Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 10; p. 3947
• Main Authors: Rubio-Loyola, Javier, Paul-Fils, Wolph Ronald Shwagger
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23-05-2022; MDPI
• Subjects: Artificial intelligence; black carbon; Carbon; Climate change; Cooling; Data analysis; Datasets; Emissions; Furnaces; Heat; Heat treatment; Industrial applications; industrial furnaces; Internet of Things; Lung cancer; Machine learning; Mechanical properties; Prediction models; predictive models; Sensors; industrial furnaces; predictive models; machine learning; black carbon
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22103947

Industry 4.0 constitutes a major application domain for sensor data analytics. Industrial furnaces (IFs) are complex machines made with special thermodynamic materials and technologies used in industrial production applications that require special heat treatment cycles. One of the most critical issues while operating IFs is the emission of black carbon (EoBC), which is due to a large number of factors such as the quality and amount of fuel, furnace efficiency, technology used for the process, operation practices, type of loads and other aspects related to the process conditions or mechanical properties of fluids at furnace operation. This paper presents a methodological approach to predict EoBC during the operation of IFs with the use of predictive models of machine learning (ML). We make use of a real data set with historical operation to train ML models, and through evaluation with real data we identify the most suitable approach that best fits the characteristics of the data set and implementation constraints in real production environments. The evaluation results confirm that it is possible to predict the undesirable EoBC well in advance, by means of a predictive model. To the best of our knowledge, this paper is the first approach to detail machine-learning concepts for predicting EoBC in the IF industry.