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• W1980147362 abstract "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." @default.
• W1980147362 created "2016-06-24" @default.
• W1980147362 creator A5055614144 @default.
• W1980147362 date "2014-10-01" @default.
• W1980147362 modified "2023-09-29" @default.
• W1980147362 title "Quality prediction and analysis for large-scale processes based on multi-level principal component modeling strategy" @default.
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• W1980147362 doi "https://doi.org/10.1016/j.conengprac.2014.06.006" @default.
• W1980147362 hasPublicationYear "2014" @default.
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