Although industrial processes often perform perfectly under design conditions

they may deviate from the optimal operating point owing to parameters drift

environmental disturbances

etc. Thus

it is necessary to develop efficacious strategies or procedure to assess the process performance online. In this paper

we explore the issue of operating optimality assessment for complex industrial processes based on performance-similarity considering nonlinearities and outliers simultaneously

and a general enforced online performance assessment framework is proposed. In the offline part

a new and modified total robust kernel projection to latent structures algorithm

T-KPRM

is proposed and used to evaluate the complex nonlinear industrial process

which can effectively extract the optimal-index-related process variation information from process data and establish assessment models for each performance grades overcoming the effects of outlier. In the online part

the online assessment results can be obtained by calculating the similarity between the online data from a sliding window and each of the performance grades. Furthermore

in order to improve the accuracy of online assessment

we propose an online assessment strategy taking account of the effects of noise and process uncertainties. The Euclidean distance between the sliding data window and the optimal evaluation level is employed to measure the contribution rates of variables

which indicate the possible reason for the non-optimal operating performance. The proposed framework is tested on a real industrial case:dense medium coal preparation process

and the results shows the efficiency of the proposed method comparing to the existing method.