Inferential estimation of molecular weights of polybutadiene rubber by neural networks

Abstract

Molecular weight distribution, which is characterized by its averages like number average (Mn) and weight average (Mw), is one of the important properties of polybutadiene rubber (PBR), and it is difficult to measure. The objective of this work is to develop models to predict Mn and Mw from readily available process variables. Neural networks that are capable of mapping highly complex and non-linear dependencies have been adapted to develop models for the Mn and Mw of PBR. The molecular weight distribution and its averages of PBR samples collected over a wide range of operating conditions were measured by the conventional Gel Permeable Chromatograph (GPC) method. Neural networks were trained with relevant data to predict Mn and Mw from process variables. The trained networks were found to generalize well when tested with new data.

Artificial neural network prediction of thermal and mechanical properties forBi2O3‐polybenzoxazinenanocomposites

“…Each link, like synapses in the human brain, can transmit a signal to other neurons 29 . An artificial neuron receives a signal, evaluates it, and then transmits it to the neurons to which it is linked 30,31 . A nonlinear function of the sum of a neuron's inputs generates its output, and the signal at a connection is represented by a real number (weights) 32 .…”

“…29 An artificial neuron receives a signal, evaluates it, and then transmits it to the neurons to which it is linked. 30,31 A nonlinear function of the sum of a neuron's inputs generates its output, and the signal at a connection is represented by a real number (weights). 32 As learning process occurs, weights are frequently modified, allowing it to describe complex data using nonlinear functions from a set of relevant input parameters.…”