  Product quality assessment in the petroleum processing industry can be
difficult and time-consuming, e.g. due to a manual collection of liquid samples
from the plant and subsequent chemical laboratory analysis of the samples. The
product quality is an important property that informs whether the products of
the process are within the specifications. In particular, the delays caused by
sample processing (collection, laboratory measurements, results analysis,
reporting) can lead to detrimental economic effects. One of the strategies to
deal with this problem is soft sensors. Soft sensors are a collection of models
that can be used to predict and forecast some infrequently measured properties
(such as laboratory measurements of petroleum products) based on more frequent
measurements of quantities like temperature, pressure and flow rate provided by
physical sensors. Soft sensors short-cut the pathway to obtain relevant
information about the product quality, often providing measurements as
frequently as every minute. One of the applications of soft sensors is for the
real-time optimization of a chemical process by a targeted adaptation of
operating parameters. Models used for soft sensors can have various forms,
however, among the most common are those based on artificial neural networks
(ANNs). While soft sensors can deal with some of the issues in the refinery
processes, their development and deployment can pose other challenges that are
addressed in this paper. Firstly, it is important to enhance the quality of
both sets of data (laboratory measurements and physical sensors) in a data
pre-processing stage (as described in Methodology section). Secondly, once the
data sets are pre-processed, different models need to be tested against
prediction error and the model's interpretability. In this work, we present a
framework for soft sensor development from raw data to ready-to-use models.
