Optimality related variations-based operating optimality assessment for nonlinear industrial processes

In industrial processes, the process operating performance may deteriorate with time from optimal performance due to process disturbances, noise, and other uncertainties, which prompts the development of process operating optimality assessment. In this study, a novel online operating optimality assessment method based on optimality related variations (ORV) is proposed for nonlinear industrial processes. The ORV of each performance grade is extracted by analyzing the common and unique variations among multiple performance grades. Furthermore, the ORV is used in assessment and both the robustness and sensitivity of the ORV-based assessment method are improved for its characteristics in highlighting the process variations related to operating performance and excluding those unrelated variations. In online assessment, the process operating performance can be evaluated as a certain performance grade or the conversion process between two performance grades based on the assessment indices of the online data with respect to each performance grade. Finally, the efficiency of the proposed method is illustrated with a case of gold hydrometallurgical process.