Cover
Preface
Part I: Overview of the Control and Monitoring of Bioprocesses and Mathematical Preliminaries
1. 1 Introduction
  1. 1.1 Overview of the Control and Monitoring of Bioprocesses
  2. 1.2 Improvements to Bioprocesses Productivity
  3. 1.3 Bioprocess Control
  4. 1.4 Process Measurements
  5. 1.5 Dynamic Bioprocess Models
  6. 1.6 Process Optimization
  7. References
2. 2 Mathematical Preliminaries
  1. 2.1 Systems of Ordinary Differential Equations
  2. 2.2 Linear Systems
  3. 2.3 Nonlinear Dynamical Systems and its Analysis
  4. 2.4 Stability Theory via Lyapunov Approach
  5. 2.5 Bifurcation Theory
  6. 2.6 Overview of Non-Smooth Dynamical Systems
  7. References
Part II: Observability and Control Concepts
1. 3 State Estimation and Observers
  1. 3.1 Observability
  2. 3.2 Observer Designs for Linear Structures
  3. 3.3 Observer Designs for Nonlinear Structures
  4. References
2. 4 Control of Bioprocess
  1. 4.1 The Control Idea
  2. 4.2 Controllers for Linear Systems
  3. 4.3 Nonlinear Controllers
  4. References
Part III: Software Sensors and Observer-Based Control Schemes for Bioprocess
1. 5 Dynamical Behavior of a 3-Dimensional Continuous Bioreactor
  1. 5.1 Introduction
  2. 5.2 Bioreactor Modeling
  3. 5.3 Main Results
  4. 5.4 Concluding Remarks
  5. References
2. 6 Observability Analysis Applied to 2D and 3D Bioreactors with Inhibitory and Non-inhibitory Kinetics Models
  1. 6.1 Introduction
  2. 6.2 Materials and Methods
  3. 6.3 Results and Discussion
  4. 6.4 Implementation of a Linear Observer to Check the Results of the Observability Analysis
  5. 6.5 Conclusion
  6. References
3. 7 Production System Myco-Diesel for Implementation of “Quality” of the Observability
  1. 7.1 Introduction
  2. 7.2 Methodology
  3. 7.3 Main Results
  4. 7.4 Conclusions
  5. References
4. 8 Regulation of a Continuously Stirred Bioreactor via Modeling Error Compensation
  1. 8.1 Introduction
  2. 8.2 Materials and Methods
  3. 8.3 Input–Output Identified Model
  4. 8.4 Control Design
  5. 8.5 Main Results
  6. 8.6 Concluding Remarks
  7. References
5. 9 Development of Virtual Sensor Based on the Just-In-Time Model for Monitoring of Biological Control Systems
  1. 9.1 Introduction
  2. 9.2 Materials and Methods
  3. 9.3 On-line Monitoring (Proposed Nonlinear Observer)
  4. 9.4 Such Approaches, Known as Proposed Just-in-Time Modeling “Hybrid Systems”
  5. 9.5 Results
  6. 9.6 Conclusions
  7. References
6. 10 Virtual Sensor Design for State Estimation in a Photocatalytic Bioreactor for Hydrogen Production
  1. 10.1 Introduction
  2. 10.2 Material and Methods
  3. 10.3 Mathematical Model Development
  4. 10.4 Virtual Sensor Design
  5. 10.5 Results and Discussion
  6. 10.6 Conclusions
  7. References
Index
End User License Agreement

