Dynamic Modeling and Predictive Control in Solid Oxide Fuel Cells - Info and Reading Options

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The high temperature solid oxide fuel cell (SOFC) is identified as one of the leading fuel cell technology contenders to capture the energy market in years to come. However, in order to operate as an efficient energy generating system, the SOFC requires an appropriate control system which in turn requires a detailed modelling of process dynamics. Introducting state-of-the-art dynamic modelling, estimation, and control of SOFC systems, this book presents original modelling methods and brand new results as developed by the authors. With comprehensive coverage and bringing together many aspects of SOFC technology, it considers dynamic modelling through first-principles and data-based approaches, and considers all aspects of control, including modelling, system identification, state estimation, conventional and advanced control. Key features: Discusses both planar and tubular SOFC, and detailed and simplified dynamic modelling for SOFC Systematically describes single model and distributed models from cell level to system level Provides parameters for all models developed for easy reference and reproducing of the results All theories are illustrated through vivid fuel cell application examples, such as state-of-the-art unscented Kalman filter, model predictive control, and system identification techniques to SOFC systems The tutorial approach makes it perfect for learning the fundamentals of chemical engineering, system identification, state estimation and process control. It is suitable for graduate students in chemical, mechanical, power, and electrical engineering, especially those in process control, process systems engineering, control systems, or fuel cells. It will also aid researchers who need a reminder of the basics as well as an overview of current techniques in the dynamic modelling and control of SOFC.

Open Data:

Intro -- Title Page -- Copyright -- Preface -- Acknowledgments -- List of Figures -- List of Tables -- Chapter 1: Introduction -- 1.1 Overview of Fuel Cell Technology -- 1.2 Modelling, State Estimation and Control -- 1.3 Book Coverage -- 1.4 Book Outline -- Part One: Fundamentals -- Chapter 2: First Principle Modelling for Chemical Processes -- 2.1 Thermodynamics -- 2.2 Heat Transfer -- 2.3 Mass Transfer -- 2.4 Fluid Mechanics -- 2.5 Equations of Change -- 2.6 Chemical Reaction -- 2.7 Notes and References -- Chapter 3: System Identification I -- 3.1 Discrete-time Systems -- 3.2 Signals -- 3.3 Models -- 3.4 Notes and References -- Chapter 4: System Identification II -- 4.1 Regression Analysis -- 4.2 Prediction Error Method -- 4.3 Model Validation -- 4.4 Practical Consideration -- 4.5 Closed-loop Identification -- 4.6 Subspace Identification -- 4.7 Notes and References -- Chapter 5: State Estimation -- 5.1 Recent Developments in Filtering Techniques for Stochastic Dynamic Systems -- 5.2 Problem Formulation -- 5.3 Sequential Bayesian Inference for State Estimation -- 5.4 Examples -- 5.5 Notes and References -- Chapter 6: Model Predictive Control -- 6.1 Model Predictive Control: State-of-the-Art -- 6.2 General Principle -- 6.3 Dynamic Matrix Control -- 6.4 Nonlinear MPC -- 6.5 General Tuning Guideline of Nonlinear MPC -- 6.6 Discretisation of Models: Orthogonal Collocation Method -- 6.7 Pros and Cons of MPC -- 6.8 Optimisation -- 6.9 Example: Chaotic System -- 6.10 Notes and References -- Part Two: Tubular SOFC -- Chapter 7: Dynamic Modelling of Tubular SOFC: First-Principle Approach -- 7.1 SOFC Stack Design -- 7.2 Conversion Process -- 7.3 Diffusion Dynamics -- 7.4 Fuel Feeding Process -- 7.5 Air Feeding Process -- 7.6 SOFC Temperature -- 7.7 Final Dynamic Model -- 7.8 Investigation of Dynamic Properties through Simulations

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