Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control

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. Design and implementation of various classical and advanced state estimation methods to solve a wide variety of problems makes this book immensely useful for the audience working in different disciplines in academics, research and industry in areas concerning to process monitoring, fault diagnosis, control and related disciplines. . Describes various classical and advanced versions of mechanistic model based state estimation algorithms. . Describes various data-driven model based state estimation techniques. . Highlights a number of real applications of mechanistic model based and data-driven model based state estimators/soft sensors. . Beneficial to those associated with process monitoring, fault diagnosis, online optimization, control and related areas. INDICE: 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  Part II - APPLICATION OF MECHANISTIC MODEL-BASED NONLINEAR FILTERING AND OBSERVATION TECHNIQUES FOR STATE ESTIMATION IN CHEMICAL PROCESSES  7.?Optimal state estimation in multicomponent batch distillation   8.?Optimal state estimation in multicomponent reactive batch distillation with optimal sensor configuration   9.?Optimal state estimation in complex nonlinear dynamical systems  10.?Optimal state estimation of a kraft pulping digester?   11.?Optimal State Estimation of a High Dimensional Fluid Catalytic Cracking Unit  12.?Optimal state estimation of continuous distillation column with optimal sensor configuration   13.?Optimal state and parameter estimation in nonlinear CSTR  Part III - APPLICATION OF QUANTITATIVE MODEL-BASED NONLINEAR FILTERING AND OBSERVATION TECHNIQUES FOR STATE ESTIMATION IN BIOCHEMICAL PROCESSES  14.?Optimal state and parameter estimation in the nonlinear batch beer fermentation process  15.?Optimal state and parameter estimation for online optimization of an uncertain biochemical reactor  Part IV - APPLICATION OF DATA-DRIVEN MODEL-BASED TECHNIQUES FOR STATE ESTIMATION IN CHEMICAL PROCESSES  16.?Data-driven methods for state estimation in multi-component batch distillation   17.?Hybrid schemes for state estimation  18.?Future development, prospective and challenges in the application of soft sensors in industrial applications 

• ISBN: 978-0-323-85878-6
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 366
• Fecha Publicación: 04/02/2022
• Nº Volúmenes: 1
• Idioma: Inglés