New Approach in Modeling of Metallocene‐Catalyzed Olefin Polymerization Using Artificial Neural Networks

Abstract

A new approach for the estimation of kinetic rate constants in olefin polymerization using metallocene catalysts is presented. The polymerization rate has been modeled using the method of moments. An ANN has been used and trained to behave like the mathematical model developed before, so that it gets polymerization rate at different reaction times and predicts reaction rate constants. The network was trained using modeling results in desired operational window. The polymerization rates were normalized to make the network work independent of operational conditions. The model has also been applied to real polymerization rate data and the predictions were satisfactory. This model is specially useful in comparing different new metallocene catalysts.magnified image

On the Robustness of Forward and Inverse Artificial Neural Networks for the Simulation of Ethylene/1‐Butene Copolymerization

“…[17] ANN models have been applied in various polymer applications as a predictive model for poly mer properties [18][19][20][21][22][23][24][25][26] and polymerization kinetics. [27][28][29] Recently, our research group developed forward and inverse ANN models to describe the copolymerization of ethylene and 1-butene with two single-site catalysts. [29] The forward model predicted the MWD and CCD, while the inverse model estimated the polymerization conditions required to make copolymers with the desired MWD and CCD.…”

“…This approach may be used to solve highly nonlinear and complex problem by finding the optimum learning patterns of relationship between input and output variables . ANN models have been applied in various polymer applications as a predictive model for polymer properties and polymerization kinetics …”

Estimation of Polymerization Conditions Needed to Make Ethylene/1-olefin Copolymers with Specific Microstructures Using Artificial Neural Networks

“…They are currently used in a variety of polymer applications with great successes. [8][9][10][11] However, most studies focused on simple forward relationship with average polymer properties for process control applications while, for ethylene/1-olefi n copolymers, the detailed microstructural distributions (MWD and CCD) are very important as they also strongly affect the fi nal properties.…”

Exploring the Compositional Heterogeneity of Vis‐Broken Impact Poly(propylene) Copolymers by Advanced Fractionation Methods

“…The need to understand how the peroxide affects the microstructure of these complex copolymers is critical in determining the final applicability and properties of the vis‐broken materials. Several studies have been conducted using high temperature size exclusion chromatography (HT‐SEC) to prove that the vis‐broken IPCs exhibit reduced molar masses and MMDs . Swart and van Reenen studied vis‐broken IPC bulk materials and their temperature rising elution fractionation (TREF) fractions using 13 C NMR and SEC coupled to Fourier transform infrared spectroscopy (FTIR) spectroscopy.…”

“…[ 11,12 ] One very feasible approach for increasing the size exclusion chromatography (HT-SEC) to prove that the vis-broken IPCs exhibit reduced molar masses and MMDs. [ 11,13,14,16,21 ] Swart and van Reenen [ 11 ] studied vis-broken IPC bulk materials and their temperature rising elution fractionation (TREF) fractions using 13 C NMR and SEC coupled to Fourier transform infrared spectroscopy (FTIR) spectroscopy. Based on the 13 C NMR analysis of the TREF fractions they showed that the vis-breaking process affects mainly copolymers with long ethylene sequences.…”