About
8
Publications
1,659
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35
Citations
Introduction
Mainly focused on Process Control Systems, Fault Detection and Diagnosis methods and Machine Learning techniques.
Skills and Expertise
Additional affiliations
October 2020 - present
Position
- PostDoc Position
Description
- Main Project: Machine Learning for Simultaneous Fault Detection and Diagnosis in Chemical Processes Ongoing Research Activities: ● Development of fault prediction models for the Tenessee Eastman Process Benchmark using Python and different machine learning methods such as ANN, Random Forest, SVM and LR.
February 2018 - December 2019
Position
- Lecturer
Description
- Main Activities: ● Teaching focused on Industry 4.0. ● Part of the development team of the teaching tool “Virtual and augmented reality applications for pilot unit operations”. ● Supervised undergraduate student’s project “Remote monitoring in PPE using Arduino”. ● Content production for the Process Safety graduation course. ● Teaching topics: Mass Balance, Modelling, Dynamics and Process Control, Fluid Mechanics, Industrial Instrumentation, Chemical Engineering Laboratory.
January 2014 - January 2016
Position
- Lecturer
Description
- Main Activities: ● Undergraduate students’ final project supervision. ● Teaching topics: Process Instrumentation and Control, Process Automation, Chemical Engineering Laboratory.
Education
May 2019 - August 2020
Field of study
- Education/ Teaching in Professional and Technological Education
September 2014 - July 2019
September 2012 - September 2014
Publications
Publications (8)
The temperature behavior in very high cycle fatigue (VHCF) testing as well as the influence of the intermittent loading is not completely understood. In many cases the high frequency causes the specimens to heat up and may interfere in the material's fatigue performance. In order to address this issue, this study proposed an experimental test with...
Due to a typesetting error the second name of the fourth author was wrong.
Soft sensors with real time prediction capabilities appear as a profitable solution for hard-to-measure variables whenever hard sensors are difficult to apply or subjected to high operational costs. Nonetheless, the use of soft sensors within industrial applications is still not widespread because of the systematic accuracy issues that can be intro...
Industrial archived process data represent a convenient source of information for data-driven models, such as artificial neural network (ANN), that can be used for safety and efficiency improvement like early or even predictive fault detection and diagnosis (FDD). Nonetheless, most of the data used for model generation are representative of the pro...
Este trabalho apresenta uma revisão da literatura acerca dos níveis de automação da indústria de processos dos anos 1940 até os dias de hoje. Sua motivação é destacar o papel da automação de processos para fins de confiabilidade, segurança e eficiência das operações por meio de uma perspectiva histórica. Ele também contempla a definição das novas t...
This study aimed at implementing an enhanced control strategy in a semi industrial boiler concerning the pollutants emission management in order to minimize operational risks due to sensor failures. The proposed control strategy is fault tolerant and it uses failure diagnosis systems in association with closed-loop online monitoring. The current wo...