List of Tables

Chapter 1
1. Table 1.1 Bioprocess based sustainable production [34].
2. Table 1.2 The most common requirements for microorganism's growth [45].
3. Table 1.3 Most bioprocesses consist of some general phases that are conducted...
4. Table 1.4 Summary: model controllers applied in bioprocesses [59].
5. Table 1.5 Feedback control strategies with focus on the monitoring technique ...
6. Table 1.6 Advantages and disadvantages of different methods for software sens...
7. Table 1.7 Kinetic models of inhibition.
8. Table 1.8 Kinetic models of inhibition by product (the expression of the comp...
9. Table 1.9 Modified kinetic models.
Chapter 2
1. Table 2.1 Review of the basic theorem of Lyapunov [27].
2. Table 2.2 Properties for the Lyapunov theorem.
3. Table 2.3 Local stability R² [49].
4. Table 2.4 Parameters used in modeling and their meaning.
Chapter 4
1. Table 4.1 Review and applications of the properties the controllability and o...
2. Table 4.2 Control techniques actually applied to real bioprocesses [29].
Chapter 5
1. Table 5.1 Kinetic rates and coefficient yields definitions.
2. Table 5.2 Kinetic models for substrate and product inhibition.
3. Table 5.3 Kinetic parameters estimated forDesulfovibrio alaskensis 6SR.
4. Table 5.4 Correlation coefficients R².
5. Table 5.5 Variability of the internal dynamics of each model with respect to ...
Chapter 6
1. Table 6.1 Kinetic models of inhibition.
2. Table 6.2 Overall results of the observability analysis applied to 2D and 3D ...
Chapter 7
1. Table 7.1 Kinetic parameters forMortierella isabellina growth.
Chapter 8
1. Table 8.1 Parameters of the proposed model.
2. Table 8.2 Correlation coefficients
Chapter 9
1. Table 9.1 Parameters of model proposed.
2. Table 9.2 Correlation coefficients.
Chapter 10
1. Table 10.1 Procedure for the adjustment of kinetic parameters of the proposed...
2. Table 10.2 Hydrogen productivities reported in literature works.
3. Table 10.3 Kinetic parameters for the mathematical model.
4. Table 10.4 Correlation coefficients and model efficiency.
5. Table 10.5 Local properties: stability and observability.

