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Summary
Illustrating techniques in model development, signal processing, data reconciliation, process monitoring, quality assurance, intelligent real-time process supervision, and fault detection and diagnosis, Batch Fermentation offers valuable simulation and control strategies for batch fermentation applications in the food, pharmaceutical, and chemical industries. The book provides approaches for determining optimal reference trajectories and operating conditions; estimating final product quality; modifying, adjusting, and enhancing batch process operations; and designing integrated real-time intelligent knowledge-based systems for process monitoring and fault diagnosis.
Author Biography
Ali Cinar is Professor of Chemical Engineering, Dean of the Graduate College, and Associate Vice President for Research, Illinois Institute of Technology, Chicago Satish J. Parulekar is Professor of Chemical Engineering and Associate Chair of the Department of Chemical and Environmental Engineering, Illinois Institute of Technology, Chicago Cenk Undey is Senior Research Associate, Illinois Institute of Technology, Chicago Gulnur Birol is Research Assistant Professor in the Department of Biomedical Engineering, Northwestern University, Evanston, Illinois
Table of Contents
| Preface | p. v |
| Nomenclature | p. xv |
| Introduction | p. 1 |
| Characteristics of Batch Processes | p. 4 |
| Focus Areas of the Book | p. 6 |
| Batch Process Modeling | p. 7 |
| Process Monitoring | p. 11 |
| Process Control | p. 12 |
| Fault Diagnosis | p. 13 |
| Penicillin Fermentation | p. 13 |
| Outline of the Book | p. 15 |
| Kinetics and Process Models | p. 21 |
| Introduction and Background | p. 21 |
| Mathematical Representation of Bioreactor Operation | p. 23 |
| Bioreactor Operation Modes | p. 24 |
| Batch Operation | p. 25 |
| Fed-Batch Operation | p. 26 |
| Continuous Operation | p. 26 |
| Conservation Equations for a Single Bioreactor | p. 27 |
| Conservation Equations for the Gas Phase | p. 28 |
| Conservation Equations for Cell Culture | p. 30 |
| Unstructured Kinetic Models | p. 33 |
| Rate Expressions for Cell Growth | p. 34 |
| Rate Expressions for Nutrient Uptake | p. 37 |
| Rate Expressions for Metabolite Production | p. 37 |
| Miscellaneous Remarks | p. 39 |
| Structured Kinetic Models | p. 39 |
| Morphologically Structured Models | p. 40 |
| Chemically Structured Models | p. 43 |
| Chemically and Morphologically Structured Models | p. 44 |
| Genetically Structured Models | p. 47 |
| Case Studies | p. 49 |
| An Unstructured Model for Penicillin Production | p. 49 |
| A Structured Model for Penicillin Production | p. 58 |
| Experimental Data Collection and Pretreatment | p. 67 |
| Sensors | p. 68 |
| Computer-Based Data Acquisition | p. 71 |
| Statistical Design of Experiments | p. 73 |
| Factorial Design | p. 75 |
| Fractional Factorial Design | p. 83 |
| Analysis of Data from Screening Experiments | p. 86 |
| Data Pretreatment: Outliers and Data Reconciliation | p. 89 |
| Data Reconciliation | p. 90 |
| Outlier Detection | p. 92 |
| Data Pretreatment: Signal Noise Reduction | p. 99 |
| Signal Noise Reduction Using Statistical Techniques | p. 100 |
| Wavelets and Signal Noise Reduction | p. 103 |
| Theoretical Confirmation/Stoichiometry and Energetics of Growth | p. 110 |
| Stoichiometric Balances | p. 110 |
| Thermodynamics of Cellular Growth | p. 112 |
| Methods for Linear Data-Based Model Development | p. 119 |
| Principal Components Analysis | p. 121 |
| Multivariable Regression Techniques | p. 125 |
| Stepwise Regression | p. 127 |
| Ridge Regression | p. 127 |
| Principal Components Regression | p. 128 |
| Partial Least Squares | p. 129 |
| Input-Output Modeling of Dynamic Processes | p. 131 |
| Time Series Models | p. 131 |
| State-Space Models | p. 135 |
| State Estimators | p. 142 |
| Batch Modeling with Local Model Systems | p. 152 |
| Functional Data Analysis | p. 158 |
| Multivariate Statistical Paradigms for Batch Process Modeling | p. 164 |
| Multiway Principal Component Analysis-MPCA | p. 164 |
