Abstract

Obtaining an accurate and comprehensive multivariable mathematical model of the polymerization processes is of strategic importance to the control engineering purposes in the polymerization industry. These processes are typically nonlinear and it is difficult to apply traditional estimation techniques. This paper describes an approach based upon multivariable fuzzy-neural representation of the process model. A concrete model is constructed with the Sugeno fuzzy inference technique by applying the state space implementation in the local fuzzy rules for modeling the dynamic behavior of the polymer process. Such multivariable fuzzy-neural models of polymer quality could be used successfully for optimization and control of polymerization processes. A short example for such an implementation is included with additional results for the modeling of the main characteristic parameters of the polymer process.

Recommended Citation

Chitanov, Vassiliy and Petrov, Michail (2009) "Multivariable Fuzzy-Neural Model of Polymer Process," Chemical Product and Process Modeling: Vol. 4 : Iss. 1, Article 10.
DOI: 10.2202/1934-2659.1204
Available at: http://www.bepress.com/cppm/vol4/iss1/10