Analytics for the Optimization of the Soybean Oil Purification Process

Abstract

Machine Learning, Artificial Intelligence, among others, are very promising methodologies and technologies that are emerging for implementing a broad spectrum of analytics within digitalized eco-systems. Analytics containing adequate analytical models are generating a burgeoning interest from Business Intelligence-, Information Technology (IT)- and Operational Technology (OT)-professionals, who are able to exploit the huge amount of internally and externally available data and information that lies behind digitalized components and systems and their associated processes. In this paper, the authors present the essential specifications of an analytical model developed and implemented applying the Knowldege Discovery in Databases (KDD) approach. The analytical model is the essential part of an analytics component, positioned as an digitalized asset within an Industry 4.0 compliant (RAMI 4.0) infrastructure, and used to optimize the industrial Soybean Oil Purification Process associated to the digitalized eco-system.