Development of a digital system prototype for intelligent automation for monitoring industrial solid waste

• Grant number: 25/04354-9
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: June 01, 2025
• Status: Discontinued
• Field of knowledge: Engineering - Electrical Engineering - Industrial Electronics, Electronic Systems and Controls
• Principal Investigator: Alexandre da Silva Simões
• Grantee: Lívia Duran Lima Almeida Collete
• Host Institution: Instituto de Ciência e Tecnologia. Universidade Estadual Paulista (UNESP). Campus de Sorocaba. Sorocaba , SP, Brazil
• Company: Secretaria de Desenvolvimento Econômico (São Paulo - Estado). Instituto de Pesquisas Tecnológicas S/A (IPT)
• Associated research grant: 20/09850-0 - Applied Artificial Intelligence Research Center: Accelerating the Evolution of Industries Toward Standard 5.0, AP.PCPE
• Associated scholarship(s): 25/21673-0 - Neural Networks with Low-Rank Learning (LRL) in embedded devices: an exploration of multimodal edge IA capabilities, BE.EP.IC

Abstract

Siemens Energy, a partner company of the IASMIN Platform (Applied Research Center in Artificial Intelligence for the evolution of industries towards the 4.0 standard), currently generates around 600 (six hundred) tons of solid waste per month. This waste requires identification, monitoring and prediction systems that can be performed automatically by systems based on Artificial Intelligence (AI). However, before the development phase of the intelligent algorithms, it is necessary to design, implement, test and validate a digital system (hardware) that provides adequate support for this type of processing and meets the requirements for on-site installation at the company's manufacturing plant. This project describes the proposal to develop a prototype of a digital system for intelligent automation aimed at monitoring industrial solid waste. The prototype must meet several technical requirements, particularly ensuring edge and/or cloud computing capacity compatible with the project's needs, considering the future execution of Machine Learning (ML) algorithms, and must also collect data and information that allow not only the recognition and/or monitoring of solid waste in the company's park, but also the generation of future improvements in the company's logistics processes, production management and reuse of waste.