students and operators of industrial plants and for the development of advanced automation tools. In Part B, the General principles are applied for particular bioreactor models. It covers the application of the computational fluiddynamic (CFD) technique to stirred tank and bubble column bioreactors. Different solution methods are presented: the Reynolds-averaging of the turbulent Navier-Stokes equations and modeling of the Reynolds stresses with an appropriate turbulence (k-ee) model, and the Euler (two fluid model), as well as the Euler-Langrange approaches."

Spis treści

The Need for Modeling and Control in Biotechnical Processes.- Some Modeling Basics.- Structure and Operation of Biotechnical Plant.- Types and Structure Elements of the Bioreactor.- The Stirred Tank Reactor as an Example for Reactors with Mechanical Energy Input; Reactors with Energy Input by Compressed Air; Membrane Reactors for Bubble Free Aeration; Liquid-Phase; Gas-Phase; Solid-Phase; Biotic Phase; Modes of Operation of a Bioreactor; Batch Cultivation; Fed-Batch Cultivation; Continuous Cultivation; Cultivation with Cell Retention; Repeated or Cyclic Batch or Fed-Batch Cultivation; Aerobic Processes; Anaerobic Processes; Micro-Aerobic Processes;.- References.- A General Principles and Techniques.- 1 Bioreactor Models.- 1.1 Introduction.- 1.2 Interrelations Between the Cells and Their Physical/ Chemical Environment.- 1.3 Stirred Tank (ST) Reactors.- 1.3.1 Description of the Physical Processes in the Reactors.- 1.3.2 Reactor Models.- 1.3.2.1 Model for the Ideal Stirred Tank Reactor.- 1.4 Bubble Column (BC) and Airlift Tower Loop (ATL) Reactors.- 1.4.1 Description of the Physical Processes in the Reactors.- 1.4.2 Flow Models.- 1.4.3 Reactor Models.- 1.5 Conclusions.- References.- 2 Bioprocess Models.- 2.1 Introduction.- 2.1.1 Intracellular Structure Elements.- 2.1.2 Regulation of the Metabolism.- 2.1.2.1 Bottle-Neck Principle.- 2.1.2.2 Optimality Principle.- 2.1.3 Kinetics of Growth and Product Formation.- 2.1.4 General Model Structure for Biotechnical Processes.- 2.1.5 Transport in Microbial Aggregates-.- 2.2 Unstructured Models.- 2.2.1 Kinetics of Growth and Substrate Uptake.- 2.2.2 Endogenous and Maintenance Metabolism.- 2.2.3 Product Formation.- 2.2.4 Other Parameters Influencing Growth.- 2.3 Structured Models.- 2.3.1 The Constitutive Equations.- 2.3.2 Some Applications of Structured Models.- 2.3.3 Cybernetic Models of the Compartment Type.- 2.3.4 Cybernetic Models of the Metabolic Regulator Type.- 2.4 Segregated Models.- 2.4.1 Simple Segregated Models.- 2.4.2 Segregated Models for Physiological Properties.- 2.4.3 A Model for Spatial Segregation by Wall Attachment.- 2.4.4 Segregated Models for Morphological Differentiation, Morphologically Structured Models.- 2.4.5 Segregated Models for Recombinant Organisms.- 2.4.6 Population Balance Models.- References.- 3 Metabolic Flux Analysis.- 3.1 Introduction.- 3.2 Flux Quantification Methods.- 3.2.1 Metabolite Balancing.- 3.2.2 Isotopic-Tracer Techniques.- 3.3 Applications of Metabolic Flux Analysis in the Elucidation of Metabolic Networks.- 3.4 Conclusions.- References.- 4 Accuracy and Reliability of Measured Data.- 4.1 Accuracy and Reliability of Measured Data.- 4.1.1 Accuracy and Precision of Measurements.- 4.1.2 Accuracy.- 4.1.3 Precision.- 4.2 Measurement Reliability.- 4.2.1 Assessment of Measured Data Reliability by Means of a Knowledge-Based System.