Data selection methods for Soft Sensor design based on feature extraction

Data selection is a critical issue in data-driven soft sensor design. The paper proposes a new method for data selection based on a feature extraction step, followed by data selection algorithms. The method has been applied to an industrial case study, i.e., the estimation of the quality of processed wastewater produced by a Sour Water Stripping plant working in a refinery. The paper reports the results obtained with different data selection algorithms. The comparison has been performed both by using raw data and the feature extraction phase.