List of Illustrations

Chapter 1
1. Figure 1.1 Opportunities for control by topics [3–12].
2. Figure 1.2 Comparison of: (a) experimental sulfate, (b) experimental biomass...
3. Figure 1.3 Comparison of: (a) experimental lactate, and (b) experimental ace...
4. Figure 1.4 Comparison of experimental Cd²⁺ with predictions of the model...
5. Figure 1.5 Simple metabolic model with feedback.
6. Figure 1.6 Temporal evolution of the variables described by Eqs. (1.7 and 1....
7. Figure 1.7 The figure shows phase-plane, we see that the trajectories are at...
8. Figure 1.8 Temporal evolution of the variables described by Eqs. (1.7 and 1....
9. Figure 1.9 The figure shows Phase-plane, we see that the trajectories are at...
10. Figure 1.10 Temporal evolution of the variables concentration of the system ...
11. Figure 1.11 The figure shows a phase-plane, we see that the trajectories are...
12. Figure 1.12 Schematic representing the current improvements to bioprocesses ...
13. Figure 1.13 Bioprocess operations [43].
14. Figure 1.14 Illustrative example for bioprocess monitoring and control.
15. Figure 1.15 Efficient fermentation process.
16. Figure 1.16 Concept of stability.
17. Figure 1.17 Scheme of stability and unstable of equilibrium.
18. Figure 1.18 Overall scheme of bioprocesses and basic control structures.
19. Figure 1.19 On-line measurements for monitoring bioprocess [83–89].
20. Figure 1.20 Balance equation based methods.
21. Figure 1.21 Observer based on mathematical model.
22. Figure 1.22 Kalman filter.
23. Figure 1.23 Neural networks.
24. Figure 1.24 Fuzzy reasoning.
25. Figure 1.25 Conceptual scheme explaining the principle of a bioreactor.
26. Figure 1.26 Comparison of experimental data (symbol) and predictions of the ...
27. Figure 1.27 Ethanol concentration, and productivity in continuous processes.
Chapter 2
1. Figure 2.1 Schematic diagram of the activated-sludge process.
2. Figure 2.2 Temporal evolution of the variable's concentration described by E...
3. Figure 2.3 Phase diagram of the open-loop system of output variables, the ef...
4. Figure 2.4 Dynamical system.
5. Figure 2.5 Showing initial condition ( x0, y0) enclosed in neighborhoods R and...
6. Figure 2.6 Geometry stability concept.
7. Figure 2.7 Stability Lyapunov.
8. Figure 2.8 The Goodwin oscillator. Phase portrait showing convergence to a l...
9. Figure 2.9 Dynamics evolution of the concentrations described by Eqs. (2.98)...
Chapter 3
1. Figure 3.1 A general estimation scheme (x: state variables, π: parameters, y
Chapter 4
1. Figure 4.1 The control idea in bioprocess.
2. Figure 4.2 Schematic of the nonlinear PI controller.
3. Figure 4.3 Stabilization by state feedback.
Chapter 5
1. Figure 5.1 Open-loop and closed-loop dynamics of the four proposed model for...
2. Figure 5.2 Open-loop and closed-loop dynamics of the four proposed model for...
3. Figure 5.3 Open-loop and closed-loop dynamics of the four proposed model for...
4. Figure 5.4 Open-loop and closed-loop dynamics of the four proposed model vs ...
5. Figure 5.5 Open-loop and closed-loop dynamics for performance index.
6. Figure 5.6 Open-loop and closed-loop dynamics of the four proposed model in ...
7. Figure 5.7 The zero dynamics performance of the uncontrolled states.
Chapter 6
1. Figure 6.1 Results of (symbols) actual values, (lines) Luenberger observer. ...
2. Figure 6.2 Results of (symbols) actual values, (lines) Luenberger observer. ...
Chapter 7
1. Figure 7.1 Bioreactor scheme for continuous culture system.
2. Figure 7.2 Bifurcation diagram of the four state variables: hydrolyzed sugar...
3. Figure 7.3 Bifurcation diagram of the sugar consumption (PS) and lipid produ...
4. Figure 7.4 Variations in the singular values of the observability matrix wit...
5. Figure 7.5 Condition number for the observability matrix (logarithmic scale)...
6. Figure 7.6 Simulation of a continuous bioreactor that produces mycodiesel an...
7. Figure 7.7 Performance index (ITSE) of the observer for the nitrogen concent...
Chapter 8
1. Figure 8.1 Dynamics of biomass, sulfate and sulfide concentrations.
2. Figure 8.2 Dynamics of acetate, lactate and biomass concentrations.
3. Figure 8.3 Open-loop and closed-loop dynamics of acetate, lactate, and sulfa...
4. Figure 8.4 Open-loop and closed-loop dynamics of sulfide and biomass.
5. Figure 8.5 Control effort.
6. Figure 8.6 Performance index.
Chapter 9
1. Figure 9.1 Virtual sensor based on JIT model for monitoring of biological co...
2. Figure 9.2 Software sensor coupled to the JIT approach in real time.
3. Figure 9.3 Simulation of mycoparasitism. Dynamic behavior of the experimenta...
4. Figure 9.4 Simulation of mycoparasitism. Dynamic behavior of the experimenta...
5. Figure 9.5 Simulation of mycoparasitism. Dynamic behavior of the experimenta...
6. Figure 9.6 Simulation of mycoparasitism. Dynamic behavior of the experimenta...
7. Figure 9.7 Simulation of mycoparasitism. Dynamic behavior of the experimenta...
8. Figure 9.8 Simulation of mycoparasitism. Dynamic behavior of the experimenta...
9. Figure 9.9 Simulation of mycoparasitism. Dynamic behavior of the experimenta...
10. Figure 9.10 The integral time absolute error. Numerical simulations (extende...
Chapter 10
1. Figure 10.1 The bio-hydrogen production in microorganism “strategies”.
2. Figure 10.2 Conceptual model.
3. Figure 10.3 Schematic representation of virtual sensor design.
4. Figure 10.4 Sulfate reduction and cadmium removal, the comparison between ba...
5. Figure 10.5 Biomass, acetate, cadmium sulfide and carbon dioxide, the compar...
6. Figure 10.6 Hydrogen production, the comparison between batch experimental d...
7. Figure 10.7 Sulfate reduction and cadmium removal, a comparison between batc...
8. Figure 10.8 Biomass, acetate, cadmium sulfide and carbon dioxide, a comparis...
9. Figure 10.9 Hydrogen production, a comparison between batch experimental dat...

