(446a) Fault Detection and Isolation and Fault Tolerant Control of a Catalytic Alkylation of Benzene Process

Fault-tolerant control is an essential component in modern process
industries as abnormal situations account for over $20 billion in lost
annual revenue in the US alone. Traditionally, control systems rely on
centralized control architectures utilizing dedicated wired links for
measurement sensors and control actuators to operate a plant at desired
conditions and a separate monitoring component for detecting faults.
While
this paradigm to process monitoring and control has been successful,
modern chemical
plants that rely on highly automated processes to maintain robust
operations and efficient production are vulnerable to abnormal
events. This issue has prompted significant research efforts in the
integration with and application of fault-tolerant control methods to
existing legacy
control systems including chemical processes controlled by distributed
model predictive control (DMPC) systems (e.g., [1]).

The focus of this work is
on the application of an integrated fault detection and isolation and
fault-tolerant control (FDIFTC) framework to a catalytic
alkylation of benzene process which is controlled by a DMPC system and
is
subjected to unknown, persistent control actuator faults. The FDIFTC
system
uses measurements of process variables like temperature and
concentrations.
To design the fault detection and isolation (FDI) system, we take
advantage
of recent results on FDI that address both FDI filter design, residual
generation and residual threshold computation using
fault-free process data [2]. After isolation of an actuator
fault, the FDIFTC system estimates the fault magnitude, recalculates a
new
optimal operating point, and ultimately reconfigures the DMPC system to
maintain stability of the process in an optimal manner. Extensive
simulations are carried out to demonstrate the performance of the FDIFTC
system from stability and performance points of view.

1. D. Chilin, J. Liu, D. Muñoz de la Peña, P.D. Christofides, J. F.
Davis, Detection, isolation and handling of actuator faults in
distributed
model predictive control systems, Journal of Process Control 20 (2010)
1059--1075.

2. D. Chilin, J. Liu, J. F. Davis, P. D. Christofides, Data-based
monitoring and
reconfiguration of a distributed model predictive control system,
International Journal of Robust and Nonlinear Control 22 (2012) 68--88.