A New Soft Sensor Based on Recursive Partial Least Squares for Online Melt Index Predictions in Grade-Changing HDPE Operations

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Faisal Ahmed ◽  
Salman Nazir ◽  
Yeong Koo Y Yeo
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Threshold Value ◽  
Soft Sensor ◽  
Polymerization Process ◽  
Model Bias ◽  
Soft Sensors ◽  
Process Data ◽  
Melt Index ◽  
Relative Root

Soft Sensors have been developed through phenomenological, empirical and hybrid modeling for quality variable predictions in various chemical processes. In this work a soft sensor based on an empirical model has been developed for the successful predictions of melt index (MI) in grade-changing polymerization of High Density Polyethylene (HDPE) processes. In order to capture the nonlinearity and grade-changing characteristics of the polymerization process efficiently, a recursive partial least squares (RPLS) update as well as a model bias update is applied to the process data successfully. Two schemes have been proposed: scheme-I and scheme-II. Scheme-I makes use of an arbitrary threshold value which selects one of the two update strategies according to the process requirement at a certain updating instance so as to minimize the relative root mean square error (RMSE). On the other hand, with the aim of preventing excessive RPLS update, scheme-II minimizes the number of RPLS update runs (NPR) while maintaining, increasing or sometimes reducing the RMSE obtained from scheme-I. Proposed schemes are compared with other strategies to exhibit their superiority.

scholarly journals Developing Soft Sensors for Polymer Melt Index in an Industrial Polymerization Process Using Deep Belief Networks

2019 ◽  
Vol 17 (1) ◽  
pp. 44-54 ◽  
Author(s):  
Chang-Hao Zhu ◽  
Jie Zhang
Keyword(s):  
Neural Networks ◽  
Polymer Melt ◽  
Polymerization Process ◽  
Training Phase ◽  
Measurement Instruments ◽  
Soft Sensors ◽  
Process Data ◽  
Melt Index ◽  
Model Complex ◽  
Unsupervised Training

Abstract This paper presents developing soft sensors for polymer melt index in an industrial polymerization process by using deep belief network (DBN). The important quality variable melt index of polypropylene is hard to measure in industrial processes. Lack of online measurement instruments becomes a problem in polymer quality control. One effective solution is to use soft sensors to estimate the quality variables from process data. In recent years, deep learning has achieved many successful applications in image classification and speech recognition. DBN as one novel technique has strong generalization capability to model complex dynamic processes due to its deep architecture. It can meet the demand of modelling accuracy when applied to actual processes. Compared to the conventional neural networks, the training of DBN contains a supervised training phase and an unsupervised training phase. To mine the valuable information from process data, DBN can be trained by the process data without existing labels in an unsupervised training phase to improve the performance of estimation. Selection of DBN structure is investigated in the paper. The modelling results achieved by DBN and feedforward neural networks are compared in this paper. It is shown that the DBN models give very accurate estimations of the polymer melt index.

Evaluating the Performance of Newly Integrated Model in Nonlinear Chemical Process Against Missing Measurements

2019 ◽  
Vol 14 (4) ◽  
Author(s):  
Vivianna Maria Mickel ◽  
Wan Sieng Yeo ◽  
Agus Saptoro
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Treatment Method ◽  
Singular Value ◽  
Data Driven ◽  
Missing Measurements ◽  
Soft Sensors ◽  
Kernel Partial Least Squares ◽  
Weighted Kernel ◽  
Value Decomposition

Abstract Application of data-driven soft sensors in manufacturing fields, for instance, chemical, pharmaceutical, and bioprocess have rapidly grown. The issue of missing measurements is common in chemical processing industries that involve data-driven soft sensors. Locally weighted Kernel partial least squares (LW-KPLS) algorithm has recently been proposed to develop adaptive soft sensors for nonlinear processes. This algorithm generally works well for complete datasets; however, it is unable to cope well with any datasets comprising missing measurements. Despite the above issue, limited studies can be found in assessing the effects of incomplete data and their treatment method on the predictive performances of LW-KPLS. To address these research gaps, therefore, a trimmed scores regression (TSR) based missing data imputation method was integrated to LW-KPLS to formulate trimmed scores regression assisted locally weighted Kernel partial least squares (TSR-LW-KPLS) model. In this study, this proposed TSR-LW-KPLS was employed to deal with missing measurements in nonlinear chemical process data. The performances of TSR-LW-KPLS were evaluated using three case studies having different percentages of missing measurements varying from 5 % to 40 %. The obtained results were then compared to the results from singular value decomposition assisted locally weighted Kernel partial least squares (SVD-LW-KPLS) model. SVD-LW-KPLS was also proposed by incorporating a singular value decomposition (SVD) based missing data treatment method into LW-KPLS. From the comparative studies, it is evident that the predictive accuracies of TSR-LW-KPLS are superior compared to the ones from SVD-LW-KPLS.

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