Comprehensive economic index prediction based operating optimality assessment and nonoptimal cause identification for multimode processes

For many multimode processes, the process operating performance may deteriorate with time from optimal state due to process disturbances, noise, and other uncertainties, and it is important to develop an effective operating optimality assessment method; however, it has not yet been paid sufficient attention and few researches have been reported in this area so far. In this study, a novel comprehensive economic index (CEI) prediction based operating optimality assessment and nonoptimal cause identification method is proposed for continuous multimode processes. The assessment strategies are formulated for both stable and transitional mode on account of their different process characteristics. In stable mode, the CEI is predicted by some common methods and then the optimality index is constructed based on the predicted CEI. In transitional mode, the CEI of a transition is predicted by the weighted average of the CEIs of the similar historical transitions, and then the optimality index is calculated for online assessment of the transitional mode. When the operating performance is nonoptimal, the responsible cause variables can be identified by the proposed nonoptimal cause identification method. Finally, the feasibility and efficiency of the proposed strategies are demonstrated through the Tennessee Eastman (TE) process.