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Table of contents
1 MBR Fundamentals
1.1 The History of Membranes inWater Treatment Processes
1.1.1 Definitions and Descriptions
1.1.1.1 The Membrane Classification
1.1.2 Membrane Process Configurations
1.2 MBR Market
1.3 Conventional Activated Sludge vs. sMBR
1.3.1 sMBR Advantages
1.3.2 sMBR Drawbacks
1.4 Fouling Formation
1.4.1 Backwash, Relaxation and Chemical Cleaning
2 Modeling sMBR
2.1 MathematicalModeling of Biological Systems
2.2 MathematicalModel Types
2.2.1 Black-box Models
2.2.2 White-box Models
2.2.3 Gray-box Models
2.3 Motivation for Modeling sMBRs
2.4 sMBR Model Types
2.4.1 sMBR Biological Modeling
2.4.2 sMBR Physical Modeling
2.4.3 sMBR Integrated Modeling
2.5 Proposed Simple Integrated sMBR Model
3 Analysis, Parameter Identification & Simulation
3.1 Introduction
3.2 Full Model Analysis
3.2.1 Fast and Slow Dynamics
3.2.1.1 Singular Perturbations
3.2.1.2 Three-time-scale Singular Perturbation
3.2.2 Asymptotic Analysis
3.2.3 Study of the Linearized Dynamics – Short-term
3.2.4 Observability
3.2.4.1 SL2H applied to sMBR Model
3.2.5 Controllability
3.2.5.1 Lie Brackets Applied to sMBR Model
3.3 Biological Aspect Simulations and Analysis
3.3.1 Global Stability Study
3.4 Physical Aspect Simulation and Analysis
3.5 Model Simulation and Parameter Identification
4 Model Validation with Experimental Data 96
4.1 Recir culating Aquaculture System Fitted with sMBR Pilot Plant Design
4.1.1 Process Description
4.1.2 Experimental Setup
4.1.3 Data Logging and Instrumentation
4.1.4 Recirculating Aquaculture System and sMBR
4.1.5 Critical Flux
4.1.6 Air Cross-Flow Study
4.1.7 Model Identification and Cross-validation
4.1.7.1 Ultra-fast Dynamic Identification
4.1.7.2 Fast Dynamic Identification
4.1.7.3 Slow Dynamic Identification
4.1.7.4 Cross-validation
4.2 Wastewater Treatment Pilot Plant1
4.2.1 Pilot Plant Description
4.2.2 Recorded Data
4.2.3 TMP Long-term Exponential-like Behavior
4.2.4 Permeate and TMP Amplitude
4.2.5 Relaxation
4.2.6 Temperature Influence
4.2.7 Air-blowers Temperature
4.2.8 Model Identification and Cross-validation
4.2.8.1 Short-term Identification
4.2.8.2 Long-term Identification
4.2.8.3 Cross-validation
4.3 Analysis of the Identified Parameters
5 Control Strategies for sMBRs
5.1 NMPC to a WWTP fitted with an sMBR
5.1.1 NMPC-sMBR Process Control
5.1.2 Simulation Results
5.2 Partial state-feedback linearizing control based on a quadratic control-Lyapunov function
5.2.1 Formula for Feedback
5.2.1.1 Control-Lyapunov Function
5.2.1.2 Partial state-feedback linearization
5.2.2 Stabilizing Sludge Cake Mass
6 Conclusions & Directions for Further Research
