Fault Detection and Fault-Tolerant Control of Particulate Processes using Population Balance Models

This paper presents a fault-tolerant control (FTC) architecture for particulate processes described by population balance models with control constraints, actuator faults and limited process measurements. The architecture integrates model-based nonlinear feedback, state estimation, fault detection and supervisory control on the basis of an appropriate reduced-order model that captures the dominant process dynamics. Appropriate fault detection thresholds and controller reconfiguration laws are derived to ensure robustness of the FTC architecture to model reduction and state estimation errors when implemented on the particulate process. The proposed methodology is applied to the problem of constrained, actuator fault-tolerant stabilization of an unstable steady-state of a continuous crystallizer.