On-line soft sensor for polyethylene process with multiple production grades

Abstract Since online measurement of the melt index (MI) of polyethylene is difficult, a virtual sensor model is desirable. However, a polyethylene process usually produces products with multiple grades. The relation between process and quality variables is highly nonlinear. Besides, a virtual sensor model in real plant process with many inputs has to deal with collinearity and time-varying issues. A new recursive algorithm, which models a multivariable, time-varying and nonlinear system, is presented. Principal component analysis (PCA) is used to eliminate the collinearity. Fuzzy c-means (FCM) and fuzzy Takagi–Sugeno (FTS) modeling are used to decompose the nonlinear system into several linear subsystems. Effectiveness of the model is demonstrated using real plant data from a polyethylene process.

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