
Dynamical Modelling & Estimation in Wastewater Treatment Processes
Description
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
More details
Other editions
Additional editions

Content
- Cover
- Copyright
- Contents
- Preface
- Dedication
- 1. Dynamical Modelling
- 1.1 Introduction
- 1.2 Classification of Mathematical Models
- 1.2.1 Model Constituents
- 1.2.2 Model Attributes
- 1.3 The Model Building Exercise
- 1.4 Optimal Experiment Design: Basic Ideas
- 1.5 Conclusion
- 2. Dynamical Mass Balance Model Building and Analysis
- 2.1 Introduction
- 2.2 The Notion of Mass Balances
- 2.2.1 Stirred Tank Reactors
- 2.2.2 A Simple Biological Reactor Model
- 2.2.3 Biomass Death and Substrate Maintenance
- 2.2.4 (Liquid and Gaseous) Product Dynamics
- 2.2.5 Oxygen Dynamics
- 2.2.6 Models of Reaction Rates
- 2.3 Examples of Biological Wastewater Treatment Process Models
- 2.3.1 Anaerobic Digestion: The 4 Population Model
- 2.3.2 Activated Sludge Process: The Basic Model
- 2.3.3 Activated Sludge Process: The IWA Activated Sludge Model No. 1
- 2.3.4 Two Step Nitrification
- 2.3.5 Two Step Denitrification
- 2.4 General Dynamical Model of Stirred Tank Reactors
- 2.4.1 The General Dynamical Model
- 2.4.2 Examples (Continued)
- 2.5 Multi Tank and Non Completely Mixed Reactors
- 2.5.1 Sequential Reactors
- 2.5.2 Fixed Bed Reactor: The Basic Mass Balance Model
- 2.5.3 General Dynamical Model of Fixed and Fluidised Bed Reactors
- 2.5.4 Settlers
- 2.6 Linear vs Nonlinear Models
- 2.7 Equilibrium Points, Linearisation and Stability Analysis
- 2.7.1 First Order Kinetics
- 2.7.2 Monod and Haldane Kinetics
- 2.7.3 Linearised Tangent Model of Nonlinear Models
- 2.7.4 Stability of Equilibrium Points
- 2.8 A Key State Transformation
- 2.8.1 Definition of the State Transformation
- 2.8.2 Example 1: Two Step Denitrification
- 2.8.3 Example 2: Activated Sludge Process: The Basic Model
- 2.8.4 Example 3: Activated Sludge Process: The IWA Activated Sludge Model No. 1
- 2.8.5 Example 4: Fixed Bed Reactor Model with a Growth Reaction and a Death/Detachment Reaction
- 2.9 Model Order Reduction
- 2.9.1 Singular Perturbation Technique for Low Solubility Products
- 2.9.2 Singular Perturbation Technique for Substrates of Fast Reactions
- 2.9.3 A General Rule for Order Reduction
- 2.9.4 Example: the Anaerobic Digestion
- 2.9.5 Specific Approach for Model Reduction of PDE Models
- 2.10 Connection Between Plug Flow Reactors and CSTRs: A Laplace Transform Approach
- 2.11 Conclusions
- 3. Structure Characterisation (SC)
- 3.1 Introduction
- 3.2 A Model Case Study
- 3.3 Structure Characterisation Methods
- 3.3.1 A Priori SC
- 3.3.2 A Posteriori SC
- 3.4 Optimal Experiment Design for Structure Characterisation
- 3.4.1 Theoretical Background of OED/SC
- 3.4.2 Application: Real-time OED/SC in a Respirometer
- 3.5 Conclusions
- 4. Structural Identifiability
- 4.1 Introduction
- 4.2 Theoretical Framework
- 4.3 Notion of Structural Identifiability of Linear Systems
- 4.3.1 A Simple Example
- 4.3.2 The Laplace Method
- 4.3.3 Some Generalisations and Definitions
- 4.3.4 A Second-Order Example: The Two Interconnected CSTRs Model
- 4.4 Methods for Testing Structural Identifiability of Nonlinear Systems
- 4.4.1 Taylor Series Expansion
- 4.4.2 Generating Series
- 4.4.3 Local State Isomorphism
- 4.4.4 Transformation of Nonlinear Models
- 4.4.5 A Simple Example
- 4.5 The Lyapunov-Based Method: An Historical Perspective with the Monod Model
- 4.6 Example #2: Respirometer-based Models
- 4.6.1 Identifiability of the First Order Kinetics Model (Laplace Transform)
- 4.6.2 Identifiability of the Single Monod Model (Taylor Series Expansion)
- 4.6.3 Identifiability of the Double Monod Model (Nonlinear Transformation)
- 4.6.4 Identifiability of a Modified ASM1 Model (Nonlinear Transformation and Generating Series)
- 4.6.5 Summary of the Results and Discussion
- 4.7 General Structural Identifiability Results for the ASM-type Models
- 4.8 Overparametrisation: An Illustrative Example
- 4.9 Conclusions
- 5. Practical Identifiability and Optimal Experiment Design for Parameter Estimation (OED/PE)
- 5.1 Introduction
- 5.2 Practical Identifiability
- 5.2.1 Theoretical Framework
- 5.2.2 Confidence Region of the Parameter Estimates
- 5.2.3 Sensitivity Functions
- 5.3 Optimal Experiment Design for Parameter Estimation (OED/PE)
- 5.3.1 Theoretical Background of OED/PE
- 5.4 Examples of OED/PE
- 5.5 Application: Real-time OED/PE in a Respirometer
- 5.5.1 Degrees of Freedom and Constraints for OED/PE
- 5.5.2 OED/PE for the Single Monod Model
