Input selection methods for data-driven Soft sensors design: Application to an industrial process

Soft Sensors (SSs) are inferential models which are widely used in industry. They are generally built through data-driven approaches that exploit industry historical databases. Selection of input variables is one of the most critical issues in SSs design. This paper aims at highlighting difficulties arising from the implementation of data-driven input selection methods when solving real-world case studies. A procedure is, therefore, proposed for input selection, based on both data-driven and expert-driven input selection methods. The procedure allows designing SSs with good prediction accuracy and a low number of inputs. The design of an SS for a real-world industrial process is used. The results reported show that the selection methods proposed in literature do not give consistent results when applied to the considered case study. The key role for plant expert knowledge emerges, outlining the opportunity of judicious use of automatic data-driven procedures.