Supervised functional modeling method for long durations of batch processes with limited batch data

Abstract

The batch duration in most batch units is quite long and the number of batch runs is very limited, so it is difficult to build accurate monitoring models. A powerful supervised functional monitoring method, called wavelet functional partial least squares, is proposed. First, an active learning strategy is used to extract features of process variables using orthogonal wavelet approximations and achieve a more concise model. Then the partial least squares method can be constructed using the extracted features and quality data, so the regression model is robust. Using the compact support property of wavelet functions, the process has multiple phases. The final quality can be expressed as a summation of multiple sub-qualities so multiple local models can be established for within-batch detection of both process data and quality data. The advantages and merits of the proposed method are demonstrated using a numerical case and an industrial sintering process for polytetrafluoroethylene.