SustainaBrew: Digital Transformation in Microbreweries enabling Sustainability

• Grant number: 24/15951-5
• Support Opportunities: Regular Research Grants
• Start date: April 01, 2025
• End date: March 31, 2027
• Field of knowledge: Engineering - Electrical Engineering - Industrial Electronics, Electronic Systems and Controls
• Principal Investigator: Andre Luis Dias
• Grantee: Andre Luis Dias
• Host Institution: Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP). Campus Sertãozinho. Sertãozinho , SP, Brazil
• Associated researchers: Afonso Celso Turcato ; Guilherme Serpa Sestito ; Jan Biering ; Jean Carlos Rodrigues da Silva ; João Vítor Silva Robazzi ; Maira Martins da Silva ; Rodrigo Nicoletti
• Associated scholarship(s): 25/21088-0 - Study of architectures for digital transformation in microbreweries, BP.TT; 25/06142-9 - Comparative Analysis of Microbreweries: Technology, Performance, and Sustainability in Brazil and Germany, BP.IC

Abstract

Technologies for digital transformation of industrial processes represent an opportunity for small and medium-sized enterprises to enhance their production processes, improving efficiency, and reducing waste and costs. Given that these companies have limited resources for investment in research and development, technologies such as artificial intelligence and the Internet of Things could offer advantages due to their low-cost characteristics. In this context, the number of breweries in Brazil has been increasing in recent years, mainly due to the creation of new microbreweries, typically small and medium-sized enterprises. These companies still have a low level of automation and require improvements in their industrial processes. Therefore, this project proposes to investigate architectures for digital transformation in microbreweries capable of increasing the transparency of the production process. Additionally, by utilizing machine learning tools, the project aims to develop predictive models using soft sensor strategies and intelligent fault diagnosis systems to contribute to reducing energy consumption, increasing equipment availability, and improving product quality. The challenging nature of the project lies primarily in investigating the development of soft sensor models and intelligent fault diagnosis systems using operational data from pumps and other variables typically available in the process, without the need for specific sensors. The project involves the participation of German institutions Versuchs- und Lehranstalt für Brauerei (VLB) and Institut für Produktionsanlagen und Konstruktionstechnik (Fraunhofer IPK). It is expected that the research project will contribute to cost-effective solutions that can be easily implemented in the production processes of microbreweries to meet the growing market demand in the sector and contribute to the sustainability of their processes. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

TURCATO, AFONSO CELSO; SESTITO, GUILHERME SERPA; DIAS, ANDRE LUIS; FLAUZINO, ROGERIO ANDRADE. Unsupervised Machine Learning-Based Intrusion Detection in PROFINET Networks. JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS, v. N/A, p. 12-pg., 2025-06-14. (24/15951-5)