Neural Virtual Sensor for the Inferential Prediction of Product Quality from Process Variables

A predictive Fuzzy ARTMAP neural system and two hybrid networks, each combining a dynamic unsupervised classifier with a different kind of supervised mechanism, were applied to develop virtual sensor systems capable of inferring the properties of manufactured products from real process variables. A new method to construct dynamically the unsupervised layer was developed. A sensitivity analysis was carried out by means of self-organizing maps to select the most relevant process features and to reduce the number of input variables into the model. The prediction of the melt index (MI) or quality of six different LDPE grades produced in a tubular reactor was taken as a case study. The MI inferred from the most relevant process variables measured at the beginning of the process cycle deviated 5% from on-line MI values for single grade neural sensors and 7% for composite neural models valid for all grades simultaneously.

Rallo R, Ferré-Giné J, Arenas A, Giralt F (2002) Neural Virtual Sensor for the Inferential Prediction of Product Quality from Process Variables. Computers and Chemical Engineering (26) 12, 1735-54

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About robertrallo

Associate Professor, Dept. d'Eng. Informàtica i Matemàtiques at URV; Director of BioCENIT, Bioinformatics & Computational Environmental Engineering Research Team at URV; Member of ATIC, Advanced Technology & Innovation Center at URV; Visiting Scholar at UCLA; Member of the WaTeR center, UCLA; Member of the Center for Environmental Implications of Nanotechnology (CEIN), UCLA; Member of the Center for Nanobiology and Predictive Toxicology (CNPT), UCLA.

Posted on July 19, 2002, in Uncategorized and tagged , , , , , , . Bookmark the permalink. Leave a comment.

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