Industries in general need a reliable system for fault identification and alarm management. The high incidence of alarms can overload the operator, exposing him to conditions that may exceed their ability to perform effective actions, impairing his performance during his workday. In this context, the present paper proposes an approach for alarms rationalization based on process mining techniques. The alarm rationalization is in accordance to the alarm lifecycle management model suggested by ANSI / ISA-18 standard. The aim of this paper is to improve the alarm system through its rationalization, allowing an adequate organization and data layout of the whole set of alarms. Thus, it is expected a better interpretation and understanding of the industrial process. Performance metrics recommended by ANSI / ISA-18 standard are used. Such metrics are used for analyzing a database of alarms coming from an industrial plant. Preliminaries results have demonstrated the feasibility of the present approach. The results show that the use of the process mining technique can provide support on rationalization alarms, standing for a promising method in alarm management domain.
This paper presents a comparative analysis among four control strategies for the control of a combined anaerobic-aerobic wastewater treatment configuration. The anaerobic stage is an Upflow Anaerobic Sludge Blanket (UASB) reactor, whereas the aerobic stage consists of the Activated Sludge (AS) process. The control variables are solids concentration in the effluent and sludge wastage rate. The proposed control strategy is considered the most reliable among them. It is based on two cascaded PI-controllers for the solids concentration in the effluent and a look-up table for the sludge wastage rate control. Experimental and simulated results are presented.
The structure of the Grand Challenges Scholars Program (GCSP) of the Universidade Federal de Minas Gerais -UFMG, School of Engineering, linked to the National Academy of Engineering (NAE) former initiative, is presented. This academic program has an aspirational vision of educating professionals for facing the challenges of engineering in the twenty-first century. The approach is based on an integrative methodology, geared toward the formation of critical competencies, with a focus on solutions to global problems. It creates opportunities for cooperation, as well as develops multicultural and multidisciplinary competencies, social ability, and commitment. Currently, the courses at the UFMG School of Engineering are adapting to meet the new national guidelines for Engineering courses in Brazil, published in January 2019, and the GCSP is a pilot program, capable of transforming the methodology, the mentality, and the educational and technological tools it uses, into an alternative for all engineering courses at UFMG. The Program started its activities in the School of Engineering in 2020, despite the challenges and limitations imposed by the Covid-19 pandemic. The operational criteria and the internal organization were defined, and the Program was introduced to the students. The first initiatives of the Program - with broad access to engineering students and open to the participation of students from other areas - included the Call No.01/2020 - Covid-19 and the I Sustainability Workshop, held in 2021.
Thermoelectric power plants have critical units, e.g. boiler that is a complex multivariable system in itself. Such complex units present non-stationary behavior and multiple points of operation, which implies constant changes in variables' setpoints. This work presents a multivariate statistical monitoring methodology, combined with Principal Component Analysis (PCA), to detect changes in operational conditions, adapted to changing process conditions, since strict PCA technique requires stationarity that is not ensured. In addition, performance indices of regulated control loops are used to assess changes in dynamical behavior, which also indicates degradation of a boiler-unit performance. The proposed methodology was implemented in a PIMS environment, integrating a monitoring system, installed in a thermoelectric power plant used as a case study. Experimental results based on actual operational data from the case study power plant are given to illustrate results of the proposed methodology. Resumo: Usinas termelétricas (UTEs) possuem unidades críticas, como caldeira, que é uma unidade multivariável complexa em si. Tais unidades apresentam comportamento não estacionário e múltiplos pontos de operação, o que implica mudanças constantes de referências de variáveis de processo. Apresenta-se uma metodologia de monitoramento estatístico multivariado, combinado com análise de componentes principais, para detecção de mudanças nas condições operacionais, adaptada às condições do processo, uma vez que o uso destas técnicas requer estacionariedade e este não a possui. Além disso, são utilizados índices de desempenho de malhas de controle para auxiliar na indicação de mudanças no comportamento das mesmas e no rastreamento das variáveis mais influentes na degradação de desempenho da caldeira. A metodologia proposta foi implementada numa plataforma PIMS, integrando um sistema de monitoramento, instalado numa UTE usada como estudo de caso. Resultados experimentais com dados de operação da usina estudada validam a proposição e ilustram a metodologia.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.