Preface Nomenclature

1 Introduction

1.1 Characteristics of Batch Processes

1.2 Focus Areas of the Book

1.2.1 Batch Process Modeling

1.2.2 Process Monitoring

1.2.3 Process Control

1.2.4 Fault Diagnosis

1.3 Penicillin Fermentation

1.4 Outline of the Book

2 Kinetics and Process Models

2.1 Introduction and Background

2.2 Mathematical Representation of Bioreactor Operation

2.3 Bioreactor Operation Modes

2.3.1 Batch Operation

2.3.2 Fed-Batch Operation

2.3.3 Continuous Operation

2.4 Conservation Equations for a Single Bioreactor

2.4.1 Conservation Equations for the Gas Phase

2.4.2 Conservation Equations for Cell Culture

2.5 Unstructured Kinetic Models

2.5.1 Rate Expressions for Cell Growth

2.5.2 Rate Expressions for Nutrient Uptake

2.5.3 Rate Expressions for Metabolite Production

2.5.4 Miscellaneous Remarks

2.6 Structured Kinetic Models

2.6.1 Morphologically Structured Models

2.6.2 Chemically Structured Models

2.6.3 Chemically and Morphologically Structured Models

2.6.4 Genetically Structured Models 2.7 Case Studies

2.7.1 An Unstructured Model for Penicillin Production

2.7.2 A Structured Model for Penicillin Production

Experimental Data Collection and Pretreatment

3.1 Sensors

3.2 Computer-Based Data Acquisition

3.3 Statistical Design of Experiments

3.3.1 Factorial Design

3.3.2 Fractional Factorial Design

3.3.3 Analysis of Data from Screening Experiments

3.4 Data Pretreatment: Outliers and Data Reconciliation

3.4.1 Data Reconciliation

3.4.2 Outlier Detection

3.5 Data Pretreatment: Signal Noise Reduction

3.5.1 Signal Noise Reduction Using Statistical Techniques

3.5.2 Wavelets and Signal Noise Reduction

3.6 Theoretical Confirmation/Stoichiometry and Energetics of Growth

3.6.1 Stoichiometric Balances

3.6.2 Thermodynamics of Cellular Growth

Methods for Linear Data-Based Model Development

4.1 Principal Components Analysis

4.2 Multivariable Regression Techniques

4.2.1 Stepwise Regression

4.2.2 Ridge Regression

4.2.3 Principal Components Regression

4.2.4 Partial Least Squares

4.3 Input-Output Modeling of Dynamic Processes

4.3.1 Time Series Models

4.3.2 State-Space Models

4.3.3 State Estimators

4.3.4 Batch Modeling with Local Model Systems

4.4 Functional Data Analysis

4.5 Multivariate Statistical Paradigms for Batch Process Modeling

4.5.1 Multiway Principal Component Analysis-MPCA

4.5.2 Multiway Partial Least Squares-MPLS

4.5.3 Multiblock PLS and PCA Methods for Modeling Complex Processes

4.5.4 Multivariate Covariates Regression

4.5.5 Other Three-way Techniques

4.6 Artificial Neural Networks

4.6.1 Structures of ANNs

4.6.2 ANN Applications in Fermentation Industry

4.7 Extensions of Linear Modeling Techniques to Nonlinear Model Development

4.7.1 Nonlinear Input-Output Models in Time Series Modeling Literature

4.7.2 Nonlinear PLS Models

5 System Science Methods for Nonlinear Model Development by Inang Birol

5.1 Deterministic Systems and Chaos

5.2 Nonlinear Time Series Analysis

5.2.1 State-Space Reconstruction

5.2.2 Nonlinear Noise Filtering

5.2.3 System Classification

5.3 Model Development

5.4 Software Resources

6 Statistical Process Monitoring

6.1 SPM Based on Univariate Techniques

6.1.1 Shewhart Control Charts

6.1.2 Cumulative Sum (CUSUM) Charts

6.1.3 Moving Average Control Charts for Individual Measurements

6.1.4 Exponentially Weighted Moving-Average Chart

6.2 SPM of Continuous Processes with Multivariate Statistical Techniques

6.2.1 SPM of Continuous Processes with PCA

6.2.2 SPM of Continuous Processes with PLS

6.3 Data Length Equalization and Determination of Phase Landmarks in Batch Fermentation

6.3.1 Indicator Variable Technique

6.3.2 Dynamic Time Warping

6.3.3 Curve Registration

6.4 Multivariable Batch Processes

6.4.1 Reference Database of Normal Process Operation

6.4.2 Multivariate Charts for SPM

6.4.3 Multiway PCA-based SPM for Postmortem Analysis

6.4.4 Multiway PLS-based SPM for Postmortem Analysis

6.4.5 Multiway Multiblock Methods

6.4.6 Multiscale SPM Techniques Based on Wavelets

6.5 On-line Monitoring of Batch/Fed-Batch Fermentation Processes

6.5.1 MSPM Using Estimates of Trajectories

6.5.2 Adaptive Hierarchical PCA

6.5.3 Online MSPM and Quality Prediction by Preserving Variable Direction

6.5.4 Kaiman Filters for Estimation of Final Product Quality

6.6 Monitoring of Successive Batch Runs

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