This book is focused on the development of rigorous, yet practical, methods for the design of advanced process control systems to improve process operational safety and cybersecurity for a wide range of nonlinear process systems.
Process Operational Safety and Cybersecurity develops designs for novel model predictive control systems accounting for operational safety considerations, presents theoretical analysis on recursive feasibility and simultaneous closed-loop stability and safety, and discusses practical considerations including data-driven modeling of nonlinear processes, characterization of closed-loop stability regions and computational efficiency. The text then shifts focus to the design of integrated detection and model predictive control systems which improve process cybersecurity by efficiently detecting and mitigating the impact of intelligent cyber-attacks.
The book explores several key areas relating to operational safety and cybersecurity including:
machine-learning-based modeling of nonlinear dynamical systems for model predictive control;
a framework for detection and resilient control of sensor cyber-attacks for nonlinear systems;
insight into theoretical and practical issues associated with the design of control systems for process operational safety and cybersecurity; and
a number of numerical simulations of chemical process examples and Aspen simulations of large-scale chemical process networks of industrial relevance.
A basic knowledge of nonlinear system analysis, Lyapunov stability techniques, dynamic optimization, and machine-learning techniques will help readers to understand the methodologies proposed. The book is a valuable resource for academic researchers and graduate students pursuing research in this area as well as for process control engineers.
Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
Process Operational Safety and Cybersecurity develops designs for novel model predictive control systems accounting for operational safety considerations, presents theoretical analysis on recursive feasibility and simultaneous closed-loop stability and safety, and discusses practical considerations including data-driven modeling of nonlinear processes, characterization of closed-loop stability regions and computational efficiency. The text then shifts focus to the design of integrated detection and model predictive control systems which improve process cybersecurity by efficiently detecting and mitigating the impact of intelligent cyber-attacks.
The book explores several key areas relating to operational safety and cybersecurity including:
machine-learning-based modeling of nonlinear dynamical systems for model predictive control;
a framework for detection and resilient control of sensor cyber-attacks for nonlinear systems;
insight into theoretical and practical issues associated with the design of control systems for process operational safety and cybersecurity; and
a number of numerical simulations of chemical process examples and Aspen simulations of large-scale chemical process networks of industrial relevance.
A basic knowledge of nonlinear system analysis, Lyapunov stability techniques, dynamic optimization, and machine-learning techniques will help readers to understand the methodologies proposed. The book is a valuable resource for academic researchers and graduate students pursuing research in this area as well as for process control engineers.
Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
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