Melt pressure is one of the key indicators of melt flow stability and quality in polymer extrusion processes. Often, process operators monitor/observe the melt pressure in real time to ensure the safe operation of industrial polymer extrusion processes. However, there might be situations where the melt pressure could not be measured using a physical sensor due to some constraints. Hence, the accurate prediction of this key extrusion parameter would enable the selection of suitable operating conditions to optimize extrusion processes and then minimize melt pressure instabilities. This paper introduces a data-driven model based on deep learning techniques for estimating melt pressure using extrusion process settings as inputs. A deep autoencoder is developed to extract nonlinear features from the process inputs while reducing the input space dimensions. The extracted features are then fed to a feedforward neural network to predict the melt pressure. No previous works have reported on using deep learning techniques for predicting the melt pressure. The proposed model exhibited good predictive performance with a normalized root mean square error of 0.045±0.003 on an unseen dataset. Moreover, it outperformed a neural network model with no dimensionality reduction techniques as well as a neural network combined with principal component analysis.
|Title of host publication||2023 European Control Conference (ECC) June 13-16, 2023, Bucharest, Romania|
|Publication status||Accepted/In press - 13 Jun 2023|