Fuzzy clustering for data compression, modeling, and optimization in recovered paper industry

Three key problems in industrial plant optimization are the compression of data from the automation system, the estimation of values which are not directly available, and the determination of process parameters leading to minimum production cost for given quality. Clustering can be used to determine technologically meaningful operating points from data sets which serve as compressed archive data. Block selection techniques yield a speedup that makes this method feasible for industrial applications. Clustering can also be used to generate nonlinear models from sensor and laboratory data. These models are used as soft sensors which give good online estimations of variables which can only be measured offline in the laboratory. Moreover, nonlinear process models obtained by clustering can be used for process optimization, so that target qualities can be achieved so that application-specific cost functions are minimized. All three methods, data compression, soft sensor, and process optimization, are applied to the deinking process in recovered paper processing in the paper industry.