Quality prediction and analysis for large-scale processes based on multi-level principal component modeling strategy

• A multi-level modeling strategy is proposed for large-scale processes.
• Quality prediction and analysis are carried out upon the multi-level data model.
• Critical-to-quality indices are defined for detailed quality analyses in large-scale process.
• Effectiveness of the new modeling strategy is confirmed through a benchmark process.

This paper proposed a multi-level principal component regression (PCR) modeling strategy for quality prediction and analysis of large-scale processes. Based on decomposition of the large data matrix, the first level PCR model divides the process into different sub-blocks through uncorrelated principal component directions, with a related index defined for determination of variables in each sub-block. In the second level, a PCR model is developed for local quality prediction in each sub-block. Subsequently, the third level PCR model is constructed to combine the local prediction results in different sub-blocks. For process analysis, a sub-block contribution index is defined to identify the critical-to-quality sub-blocks, based on which an inside sub-block contribution index is further defined for determination of the key variables in each sub-block. As a result, correlations between process variables and quality variables can be successfully constructed. A case study on Tennessee Eastman (TE) benchmark process is provided for performance evaluation.