Towards the Automation of a Chemical Sulphonation Process with Machine Learning

• URL: http://arxiv.org/abs/2009.12125v1
• Date: Fri, 25 Sep 2020 10:56:41 GMT
• Title: Towards the Automation of a Chemical Sulphonation Process with Machine Learning
• Authors: Enrique Garcia-Ceja, {\AA}smund Hugo, Brice Morin, Per-Olav Hansen, Espen Martinsen, An Ngoc Lam, {\O}ystein Haugen
• Abstract summary: This paper presents the results of applying machine learning methods during a chemical sulphonation process. We used data from process parameters to train different models including Random Forest, Neural Network and linear regression. Our experiments showed that it is possible to predict those product quality values with good accuracy, thus, having the potential to reduce time.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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