Operating optimality assessment based on optimality related variations and nonoptimal cause identification for industrial processes

• The extraction of the optimality related variations is developed.
• Optimality related variations-based assessment method is proposed.
• Online assessment results include steady performance grades and the conversions.
• A cause identification strategy is developed for nonoptimal operating performance.

In this study, a novel online operating optimality assessment based on optimality related variations and nonoptimal cause identification method is proposed for industrial processes. The optimality related variations are extracted from each steady performance grade by analyzing the common and unique variations among steady performance grades, which avoids the time-consuming data alignment. When the optimality related variations are used in assessment, both the robustness and sensitivity of the assessment method are improved compared with the PCA-based assessment for its abilities in highlighting the process variations related to operating performance and excluding those unrelated variations. Based on the similarities between the optimality related variations of the online data and that of each steady performance grade, the process operating performance can be evaluated as the steady performance grade or the conversion process between performance grades, and this provides more information for the in-depth understanding of the process operating. For nonoptimal operating performance, the nonoptimal cause identification strategy is developed for further production adjustment and performance improvement. Finally, the efficiency of the proposed method is illustrated with a case of gold hydrometallurgical process.