Rhinehart, R. RussellUnknown
John Wiley & Sons (Asia) Pte.Ltd (Hoboken, New Jersey, 2016) (eng) English9781118597972Wiley-ASME Press seriesUnknownTECHNOLOGY--&--ENGINEERING--REFERENCE; Includes bibliographical references and index; Since mathematical models express our understanding of how nature behaves, we use them to validate our understanding of the fundamentals about systems (which could be processes, equipment, procedures, devices, or products). Also, when validated, the model is useful for engineering applications related to diagnosis, design, and optimization.
First, we postulate a mechanism, then derive a model grounded in that mechanistic understanding. If the model does not fit the data, our understanding of the mechanism was wrong or incomplete. Patterns in the residuals can guide model improvement. Alternately, when the model fits the data, our understanding is sufficient and confidently functional for engineering applications.
This book details methods of nonlinear regression, computational algorithms,model validation, interpretation of residuals, and useful experimental design. The focus is on practical applications, with relevant methods supported by fundamental analysis.
This book will assist either the academic or industrial practitioner to properly classify the system, choose between the various available modeling options and regression objectives, design experiments to obtain data capturing critical system behaviors, fit the model parameters based on that data, and statistically characterize the resulting model. The author has used the material in the undergraduate unit operations lab course and in advanced control applications.
Physical dimension
1 online resource (xxxviii, 361 p.)UnknownUnknown
Summary / review / table of contents
Front Matter (Pages: i-xxxviii)
Part I : INTRODUCTION
CHAPTER 1 Introductory Concepts (Pages: 1-15)
CHAPTER 2 Model Types (Pages: 16-39)
Part II : PREPARATION FOR UNDERLYING SKILLS
CHAPTER 3 Propagation of Uncertainty (Pages: 41-66)
CHAPTER 4 Essential Probability and Statistics (Pages: 67-92)
CHAPTER 5 Simulation (Pages: 93-100)
CHAPTER 6 Steady and Transient State Detection (Pages: 101-115)
Part III : REGRESSION, VALIDATION, DESIGN
CHAPTER 7 Regression Target – Objective Function (Pages: 119-140)
CHAPTER 8 Constraints (Pages: 141-148)
CHAPTER 9 The Distortion of Linearizing Transforms (Pages: 149-156)
CHAPTER 10 Optimization Algorithms (Pages: 157-175)
CHAPTER 11 Multiple Optima (Pages: 176-184)
CHAPTER 12 Regression Convergence Criteria (Pages: 185-198)
CHAPTER 13 Model Design – Desired and Undesired Model Characteristics and Effects (Pages: 199-219)
CHAPTER 14 Data Pre‐ and Post‐processing (Pages: 220-236)
CHAPTER 15 Incremental Model Adjustment (Pages: 237-241)
CHAPTER 16 Model and Experimental Validation (Pages: 242-271)
CHAPTER 17 Model Prediction Uncertainty (Pages: 272-276)
CHAPTER 18 Design of Experiments for Model Development and Validation (Pages: 277-292)
CHAPTER 19 Utility versus Perfection (Pages: 293-296)
CHAPTER 20 Troubleshooting (Pages: 297-305)
Part IV : CASE STUDIES AND DATA
CHAPTER 21 Case Studies (Pages: 309-318)
CHAPTER A VBA Primer: Brief on VBA Programming – Excel in Office 2013 (Pages: 319-327)
CHAPTER B Leapfrogging Optimizer Code for Steady‐State Models (Pages: 328-340)
CHAPTER C Bootstrapping with Static Model (Pages: 341-349)
References and Further Reading (Pages: 350-353)
Index (Pages: 355-361)