This paper deals with the problem of inferential (soft) sensor design. The
nonlinear character of industrial processes is usually the main limitation to
designing simple linear inferential sensors with sufficient accuracy. In order
to increase the inferential sensor predictive performance and yet to maintain
its linear structure, multi-model inferential sensors represent a
straightforward option. In this contribution, we propose two novel approaches
for the design of multi-model inferential sensors aiming to mitigate some
drawbacks of the state-of-the-art approaches. For a demonstration of the
developed techniques, we design inferential sensors for a Vacuum Gasoil
Hydrogenation unit, which is a real-world petrochemical refinery unit. The
performance of the multi-model inferential sensor is compared against various
single-model inferential sensors and the current (referential) inferential
sensor used in the refinery. The results show substantial improvements over the
state-of-the-art design techniques for single-/multi-model inferential sensors.

本文研究针对推理（软）传感器设计的问题，建议两种新方法用于设计多模型推理传感器，以提高其预测性能并保持线性结构。通过设计真实世界石化炼油装置中的推理传感器，与其他单模型推理传感器和参考传感器进行对比，结果显示单模型 / 多模型推理传感器的设计技术相较于当前技术有显著改进。