  Nowadays, the continuous improvement and automation of industrial processes
has become a key factor in many fields, and in the chemical industry, it is no
exception. This translates into a more efficient use of resources, reduced
production time, output of higher quality and reduced waste. Given the
complexity of today's industrial processes, it becomes infeasible to monitor
and optimize them without the use of information technologies and analytics. In
recent years, machine learning methods have been used to automate processes and
provide decision support. All of this, based on analyzing large amounts of data
generated in a continuous manner. In this paper, we present the results of
applying machine learning methods during a chemical sulphonation process with
the objective of automating the product quality analysis which currently is
performed manually. We used data from process parameters to train different
models including Random Forest, Neural Network and linear regression in order
to predict product quality values. Our experiments showed that it is possible
to predict those product quality values with good accuracy, thus, having the
potential to reduce time. Specifically, the best results were obtained with
Random Forest with a mean absolute error of 0.089 and a correlation of 0.978.
