\u003cp\u003e\u003ci\u003eOptimal State Estimation for Process Monitoring, Fault Diagnosis and Control\u003c/i\u003e 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.\u003c/p\u003e \u003cp\u003eDesign 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.\u003c/p\u003e\u003cul\u003e \u003cli\u003eDescribes various classical and advanced versions of mechanistic model based state estimation algorithms\u003c/li\u003e \u003cli\u003eDescribes various data-driven model based state estimation techniques\u003c/li\u003e \u003cli\u003eHighlights a number of real applications of mechanistic model based and data-driven model based state estimators/soft sensors\u003c/li\u003e \u003cli\u003eBeneficial to those associated with process monitoring, fault diagnosis, online optimization, control and related areas\u003c/li\u003e\u003c/ul\u003e
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