Virtual Analyzers for MI and Density Based on Neural Networks Improved through an Integrated Strategy Involving a Constructive Algorithm and Definition of Initial Weights

Abstract

AbstractThis work presents the development and validation of two virtual analyzers (density and Melt Index (MI)) for quality monitoring and control of the final product in an industrial unit of Linear Polyethylene (LPE). Both models are based on Feedforward Neural Networks which are improved through a strategy involving the initial estimation of weights and a constructive algorithm to define the number of hidden units. The initialization strategy is based on linearization of the neural model with only one hidden unit (nonlinear model) and subsequent optimization of this model by maximizing its similarity to the standard linear regression model whose solution is obtained analytically. The Initial Neural Model (INM) is then used as a starting point for a gradual increase in the number of hidden units. In a validation test involving MI and density values collected over 2 years of operation, the neural model is able to predict these properties with mean percentage errors equal to 0.81% (MI) and 0.04% (density) and determination coefficients equal to 0.970 (MI) and 0.983 (density). The population coefficient estimated in all tests involving grade transitions (0.96) shows a strong linear correlation between the proposed model and laboratory measurements.