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.
Acknowledgements
The authors would like to acknowledge the Fundamental Research Grant Scheme (FRGS/2/2014/TK05/ CURTIN/02/1), Ministry of Education, Malaysia and Curtin University Malaysia for co-funding this research.
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Artikel in diesem Heft
- Research Articles
- Evaluating the Performance of Newly Integrated Model in Nonlinear Chemical Process Against Missing Measurements
- Human Immunoglobulin G Adsorption in Epoxy Chitosan/Alginate Adsorbents: Evaluation of Isotherms by Artificial Neural Networks
- Effect of Anode Gas Diffussion Layer Thickness and Porosity on the Performance of Passive Direct Methanol Fuel Cell
- A Multiobjective Robust Approach for the Design of Natural Gas Transmission Pipelines
- Steam Reforming of Acetic Acid: Response Surface Modelling and Study of Factor Interactions
- Optimization of a Computer Simulated Styrene Plant by Surface Response and Environmental Impact Evaluation
- A State Estimation Method Based on Integration of Linear and Extended Kalman Filters
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