The performance evaluation of the process industry

which has been a popular topic nowadays

can not only find the weakness and verify the resilience and reliability of the process

but also provide some suggestions to improve the process benefits and efficiency. Nevertheless

the performance assessment principally concentrates upon some parts of the entire system at present

for example the controller assessment. Although some researches focus on the whole process

they aim at discovering the relationships between profit

society

policies and so forth

instead of relations between overall performance and some manipulated variables

that is

the total plant performance. According to the big data of different performance statuses

this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process

a classificationvisualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram.