Abstract
Multivariate statistical process control (MSPC) techniques play an important role in industrial batch process monitoring and control. One particularly popular approach to MSPC is partial least squares (PLS), which has been successfully applied many times in the modelling, estimation and control of batch processes. However, the nonlinear nature of many real, complex chemical systems means that traditional linear PLS is not always suitable. In this paper, the use of a nonlinear multi-way PLS is proposed to address the issues of non-linearity in batch processes. By analysing and comparing linear multi-way PLS, Neural network multi-way PLS, Type I and Type II nonlinear multi-way PLS models, the advantages and limitations of these methods are identified and summarised.
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