- 4.2.2 Numerical and Statistical Tests Performed by the Knowledge-Based System.- 4.2.3 Knowledge-Based Module.- 4.2.4 Methodology of the Knowledge-Based System.- 4.3 Conclusions.- References.- 5 Bioprocess Control.- 5.1 Introduction.- 5.2 Bioprocess Control: Basic Concepts.- 5.2.1 Disturbances.- 5.2.2 Stability.- 5.2.2.1 Equilibrium Points.- 5.2.2.2 Stability Analysis.- 5.2.3 Regulation vs Tracking.- 5.3 Bioprocess Control: Basic Ingredients.- 5.3.1 Dynamical Model.- 5.3.2 Feedback.- 5.3.3 Proportional Action.- 5.3.4 Integral Action.- 5.3.5 Feedforward Action.- 5.3.6 Linear Control vs Nonlinear Control.- 5.3.6.1 Linear Control.- 5.3.6.2 Nonlinear Control.- 5.3.7 Adaptive Control vs Non-Adaptive Control.- 5.3.8 Other Approaches.- 5.4 Adaptive Linearizing Control of Bioprocesses.- 5.4.1 General Dynamical Model.- 5.4.1.1 Example 1: Anaerobic Digestion.- 5.4.1.2 Example 2: Animal Cell Culture.- 5.4.2 Model Reduction.- 5.4.2.1 Singular Perturbation Technique for Low Solubility Products.- 5.4.2.2 A General Rule for Order Reduction.- 5.4.2.3 Example 1: Anaerobic Digestion.- 5.4.3 Control Design.- 5.4.3.1 The Monitoring Tool 1: An Asymptotic Observer.- 5.4.3.2 The Monitoring Tool 2: The Parameter Estimation.- 5.4.3.3 The Control Tool: The Adaptive Linearizing Controller.- 5.4.4 Experimental Results.- References.- 6 On-Line Simulation Techniques for Bioreactor Control Development.- 6.1 Introduction.- 6.2 Application.- 6.2.1 Application in the Biochemical Industry.- 6.2.1.1 Plant Set Up.- 6.2.1.2 Economy.- 6.2.1.3 Quality.- 6.2.1.4 Validation.- 6.2.1.5 Complexity.- 6.2.1.6 Training.- 6.2.2 Application in Education.- 6.3 General Architecture of On-Line Simulation Systems.- 6.3.1 Components of Simulation Systems.- 6.3.1.1 Models.- 6.3.1.2 Numerical Methods.- 6.3.1.3 User Interface.- 6.4 Full Scope Model of the Fermentation Process.- 6.5 Submodels of the Bioreactor Process.- 6.5.1 Engineering Components.- 6.5.1.1 Temperature Control System.- 6.5.1.2 Pressure Behavior.- 6.5.1.3 Aeration Behavior.- 6.6 Mass Balances of the Complete Aerobic Growth Process.- 6.6.1 Gas Phase Balances.- 6.6.2 The O2- and CO2-Transfer Equations.- 6.6.3 The kLa Correlation.- 6.6.4 The Liquid Phase Balances.- 6.6.5 The Feed and Titration Vessels System.- 6.7 The pH Model.- 6.8 The Reaction Model.- 6.9 Application Examples of On-Line Simulation Techniques.- 6.9.1 Training with Virtual Reaction Processes.- 6.9.2 Development of a High Cell Density Cultivation.- 6.9.2.1 The µ-Stat Problem.- 6.9.2.2 Observation of Cell-Specific Growth Rate.- 6.9.2.3 Course and Testing of Processing Strategies.- 6.10 Summary.- References.- B Application of General Principles for Reactor Models.- 7 Application of Computational Fluiddynamics (CFD) to Modeling Stirred Tank Bioreactors.- 7.1 Introduction.- 7.2 Modeling and Simulation of Gas/Liquid Flow in Stirred Tank Reactors.- 7.3 Single Phase Flow.- 7.3.1 Transport Equations.- 7.3.2 Simulations and Comparison with Experimental Observations.- 7.4 Multiple Impellers.- 7.5 Gas-Liquid Flow.- 7.5.1 Interfacial Forces.- 7.5.1.1 Drag Force.- 7.5.1.2 Virtual Mass Force.- 7.5.2 Turbulence Model.- 7.5.3 Impeller Model.- 7.5.4 Simulation Results.- 7.6 Application of CFD to Simulations of Mixing and Biotechnical Processes.- 7.6.1 Methodology.- 7.6.2 Simulation of Tracer Experiments.- 7.6.3 Simulation of Substrate Distributions in Fed Batch Fermentations.- 7.6.4 Production of Acetoin/Butanediol with Bacillus subtilis.- References.