| Multiway Partial Least Squares-MPLS | p. 165 |
| Multiblock PLS and PCA Methods for Modeling Complex Processes | p. 168 |
| Multivariate Covariates Regression | p. 173 |
| Other Three-way Techniques | p. 174 |
| Artificial Neural Networks | p. 175 |
| Structures of ANNs | p. 177 |
| ANN Applications in Fermentation Industry | p. 183 |
| Extensions of Linear Modeling Techniques to Nonlinear Model Development | p. 185 |
| Nonlinear Input-Output Models in Time Series Modeling Literature | p. 185 |
| Nonlinear PLS Models | p. 192 |
| System Science Methods for Nonlinear Model Development | p. 195 |
| Deterministic Systems and Chaos | p. 196 |
| Nonlinear Time Series Analysis | p. 215 |
| State-Space Reconstruction | p. 215 |
| Nonlinear Noise Filtering | p. 222 |
| System Classification | p. 227 |
| Model Development | p. 228 |
| Software Resources | p. 239 |
| Statistical Process Monitoring | p. 243 |
| SPM Based on Univariate Techniques | p. 245 |
| Shewhart Control Charts | p. 246 |
| Cumulative Sum (CUSUM) Charts | p. 255 |
| Moving Average Control Charts for Individual Measurements | p. 257 |
| Exponentially Weighted Moving-Average Chart | p. 260 |
| SPM of Continuous Processes with Multivariate Statistical Techniques | p. 261 |
| SPM of Continuous Processes with PCA | p. 264 |
| SPM of Continuous Processes with PLS | p. 264 |
| Data Length Equalization and Determination of Phase Landmarks in Batch Fermentation | p. 269 |
| Indicator Variable Technique | p. 271 |
| Dynamic Time Warping | p. 277 |
| Curve Registration | p. 303 |
| Multivariable Batch Processes | p. 315 |
| Reference Database of Normal Process Operation | p. 316 |
| Multivariate Charts for SPM | p. 318 |
| Multiway PCA-based SPM for Postmortem Analysis | p. 326 |
| Multiway PLS-based SPM for Postmortem Analysis | p. 331 |
| Multiway Multiblock Methods | p. 339 |
| Multiscale SPM Techniques Based on Wavelets | p. 346 |
| On-line Monitoring of Batch/Fed-Batch Fermentation Processes | p. 352 |
| MSPM Using Estimates of Trajectories | p. 353 |
| Adaptive Hierarchical PCA | p. 360 |
| Online MSPM and Quality Prediction by Preserving Variable Direction | p. 366 |
| Kalman Filters for Estimation of Final Product Quality | p. 377 |
| Monitoring of Successive Batch Runs | p. 378 |
| Process Control | p. 383 |
| Introduction | p. 383 |
| Open-Loop (Optimal) Control | p. 387 |
| Nonlinear Models of Bioreactor Dynamics | p. 387 |
| Background on Optimal Control Theory | p. 388 |
| Singular Control | p. 391 |
| Optimal Control | p. 392 |
| Case Study - Feeding Policy in Single-Cycle and Repeated Fed-Batch Operations | p. 393 |
| Forced Periodic Operations | p. 406 |
| Preliminaries on the [pi]-Criterion | p. 407 |
| Case Study - Forced Periodic Operations | p. 413 |
| Feedback Control | p. 423 |
| State-Space Representation | p. 423 |
| Multi-Loop Feedback Control | p. 424 |
| Optimal Linear-Quadratic Feedback Control | p. 434 |
| Model Predictive Control | p. 436 |
| Fault Diagnosis | p. 453 |
| Contribution Plots | p. 456 |
| Statistical Techniques for Fault Diagnosis | p. 462 |
| Statistical Discrimination and Classification | p. 462 |
| FDD with Fisher's Discriminant Analysis | p. 470 |
| FDD with Neural Networks | p. 476 |
| Statistical Techniques for Sensor Fault Detection | p. 478 |
| Model-based Fault Diagnosis Techniques | p. 481 |
| Residuals-Based FDD Methods | p. 485 |
| FDD Based on Model Parameter Estimation | p. 495 |
| FDD with Hidden Markov Models | p. 498 |
| Model-free Fault Diagnosis Techniques | p. 501 |
| Real-time Knowledge-Based Systems (RTKBS) | p. 503 |
| Real-time Supervisory KBS for Process Monitoring and FDD | p. 510 |
| Related Developments | p. 517 |
| Role of Metabolic Engineering in Process Improvement | p. 519 |
| Contributions of MFA and MCA to Modeling | p. 524 |
| Dynamic Optimization of Batch Process Operations | p. 528 |
| Integrated Supervisory KBS for On-line Process Supervision | p. 533 |
| Appendix | p. 537 |
| Bibliography | p. 539 |
| Index | p. 593 |
| Table of Contents provided by Ingram. All Rights Reserved. |
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