Desenvolvimento de algoritmo para gestão e análise da conformidade da segurança ocupacional em unidades armazenadoras de grãos

• Ano de defesa: 2021
• Autor(a) principal: Bellochio, Sabrina Dalla Corte
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Engenharia Agrícola; UFSM; Programa de Pós-Graduação em Engenharia Agrícola; Centro de Ciências Rurais
• Palavras-chave em Português: Riscos ocupacionais; Normas; Pós-colheita; Computação aplicada à agricultura; Occupational hazards; Standards; Post-harvest; Computing applied to agriculture; CNPQ::CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA
• Link de acesso: http://repositorio.ufsm.br/handle/1/23534

Brazilian grain production stands out worldwide and post-harvest processes are important in defining the price strategy for agricultural commodities. On the other hand, operations at the grain storage facilities offer occupational hazards. Thus, the facilities need to comply with the determinations of the Regulatory Standards (NR) for Occupational Safety. NR 1, which will come into effect in January 2022, aims to establish guidelines and requirements for occupational hazards management. In this sense, it is possible to find several occupational hazards in grain storage activities, such as: physical (noise and heat); chemical (dust and modified atmosphere in confined spaces); biological (rats and pigeons); the ergonomic and accidents (grain entrapment and work at height). Thus, the objective of the study was to develop a computational tool governed by an algorithm that can help to analyze and manage the application of NR 31, NR 33 and NR 35, in addition to Technical Resolution No. 22 and at grain storage facilities processes. For the application, checklists with the standards requirements by operation, machine and/or equipment were elaborated. The noise, heat and dust quantification occurred with specific equipment, calibrated, in a grain storage facility located in the region of Vale do Rio Pardo, in RS. The logical sequence for the algorithm development was given by guidelines and flowcharts. The algorithm was called GerSegUA. The database was relational, with a PostgreSQL management system, hosted on the UFSM server at Cachoeira do Sul campus. Its development language was Python with Flask data package and Flask SQLAlchemy for the backend and JavaScript with data package NodeJS data for the front-end, this choice was based on the availability of resources (human, material and infrastructure). The main results showed 73% of compliance with the standards. The expedition process had the greatest non-compliance. As for the quantified hazards, noise in the grain cleaning process presented values above the tolerance limit established by NR15, as well as dust in the grain receiving, cleaning and expedition processes. It was concluded that the evaluated storage facility did not present full compliance with the provisions of the confronted standards and does not have an occupational risk management program. Finally, it is possible to apply GerSegUA as a tool for the management and analysis of occupational safety compliance in grain storage units, in compliance with NR 1.