- 8 Bubble Column Bioreactors.- 8.1 Introduction.- 8.2 Phenomenology.- 8.3 Basic Equations of Motion.- 8.3.1 Fundamental Laws of Fluid Motion.- 8.3.1.1 Mass Conservation.- 8.3.1.2 Conservation of Momentum.- 8.3.1.3 Navier-Stokes Equation System.- 8.3.1.4 Problems with Solving the Equations of Motion.- 8.3.1.5 Numerical Aspects.- 8.3.2 Two-Fluid Model.- 8.3.3 Euler-Lagrange Approach.- 8.3.3.1 Dynamics of the Dispersed Gas-Phase.- 8.3.3.2 Effective Viscosity.- 8.3.3.3 Mass Transfer and Chemical Reaction.- 8.3.3.4 Mixing Due to the Bubble Rise.- 8.3.3.5 Problem of Bubble Coalescence and Redispersion.- 8.3.3.6 Rating of the Euler-Lagrange Representation.- 8.4 Modeling of Particular Aspects of Bubble Column Reactors.- 8.4.1 Velocity Patterns in Bubble Column Reactors.- 8.4.2 Fate of Individual Cells in the Bubble Column Bioreactor.- 8.4.3 Influence of Tilted Columns.- 8.4.4 Oxygen Distribution in a Yeast Fermenter.- 8.5 Conclusions.- References.- C Application of General Principles for Process Models Including Control.- 9 Baker’s Yeast Production.- 9.1 Introduction.- 9.1.1 Metabolic Types of Yeast Growth and Regulatory Effects.- 9.1.2 The Asymmetric Propagation of Yeast.- 9.2 Growth Modeling.- 9.2.1 Stoichiometric Model.- 9.2.2 Cybernetic Modeling of Metabolic Regulation.- 9.2.3 Application of the Model for Simulation of Batch, Fed-Batch, and Continuous Cultivations.- 9.3 Growth in Airlift Tower-Loop Reactors.- 9.4 Population Balance Models for the Asymmetric Cell Cycle of Yeast.- 9.4.1 Age Distribution Model of Yeast for Batch and Fed-Batch Processes.- 9.4.2 Age Distribution Model for Data Analysis of Stable Synchronous Oscillations in a Chemostat.- 9.5 Considerations for Process Optimization.- 9.5.1 Optimization of Product Quality.- 9.5.2 Economic Optimization.- 9.6 Automatic Control of Fed-Batch Processes.- 9.6.1 General Remarks.- 9.6.2 Examples for Applied Control Systems.- References.- 10 Modeling of the Beer Fermentation Process.- 10.1 Introduction.- 10.2 Process Optimization.- 10.2.1 Different Knowledge Representation Techniques.- 10.2.1.1 Classical Approach.- 10.2.1.2 Heuristic Approach.- 10.2.1.3 Alternative Methods to Describe the Kinetics.- 10.2.2 State Prediction for Process Optimization.- 10.2.3 Remarks on Hybrid Models.- 10.3 Process Supervision.- 10.3.1 On-Line Measurement are Difficult to Perform.- 10.3.2 Estimation of the Extract Degradation.- 10.3.2.1 Simple Mathematical Model.- 10.3.2.2 Estimation of the Extract Degradation by Artificial Neural Networks.- 10.3.2.3 Hybrid Modeling.- 10.3.3 Kalman Filters, and an Advanced Method for State Estimation.- 10.4 Process Control.- 10.4.1 Controllers that Consider the Dynamics of the Fermenters.- 10.4.2 Reduction of Energy Costs by Temperature Profile Optimization and Control in a Production-Scale Brewery.- 10.5 Conclusion.- 10.5.1 Summary of the Application of the Techniques to Beer Fermentation.- References.- 11 Lactic Acid Production.- 11.1 Introduction.- 11.2 Classification of Lactic Bacteria.- 11.3 Sugar Metabolism of LAB.- 11.3.1 An Example Showing the Functioning of PTS Systems.- 11.3.2 Sugar Uptake by LAB in General.- 11.3.3 Homolactic vs Heterolactic Fermentation.- 11.4 Nitrogen Uptake and Metabolism.- 11.5 Growth Kinetics and Product Formation Kinetics.- 11.6 Lactic Acid Production on the Industrial Scale.- 11.7 Process Technology in Lactic Acid Fermentation.- References.- 12 Control Strategies for High-Cell Density Cultivation ofEscherichia coli.