- 5.5.3 Experimental Validation of OED/PE for the Single Monod Model
- 5.6 Optimal Experiment Design for the Dual Problem of Structure Characterisation and Parameter Estimation
- 5.7 Conclusions
- 6. Estimation of Model Parameters
- 6.1 Introduction
- 6.2 Introductory Examples
- 6.2.1 Example 1: Estimating the Mean of a Data Set
- 6.2.2 Example 2: A Simple Nonlinear Parameter Estimation Problem
- 6.3 Preliminary Steps in Parameter Estimation
- 6.3.1 Selecting Data Subsets for Estimation and Validation
- 6.3.2 Selection of Parameters to be Estimated
- 6.3.3 Differentiating Linear and Nonlinear Parameters
- 6.3.4 Use and Misuse of Linearised Forms of Nonlinear Models
- 6.3.5 Reparameterisation of Models with Nonlinear Parameters
- 6.3.6 Initial Estimates of the Parameters
- 6.3.7 Inequality Constraints on the Parameters
- 6.4 Error Characteristics
- 6.4.1 Measurement Errors and Residuals
- 6.4.2 Autocorrelated Residuals
- 6.4.3 Estimation of the Measurement Error Covariance Matrix
- 6.4.4 Errors-in-Variables Problems
- 6.5 Objectives in Parameter Estimation: Estimators
- 6.5.1 Maximum Likelihood Estimation
- 6.5.2 Weighted Least Squares (WLS) or ?2 Estimation
- 6.5.3 Ordinary Least Squares (OLS) Estimation
- 6.5.4 Bayesian Estimation
- 6.5.5 Robust Estimation
- 6.5.6 Alternative Objective Functions
- 6.5.7 Selecting a Set of Feasible Parameters
- 6.5.8 Multi-Objective Functions
- 6.5.9 Multivariate Estimation
- 6.5.10 Multiresponse Estimation
- 6.6 Minimisation Approach
- 6.6.1 Linear Parameter Estimation
- 6.6.2 Nonlinear Parameter Estimation
- 6.6.3 Local Minimisation Algorithms Using Derivative Information
- 6.6.4 Derivative-Free Local Minimisation Algorithms
- 6.6.5 Global Minimisation Algorithms
- 6.6.6 Comparison of Different Minimisation Algorithms
- 6.7 Evaluation of Parameter Estimation Quality
- 6.7.1 Residuals Analysis
- 6.7.2 Parameter Estimation Error Covariance Matrix Determination
- 6.7.3 Confidence Regions
- 6.7.4 Significance of a Single Parameter
- 6.7.5 Significance of the Parameter Set
- 6.7.6 Correlation Among Parameters
- 6.8 Conclusions
- 7. Recursive State and Parameter Estimation
- 7.1 Introduction
- 7.2 State Observability
- 7.2.1 Example #1: Two Step Nitrification Process
- 7.2.2 Simple Local Observability Tests
- 7.2.3 Example #1: Two Step Nitrification Process (Continued)
- 7.2.4 Example #2: Simple Microbial Growth Process
- 7.3 Classical Observers
- 7.3.1 The Basic Structure of a State Observer
- 7.3.2 Extended Luenberger Observer
- 7.3.3 Extended Kalman Observer
- 7.3.4 Application to the General Dynamical Model
- 7.3.5 Performance of Classical Observers in Presence of Model Uncertainties
- 7.4 Asymptotic Observers
- 7.4.1 Asymptotic Observers for Single Tank Bioprocesses
- 7.4.2 Asymptotic Observers for Multi-Tank Reactors
- 7.4.3 Asymptotic Observers for Tubular Bioreactors
- 7.4.4 Asymptotic Observers as a Tool for Model Selection
- 7.5 Observers for Processes with Badly Known Kinetics
- 7.5.1 State Observer with the Unknown Parameters as Design Parameters
- 7.5.2 Adaptive State Observer
- 7.5.3 Generalisation
- 7.6 On-line Parameter Estimation
- 7.6.1 The Observer-Based Estimator
- 7.6.2 The Recursive Least Squares Estimator
- 7.7 Conclusions
- Appendix A: Glossary
- A.1 Model Constituents
- A.2 Model Attributes
- A.3 Terms of Model Building
- Appendix B: Nomenclature
- B.1 Greek Letters
- B.2 Indices
- B.3 Abbreviations
- References
System requirements
File format: PDF
Copy-Protection: Adobe-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Install the free reader Adobe Digital Editions prior to download (see eBook Help).
- Tablet/smartphone (Android; iOS): Install the free app Adobe Digital Editions or the app PocketBook before downloading (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (only limited: Kindle).
The file format PDF always displays a book page identically on any hardware. This makes PDF suitable for complex layouts such as those used in textbooks and reference books (images, tables, columns, footnotes). Unfortunately, on the small screens of e-readers or smartphones, PDFs are rather annoying, requiring too much scrolling.
This eBook uses Adobe-DRM, a „hard” copy protection. If the necessary requirements are not met, unfortunately you will not be able to open the eBook. You will therefore need to prepare your reading hardware before downloading.
Please note: We strongly recommend that you authorise using your personal Adobe ID after installation of any reading software.
For more information, see our eBook Help page.