Beschreibung:
Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control presents various mechanistic model based state estimators and data-driven model based state estimators with a special emphasis on their development and applications to process monitoring, fault diagnosis and control. The design and analysis of different state estimators are highlighted with a number of applications and case studies concerning to various real chemical and biochemical processes. The book starts with the introduction of basic concepts, extending to classical methods and successively leading to advances in this field.
Part I - BASIC DETAILS AND STATE ESTIMATION ALGORITHMS 1.?Optimal state estimation and its importance in process systems engineering 2.?Stochastic process and filtering theory 3.?Linear filtering and observation techniques with examples 4.?Mechanistic model-based nonlinear filtering and observation techniques for state estimation 5.?Data-driven modelling techniques for state estimation 6.?Optimal sensor configuration methods for state estimation