Online fault diagnostic monitoring of a NMPC controlled hybrid CSTR plant in start-up operation using a modified particle filtering algorithm

Fault diagnostic monitoring in nonlinear hybrid processes requires dedicated techniques being capable of dealing with both nonlinearity and hybrid dynamic characteristics. This paper introduces a modified particle filtering estimation-based methodology for online diagnostic purposes by individual tracking of the most likely faulty modes and the process states in a nonlinear complex continuous stirred tank reactor (CSTR) process plant for start-up operation. A receding horizon nonlinear model predictive controller (NMPC) has been designed and installed on the CSTR process plant using a genetic algorithm (GA) to generate optimal discrete and continuous control inputs so as to drive the process operation from an initial starting state into a final desired state within a target region without violating forbidden regions. A series of simulation experiments have been conducted to demonstrate the effectiveness of the presented online particle filtering diagnostic approach.