- 12.1 Introduction.- 12.2 Basic Modeling of a Fed-Batch Strategy.- 12.2.1 The Physiological Model.- 12.2.2 The Reactor Model.- 12.3 Growth Rate Control via Substrate Feeding.- 12.4 Growth Rate Control via Oxygen Supply.- 12.5 Considerations for Improved Observation and Control.- 12.6 A Case Study: Kinetics of Acetate Formation and Recombinant Protein Synthesis in HCDC.- References.- 13 ?-Lactam Antibiotics Production withPenicillium chrysogenum and Acremonium chrysogenum.- 13.1 Introduction.- 13.2 Modeling of Penicillin Production.- 13.2.1 Unstructured and Simple Segregated Models.- 13.2.2 Biosynthesis Model of Penicillin V.- 13.2.3 Morphologically Structured Models for Growth of Hyphae.- 13.2.4 Models for Growth of Fungal Pellets.- 13.2.5 Models for Growth of Pellet Populations.- 13.3 Modeling of Cephalosporin C Production.- 13.3.1 Biosynthesis of Cephalosporin.- 13.3.2 Simple Cybernetic Model for Growth and Production on Sugar and Soy-Oil.- 13.3.3 Segregated Models Describing Morphological Differentiation.- 13.4 Process Control and Optimization.- 13.4.1 Problems and Possibilities.- 13.4.2 Example for Dynamic Optimal Control of Fed-Batch Antibiotics Production.- 13.4.3 Economic Optimization for Mycelia Fed-Batch Cultivation.- References.- D Metabolite Flux Analysis, Metabolic Design.- 14 Quantitative Analysis of Metabolic and Signaling Pathways inSaccharomyces cerevisiae.- 14.1 Introduction.- 14.2 Metabolic Flux Analysis.- 14.2.1 Metabolite Balancing in Compartmented Systems.- 14.2.2 Stoichiometric Model.- 14.2.3 Computational Aspects.- 14.2.4 Results.- 14.3 Measurement of Intracellular Compounds.- 14.3.1 Measurement of Intracellular Metabolites and Signals - General Tools.- 14.3.2 Dynamic Response of Metabolite Pools of Glycolysis.- 14.3.3 Dynamics of the Pentose Phosphate Pathway - an Example for in vivo Diagnosis of Intracellular Enzyme Kinetics.- 14.4 Quantitative Analysis of Glucose Induced Signal Transduction.- 14.4.1 Measurement of Intracellular cAMP.- 14.4.2 Measurement of the PFK2 Activity.- 14.4.3 Measurement of F2,6bP.- 14.5 Comparison Between in vitro and in vivo Kinetics - Illustrated for the Enzyme PFK1 (Phosphofructokinase 1).- References.- 15 Metabolic Analysis ofZymomonas mobilis.- 15.1 Introduction.- 15.1.1 Zymomonas mobilis.- 15.1.2 Substrate Spectrum Engineering.- 15.1.3 Purpose.- 15.2 Methods for Metabolic Analysis.- 15.2.1 Introduction.- 15.2.2 Metabolite Pool Determination.- 15.2.2.1 Invasive Approaches.- 15.2.2.2 In vivo Techniques.- 15.2.2.3 Rapid Sampling.- 15.2.3 Metabolic Flux Analysis.- 15.2.3.1 Basic Carbon Balancing.- 15.2.3.2 Metabolite Balancing.- 15.2.3.3 Stable Isotope Labeling.- 15.2.3.4 NMR Magnetization Transfer.- 15.2.4 Metabolic Modeling.- 15.3 Metabolic Analysis of Zymomonas mobilis.- 15.3.1 Introduction.- 15.3.2 Enzymatic Studies.- 15.3.3 Metabolite Pool Measurements.- 15.3.3.1 Overview.- 15.3.3.2 Glycolytic Intermediates.- 15.3.3.3 Sugars.- 15.3.3.4 Ethanol.- 15.3.4 Flux Analyses.- 15.3.4.1 Overview.- 15.3.4.2 Metabolite Balancing.- 15.3.4.3 NMR and Stable Isotope Labeling.- 15.3.5 Summary.- 15.4 Concluding Remarks.- References.- 16 Metabolic Flux Analysis ofCorynebacterium glutamicum.- 16.1 Introduction.- 16.2 Fundamentals of Intracellular Metabolic Flux Analysis in Corynebacterium glutamicum.- 16.2.1 Metabolite Balancing.- 16.2.1.1 Biomass Composition.- 16.2.1.2 Condensed Bioreaction Network.- 16.2.1.3 Approaches to Resolve Network Underdeterminacy.