  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.
