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.

软传感器（SS）是在行业中广泛使用的推理模型。它们通常通过利用行业历史数据库的数据驱动方法来构建。输入变量的选择是SS设计中最关键的问题之一。本文旨在突出解决实际案例研究时由于实施数据驱动的输入选择方法而引起的困难。因此，提出了一种基于数据驱动和专家驱动的输入选择方法的输入选择程序。该程序允许设计具有良好预测精度和少量输入的SS。

使用用于实际工业过程的SS设计。报告的结果表明，文献中提出的选择方法应用于所考虑的案例研究时并不能给出一致的结果。工厂专家知识的关键作用应运而生，概述了明智地使用自动数据驱动程序的机会。