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Marco Reis

Marco P. Seabra dos Reis

Marco Paulo Seabra dos Reis

Associate Professor

Aggregation in Chemical Engineering, University of Coimbra, 2016

PhD in Chemical Engineering, University of Coimbra, 2006

Diploma in Chemical Engineering, University of Coimbra, 1995

E-mail address

Teaching activities

Quality systems engineering

Management & entrepreneurship 

Advanced modelling and simulation methodologies  

Applied statistics

Signal and Image Analysis and Processing

Process Improvement Management

Research group
Process Systems Engineering Group (GEPSI)
Research interests

Industrial Statistics (Multivariate data analysis, SPC, DOE, Measurement uncertainty and uncertainty propagation)

Process Systems Engineering (fault detection & diagnosis, process monitoring, modeling and control)

Data-Driven Multiscale Methodologies (applications in process monitoring, system identification, image analysis, pattern recognition)

Image Analysis (MIA)

Quality Improvement (Lean, Six Sigma, TOC)

Pulp and Paper Technology (Dimensional stability, Curl of paper and paperboard)

Funded research projects

“Optimal Fusion of Multiresolution Information for Soft Sensor Development, Process Monitoring and Model-Based Estimation” (PTDC/QEQ-EPS/1323/2014)

“Finding the naturally evolved design principles of prevalent metabolic circuits”  (PTDC/QUI-BIQ/119657/2010)

Development of an integrated framework for monitoring and control of complex industrial and biologic systems using advanced partial-correlation and information-theoretic approaches (PTDC/EQU-ESI/108374/2008)

Development of an unified framework for the integrated multivariate and multiscale monitoring of profiles (PTDC/EQU-ESI/108597/2008)

PILLS - "Process Intensification methodologies applied to Liquid-Liquid Systems in structured equipment"

Key publications

Reis, M.S., G. Gins, Industrial Process Monitoring in the Big Data/Industry 4.0 Era: From Detection, to Diagnosis, to Prognosis. Processes. 5(3), 35, (2017), p.1-16.

Rendall, R., A.C. Pereira, M.S. Reis, Advanced Predictive Methods for Wine Age Prediction: Part I - A Comparison Study of Single-Block Regression Approaches based on Variable Selection, Penalized Regression, Latent Variables and Tree-based Ensemble Methods. Talanta. 171 (2017), p. 341-350.

Campos, M., R. Sousa, A.C. Pereira, M.S. Reis, Advanced Predictive Methods for Wine Age Prediction: Part II - A Comparison Study of Multiblock Regression Approaches. Talanta. 171 (2017), p. 132-142.

Rato, T.J., J. Blue, J. Pinaton, M.S. Reis, Translation Invariant Multiscale Energy-based PCA for Monitoring Batch Processes in Semiconductor Manufacturing. IEEE – Transactions on Automation Science and Engineering. 14(2) (2017), p. 894-904.

Reis, M.S., R.S. Kenett, A Structured Overview on the Use of Computational Simulators for Teaching Statistical Methods. Quality Engineering. (2016), p. 1-16.

Coleman, S., R. Goeb, G. Manco, A. Pievatolo, X. Tort-Martorell, M.S. Reis, How can SMEs benefit from Big Data? Challenges and a Path Forward. Quality and Reliability Engineering International. 32(6) (2016), p. 2151-2164.

Rato, T.J., M.S. Reis, On-line process monitoring using local measures of association. Part I: Detection performance. Chemometrics and Intelligent Laboratory Systems. 142 (2015), p. 255-264.

Rato, T.J., M.S. Reis, On-line process monitoring using local measures of association. Part II: Design issues and fault diagnosis. Chemometrics and Intelligent Laboratory Systems. 142 (2015), p. 265-275.

Rato, T.J., M.S. Reis, Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR). Chemometrics and Intelligent Laboratory Systems. 125 (2013), p. 101-108. 

Reis, M.S., Network-Induced Supervised Learning: Network-Induced Classification (NI-C) and Network-Induced Regression (NI-R). AIChE Journal. 59(5) (2013), p. 1570-1587.

Theses supervision

Tiago Rato. Desenvolvimento de Plataformas Integradas para Monitorização, Análise e Controlo de Sistemas Complexos. PhD thesis, 2014.

Ricardo Rendall (PhD student). A multiresolution and megavariate platform for batch process monitoring and soft sensor development (ongoing)

Maria Campos (PhD student). Multiblock methods for handling complex and heterogeneous data structures in industry (ongoing)

Sílvia Carvalho dos Santos Rocha. Lean Seis Sigma no Turismo: O Caso da Indústria Hoteleira Portuguesa. MSc thesis, 2014.

Gonçalo Dordio Martinho de Almeida Urbano, Implementação da Metodologia Seis Sigma numa Indústria Cerâmica Portuguesa. MSc thesis, 2008.

Other information

ORCID ID: 0000-0002-4997-8865

Process Chemometrics Laboratory (

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