  In this paper we present the results of a feature importance analysis of a
chemical sulphonation process. The task consists of predicting the
neutralization number (NT), which is a metric that characterizes the product
quality of active detergents. The prediction is based on a dataset of
environmental measurements, sampled from an industrial chemical process. We
used a soft-sensing approach, that is, predicting a variable of interest based
on other process variables, instead of directly sensing the variable of
interest. Reasons for doing so range from expensive sensory hardware to harsh
environments, e.g., inside a chemical reactor. The aim of this study was to
explore and detect which variables are the most relevant for predicting product
quality, and to what degree of precision. We trained regression models based on
linear regression, regression tree and random forest. A random forest model was
used to rank the predictor variables by importance. Then, we trained the models
in a forward-selection style by adding one feature at a time, starting with the
most important one. Our results show that it is sufficient to use the top 3
important variables, out of the 8 variables, to achieve satisfactory prediction
results. On the other hand, Random Forest obtained the best result when trained
with all variables.
