Control of coffee grinding with Artificial Neural Networks

Quality assessment and standardization of the property of the final product is fundamental in food industry. Coffee particle granulometry and density are continuously monitored during coffee beans grinding and grinders are controlled by operators in order to keep coffee particle granulometry within specific thresholds. In this work, a neural system is used to learn how to control two grinders used for coffee production at LAVAZZA factory, obtaining average control error of the order of a few μm. The results appear promising for the future development of an automatic decision support system.