Diego Queiroz Faria de Menezes
@portal.peq.coppe.ufrj.br
PEQ/COPPE/UFRJ and ISI B&F
COPPETEC and SENAI CETIQT
Currently working in the Chemical Engineering Program at UFRJ/COPPETEC and SENAI CETIQT as a researcher. Have research interests in data reconciliation, parameter estimation, robust statistics, time series analysis, distillation, membrane process separation, simulation, modeling, and process optimization. Working as a researcher in the development of monitoring techniques, identification, and diagnosis of failures with an industrial process.
EDUCATION
D.Sc. in Chemical Engineering
RESEARCH, TEACHING, or OTHER INTERESTS
Chemical Engineering, Statistics, Probability and Uncertainty, Filtration and Separation, Process Chemistry and Technology
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Gildeir L. Rabello, Gabriel M.P. Andrade, Diego Q.F. de Menezes, Rafael M. Soares, Tiago S.M. Lemos, Leonardo D. Ribeiro, Bruno F. Vieira, and José Carlos Pinto
Elsevier BV
Gabriel M.P. Andrade, Diego Q.F. de Menezes, Rafael M. Soares, Tiago S.M. Lemos, Alex F. Teixeira, Leonardo D. Ribeiro, Bruno F. Vieira, and José Carlos Pinto
Elsevier BV
Tahyná B. Fontoura, Marília Caroline C. de Sá, Diego Q. Faria de Menezes, Bruno Francisco Oechsler, Afrânio Melo, Luiz Felipe de O. Campos, Thiago K. Anzai, Fábio C. Diehl, Pedro H. Thompson, and José Carlos Pinto
Elsevier BV
D.Q.F. de Menezes, D.M. Prata, A.R. Secchi, and J.C. Pinto
Elsevier BV
Ana Carolina S. Dias, Marília Caroline C. De Sá, Tahyná B. Fontoura, Diego Q. Menezes, Thiago K. Anzai, Fábio C. Diehl, Pedro H. Thompson, and José Carlos Pinto
Elsevier BV
Diego Queiroz Faria de Menezes, Marília Caroline Cavalcante de Sá, Tahyná Barbalho Fontoura, Thiago Koichi Anzai, Fabio Cesar Diehl, Pedro Henrique Thompson, and Jose Carlos Pinto
MDPI AG
The present work presents a methodology based on data reconciliation to monitor membrane separation processes reliably, online and in real time for the first time. The proposed methodology was implemented in accordance with the following steps: data acquisition; data pre-treatment; data characterization; data reconciliation; gross error detection; and critical evaluation of measured data with a soft sensor. The acquisition of data constituted the slowest stage of the monitoring process, as expected in real-time applications. The pre-treatment stage was fundamental to assure the robustness of the code and the initial characterization of collected data was carried out offline. The characterization of the data showed that steady-state modeling of the process would be appropriate, also allowing the implementation of faster numerical procedures for the data reconciliation step. The data reconciliation step performed well, quickly and consistently. Thus, data reconciliation allowed the estimation of unmeasured variables, playing the role of a soft sensor and allowing the future installation of a digital twin. Additionally, monitoring of measurement bias constituted a tool for measurement diagnosis. As shown in the manuscript, the proposed methodology can be successfully implemented online and in real time for monitoring of membrane separation processes, as shown through a real dashboard web application developed for monitoring of an actual industrial site.
Patrick V. Mangili, Yuri P.D.M. Souza, Diego Q.F. de Menezes, Lizandro S. Santos, and Diego M. Prata
Elsevier BV