  Inferential (or soft) sensors are used in industry to infer the values of
imprecisely and rarely measured (or completely unmeasured) variables from
variables measured online (e.g., pressures, temperatures). The main challenge,
akin to classical model overfitting, in designing an effective inferential
sensor is the selection of a correct structure of the sensor. The sensor
structure is represented by the number of inputs to the sensor, which
correspond to the variables measured online and their (simple) combinations.
This work is focused on the design of inferential sensors for product
composition of an industrial distillation column in two oil refinery units, a
Fluid Catalytic Cracking unit and a Vacuum Gasoil Hydrogenation unit. As the
first design step, we use several well-known data pre-treatment (gross error
detection) methods and compare the ability of these approaches to indicate
systematic errors and outliers in the available industrial data. We then study
effectiveness of various methods for design of the inferential sensors taking
into account the complexity and accuracy of the resulting model. The
effectiveness analysis indicates that the improvements achieved over the
current inferential sensors are up to 19 %.