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Cover Design: Wiley

Preface

The purpose of this book is to present the different approaches most commonly employed in the control of bioprocesses. It aims to develop in some detail the bases and concepts of bioprocesses related to the control theory introduced in basic principles of mathematical modeling in bioprocesses. From this viewpoint, the systems approach to bioengineering and bioprocessing, with its current focus on the development of mathematical models and their analysis, is a logical sequel that the control theory that will play a relevant role in understanding the mechanisms of cellular and metabolic processes. It concerns specifically applications in modeling, estimation, and control of bioprocesses. Consequently, this book presents key results in various fields, including: dynamic modeling, dynamic properties of bioprocess models, software sensors designed for the online estimation of parameters and state variables, and control and supervision of bioprocesses. The book is divided into three sections.

Part I: Overview of the Control and Monitoring of Bioprocesses and Mathematical Preliminaries, contains Chapters 1 and 2. Chapter 1 is a general overview of the control and monitoring of biotechnological processes. Chapter 2 introduces the mathematical framework necessary for the analysis and characterization of bioprocess dynamics. In other words, Chapter 2 deals with the mathematical approach we follow to describe the evolution in time of the bioprocess under consideration. Therefore, understanding and formalizing the role of nonlinearity is indeed one of the greatest challenges in the study of living systems, mainly in bioprocesses. In engineering practice two of the most important sources of modeling error are the presence of nonlinearities in the system and a lack of exact knowledge of some of the system parameters, therefore, it is necessary to describe the properties of the state of the dynamics of the nominal model under the theory of systems.

Part II: Observability and control concepts, contains Chapter 3 on state estimation and observers and Chapter 4 focusses on control of bioprocess. Chapter 3 introduces the reader to the observability concepts that are the basis to designing online estimation algorithms (software sensor) for bioprocess. The observability conditions for bioprocess models from local linearization, differential geometric and algebraic differential approaches are established. Chapter 4 reviews the controllability concepts that are the basis for designing automatic feedback control schemes for bioprocesses. A clear explanation is developed from the classical linear schemes to advanced robust algorithms with application in bioprocess systems. However, performance may degrade when they are applied to highly nonlinear processes, which are the fact rather than the exception in the chemical and biochemical process industry.

Part III: Software sensors and observer-based control schemes for bioprocess. This last part deals with application cases in Chapters 5–10. Chapter 5 covers the dynamical behavior of a three-dimensional continuous bioreactor. The dynamic behavior of a two-dimensional model of a continuous bioreactor was studied in this chapter. The objective of the analysis under the control of feedback allows the most suitable regions to be found and to model where the best performance of the bioreactor operates. Chapter 6 reviews observability analysis applied to 2D and 3D bioreactors with inhibitory and non-inhibitory kinetics models. The results indicate that the proposed model can be applied for simulation in different conditions of operation for possible instrumentation, estimation and control from laboratory scale up to semi-pilot scale. Chapter 7 introduces the production system myco-diesel for implementation of "quality" of the observability. Myco-diesel is a new alternative and, compared to traditional fossil fuels, an environmentally sustainable biofuel source due to reduced gas emission. It is made from renewable bioprocess sources such as vegetable oils, animal fats, and microorganism culture. Chapter 8 is about the regulation of continuously stirred bioreactors via modeling error compensation. The aim of this chapter is to control the nonlinear behavior of a class of continuously stirred bioreactors with regulation purposes. From the above, a linearized representation of the state space bioreactor's model is obtained via standard identification processes employing a step disturbance in the control input. Chapter 9 reviews the development of virtual sensors based on the just-in-time model for monitoring of biological control systems. Real-time monitoring of physiological characteristics during a cultivation process is of great importance in bioprocesses. Biological control involves the use of beneficial organisms for metabolite production that reduces the negative effects of plant pathogens disease suppression. Finally, chapter 10 discusses virtual sensor design for state estimation in a photocatalytic bioreactor for hydrogen production. This chapter is focused on the design of a virtual sensor for a class of continuous bioreactors to estimate the production of hydrogen. The proposed mathematical model is suitable for predicting concentrations of biomass, acetate, cadmium in liquid, sulfate, lactate, carbon dioxide, sulfide, and cadmium sulfide, as well as hydrogen production.

Bioprocess modeling and control still offers interesting perspectives to obtain automatization solutions for the aerobic and anaerobic bioprocesses.

Control in Bioprocessing

Modeling, Estimation and the Use of Soft Sensors

Preface

Part I
Overview of the Control and Monitoring of Bioprocesses and Mathematical Preliminaries