Abstract:

Process transition during start-up，shut-down or product changeover is frequently encountered in chemical industry. Processes are more prone to various malfunctions and unknown disturbances during transitions. Fault detection and diagnosis during process transitions is critical to ensure process safety and production capacity. A novel modeling method，two-dimensional dynamic principal component analysis（2DDPCA），was developed for monitoring batch process transition in author’s previous work. To follow up，a fault diagnosis method was proposed in this paper. Process characteristics changed by faults were decomposed into “within-batch” and “batch-to-batch” information. Based on this extracted information，contribution plot，associated with the change of fault variables correlation in the optimal region of support，can then be used to isolate and diagnose the abnormal process variables. Simulation results showed the feasibility and validity of the proposed method.