- 16.2.1.4 Theoretical Lysine Selectivity.- 16.2.1.5 Limitations.- 16.2.2 Isotopic Labeling Combined with NMR Spectroscopy.- 16.2.2.1 Isotopic Atom Balancing.- 16.2.2.2 Resolving Glycolysis and Pentose Phosphate Pathway.- 16.2.2.3 Resolving the Parallel Lysine Biosynthetic Pathways.- 16.2.2.4 Resolving Anaplerosis, Citric Acid Cycle, and the Glyoxylate Shunt.- 16.2.2.5 Resolving the Principal Ammonium-Assimilatory Pathways.- 16.2.2.6 Influence of Reaction Reversibility.- 16.2.2.7 Isotopomers.- 16.2.2.8 Sources of Isotopic Measurement Data.- 16.2.2.9 A Comprehensive Modeling Framework.- 16.3 Metabolite Balancing Studies.- 16.3.1 Overview.- 16.3.2 Comparison of Fluxes During Growth and Lysine Production.- 16.3.3 The Search for Yield-Limiting Flux Control Architectures.- 16.3.3.1 The Pyruvate Branch Point.- 16.3.3.2 The Glucose-6-Phosphate Branch Point.- 16.3.4 Growth Rate-Dependent Modulation of the Central Metabolic Fluxes.- 16.3.4.1 Growth on Lactate.- 16.3.4.2 Growth on Glucose.- 16.3.5 Summary.- 16.4 Studies Based on Isotopic Labeling and NMR.- 16.4.1 Overview.- 16.4.2 The Dual Pathways of Lysine Biosynthesis.- 16.4.2.1 Correlation with Lysine Production.- 16.4.2.2 Correlation with Culture Parameters.- 16.4.3 Distinct Metabolic Modes: Growth, Glutamate Production, and Lysine Production.- 16.4.3.1 Comparing Isogenic Strains in Continuous Cultures.- 16.4.3.2 Comparing Different Strains in Batch Cultures.- 16.4.4 Perturbations of the Redox Metabolism.- 16.4.5 The Ammonium-Assimilating Fluxes.- 16.4.6 Summary.- 16.5 Concluding Remarks.- References.- 17 Analysis of Metabolic Fluxes in Mammalian Cells.- 17.1 Applications of Metabolic Flux Analysis in Mammalian Cells.- 17.1.1 Optimization of Protein Production.- 17.1.2 Metabolic Regulation in Transformed Cells.- 17.1.3 Metabolic Regulation in Non-Transformed Cells.- 17.2 Experimental Techniques.- 17.2.1 Direct Measurement of Extracellular Production and Consumption Rates.- 17.2.1.1 Continuous Suspension Culture.- 17.2.1.2 Perfused Culture.- 17.2.1.3 Batch Culture.- 17.2.2 Detection of Isotope Distribution by 13C-NMR.- 17.2.2.1 Measuring Fractional Enrichments.- 17.2.2.2 Measuring Isotopomer Fractions.- 17.2.2.3 In Vivo NMR.- 17.2.2.4 Extraction NMR.- 17.2.3 Radio-Isotope Tracer Studies and Enzyme Activity Assays.- 17.3. Mathematical Descriptions to Quantify Fluxes in Metabolic Models.- 17.3.1 Determining Fluxes Using Cometabolite Measurements.- 17.3.1.1 Solution of the Stoichiometric Matrix.- 17.3.1.2 The Objective Function.- 17.3.2 General Principles of Isotope Balancing.- 17.3.2.1 Steady State Flux Analysis.- 17.3.2.2 The Isotope Balance.- 17.3.3 Least Squares Fitting of the Algebraic Form.- 17.3.4 Atom Mapping/Transition Matrices.- 17.3.5 Isotopomer Mapping Matrices.- 17.3.6 Transient NMR Measurement.- 17.3.7 Errors in the Determination of Fluxes.- 17.3.7.1 Errors in Linear Models.- 17.3.7.2 Errors in Non-linear Models.- 17.4 Biochemical Pathway Model Formulation and Reduction.- 17.4.1 Reduction of Comprehensive Models.- 17.4.2 Pathway Inclusion and Reduction Assumptions.- 17.5 Observed Metabolic Flux Patterns in Mammalian Cells.- 17.5.1 Linkage of Glycolysis to the Tricarboxylic Acid Cycle.- 17.5.2 Reducing Equivalents.- 17.5.3 Glutaminolysis.- 17.5.4 Pyruvate Carboxylase.- 17.5.5 Pentose Phosphate Pathway.- 17.5.6 Tumors as Nitrogen Sinks.- 17.5.7 Oxidative Glycolysis in the Rat Brain.- 17.6 Specific Uses of Flux Pattern Information.- References.