M. B. De Souza Jr., J. C. Pinto and E. L. Lima, “Control of a Chaotic Polymerization Reactor: A Neural Network Based Model Predictive Approach,” Polymer Engineering & Science, Vol. 36, No. 4, 1996, pp. 448-457.
http://dx.doi.org/10.1002/pen.10431

has been cited by the following article:

• TITLE: Constrained Nonlinear Model Predictive Control of a Polymerization Process via Evolutionary Optimization

  AUTHORS: Masoud Abbaszadeh, Reza Solgi

  KEYWORDS: Model Predictive Control; Genetic Algorithms; Polymerization; Methyl Methacrylate; Parameter Identification

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.6 No.1, February 14, 2014

  ABSTRACT: In this work, a nonlinear model predictive controller is developed for a batch polymerization process. The physical model of the process is parameterized along a desired trajectory resulting in a trajectory linearized piecewise model (a multiple linear model bank) and the parameters are identified for an experimental polymerization reactor. Then, a multiple model adaptive predictive controller is designed for thermal trajectory tracking of the MMA polymerization. The input control signal to the process is constrained by the maximum thermal power provided by the heaters. The constrained optimization in the model predictive controller is solved via genetic algorithms to minimize a DMC cost function in each sampling interval.