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Towards the Automation of a Chemical Sulphonation Process with Machine Learning

Towards the Automation of a Chemical Sulphonation Process with Machine Learning

Category
Conference lecture and academic presentation
Abstract
Nowadays, the continuous improvement of industrial processes has become a key factor in many fields, and the chemical industry 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 industrial processes today, 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 optimize 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 aim of optimizing the process. 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. Specifically, the best results were obtained with Random Forest with a mean absolute error of 0.089 and a correlation of 0.978.
Client
  • Norges forskningsråd / 282904
  • EU / 737459
Language
English
Author(s)
Affiliation
  • SINTEF Digital / Software and Service Innovation
  • Østfold University College
Presented at
The 7th International Conference on Control, Mechatronics and Automation
Place
Delft
Date
05.11.2019 - 07.11.2019
Year
2019