Abstract
Process operational safety plays an important role in designing control systems for chemical processes. Motivated by this, in this work, we develop a process Safeness Index-based economic model predictive control system for a broad class of stochastic nonlinear systems with input constraints. A stochastic Lyapunov-based controller is first utilized to characterize a region of the state-space surrounding the origin, starting from which the origin is rendered asymptotically stable in probability. Using this stability region characterization and a process Safeness Index function that characterizes the region in state-space in which it is safe to operate the process, an economic model predictive control system is then developed using Lyapunov-based constraints to ensure economic optimality, as well as process operational safety and closed-loop stability in probability. A chemical process example is used to demonstrate the applicability and effectiveness of the proposed approach.
Highlights
Process operational safety has become crucially important in the chemical industry since the failure of process safety devices/human error often leads to disastrous incidents causing human and capital loss [1]
In [2], the Safeness Index function S( x ) was developed to indicate the level of safety of a given state, through which process operational safety was integrated with process control system design to account for the process operational safety considerations resulting from multivariable interactions or interactions between units
A Safeness Index-based Lyapunov-based economic model predictive control (LEMPC) design was developed for stochastic nonlinear systems
Summary
Process operational safety has become crucially important in the chemical industry since the failure of process safety devices/human error often leads to disastrous incidents causing human and capital loss [1]. A new class of economic model predictive control systems (EMPC), in which the cost function penalizes process economics instead of the distances from the steady-state in a general quadratic form, was utilized to account for process operational safety and economic optimality based on a function called the Safeness Index [2,3]. Under the assumption of the stabilizability of the origin of the stochastic nonlinear system via a stochastic Lyapunov-based control law, a process Safeness Index function and the level sets of multiple Lyapunov functions are first utilized to characterize a safe operating region in state-space, starting from which recursive feasibility and process operational safety are derived in probability for the stochastic nonlinear system under an economic model predictive controller. A nonlinear chemical process example is used to demonstrate the application of the proposed stochastic Safeness Index-based LEMPC
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