M. Reis

Cláudia Silva, Helena Bigares Grangeia, Sérgio Paulo Simões, 2019, European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V.

Predictive analytics in the petrochemical industry: Research Octane Number (RON) forecasting and analysis in an industrial catalytic reforming unit

Pedro M. Saraiva, Rodolfo Oliveira, Tiago Dias, 2020, Comput. Chem. Eng..

PAT soft sensors for wide range prediction of key properties of diesel fuels and blending components for the oil industry

Marco S. Reis, Daniela C. M. de Souza, Luís Cabrita, 2021, Computers and Chemical Engineering.

Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis

Geert Gins, Marco S. Reis, M. Reis, 2017 .

A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part I—Assessing Detection Strength

Pedro M. Saraiva, Tiago J. Rato, Marco S. Reis, 2016 .

Recent trends on hybrid modeling for Industry 4.0

Marco S. Reis, Leo H. Chiang, Ivan Castillo, 2021, Comput. Chem. Eng..

Data-Driven Process System Engineering-Contributions to its consolidation following the path laid down by George Stephanopoulos

P. Saraiva, M. Reis, 2022, Comput. Chem. Eng..

A multiscale empirical modeling framework for system identification

Marco S. Reis, M. Reis, 2009 .

A structured overview on the use of computational simulators for teaching statistical methods

Ron S. Kenett, Marco S. Reis, M. Reis, 2017 .

Wide spectrum feature selection (WiSe) for regression model building

Marco S. Reis, Leo H. Chiang, Ivan Castillo, 2019, Comput. Chem. Eng..

Data-centric process systems engineering: A push towards PSE 4.0

Pedro M. Saraiva, Marco S. Reis, P. Saraiva, 2021, Comput. Chem. Eng..

Image-based manufacturing analytics: Improving the accuracy of an industrial pellet classification system using deep neural networks

Bo Lu, Marco S. Reis, Ricardo Rendall, 2018, Chemometrics and Intelligent Laboratory Systems.

Definitive Screening Designs and latent variable modelling for the optimization of solid phase microextraction (SPME): Case study - Quantification of volatile fatty acids in wines

Marco S. Reis, José C. Marques, Ana C. Pereira, 2018, Chemometrics and Intelligent Laboratory Systems.

Discussion of “Industrial statistics and manifold data”

Marco S. Reis, M. Reis, 2020, Quality Engineering.

Madeira wine ageing prediction based on different analytical techniques: UV–vis, GC-MS, HPLC-DAD

Marco S. Reis, José C. Marques, Pedro Saraiva, 2011 .

Forecasting the research octane number in a Continuous Catalyst Regeneration (CCR) reformer

Tiago Dias, Rodolfo Oliveira, Pedro Saraiva, 2021, Qual. Reliab. Eng. Int..

Which regression method to use? Making informed decisions in “data-rich/knowledge poor” scenarios – The Predictive Analytics Comparison framework (PAC)

Marco S. Reis, Ricardo Rendall, Ricardo R. Rendall, 2018, Chemometrics and Intelligent Laboratory Systems.

A data-driven approach for the study of coagulation phenomena in waste lubricant oils and its relevance in alkaline regeneration treatments.

M. Quina, C. Pinheiro, L. Gando-Ferreira, 2017, The Science of the total environment.

Parameter selection guidelines for adaptive PCA‐based control charts

Mia Hubert, Tiago J. Rato, Eric Schmitt, 2016 .

Data preprocessing for multiblock modelling – A systematization with new methods

Marco S. Reis, Maria P. Campos, M. Reis, 2020 .

Prediction of Sugar Content in Port Wine Vintage Grapes Using Machine Learning and Hyperspectral Imaging

Francisco Rovira-Más, Pedro Melo-Pinto, Marco S. Reis, 2021, Processes.

SS-DAC: A systematic framework for selecting the best modeling approach and pre-processing for spectroscopic data

Tiago J. Rato, Marco S. Reis, M. Reis, 2019, Comput. Chem. Eng..

Multiresponse and multiobjective latent variable optimization of modern analytical instrumentation for the quantification of chemically related families of compounds: Case study—Solid‐phase microextraction (SPME) applied to the quantification of analytes with impact on wine aroma

Marco S. Reis, José C. Marques, Ana C. Pereira, 2018, Journal of Chemometrics.

Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR)

Tiago J. Rato, Marco S. Reis, M. Reis, 2013 .

First Principles Statistical Process Monitoring of High-Dimensional Industrial Microelectronics Assembly Processes

Tiago J. Rato, Marco S. Reis, Cristina Martins, 2020, Processes.

Incorporation of process-specific structure in statistical process monitoring: A review

Geert Gins, Tiago J. Rato, Marco S. Reis, 2019, Journal of Quality Technology.

Markovian and Non-Markovian sensitivity enhancing transformations for process monitoring

Tiago J. Rato, Marco S. Reis, M. Reis, 2017 .

Improved Fault Diagnosis in Online Process Monitoring of Complex Networked Processes: a Data-Driven Approach

Tiago J. Rato, Marco S. Reis, M. Reis, 2017 .

On-line process monitoring using local measures of association. Part II: Design issues and fault diagnosis

Tiago J. Rato, Marco S. Reis, M. Reis, 2015 .

Data‐Centric Process Systems Engineering for the Chemical Industry 4.0

Pedro M. Saraiva, Marco S. Reis, P. Saraiva, 2019 .

Discussion: Process data streams aggregation versus product samples aggregation

Marco S. Reis, M. Reis, 2019, Journal of Quality Technology.

How Can SMEs Benefit from Big Data? Challenges and a Path Forward

Shirley Coleman, Xavier Tort-Martorell, Rainer Göb, 2016, Qual. Reliab. Eng. Int..

Persistence Landscapes—Implementing a Dataset Verification Method in Resource-Scarce Embedded Systems

M. Reis, Sérgio Branco, João G. Carvalho, 2023, Computers.

Statistical monitoring of control loops performance: an improved historical‐data benchmark index

Tiago J. Rato, Marco S. Reis, M. Reis, 2010, Qual. Reliab. Eng. Int..

Data-driven methods for batch data analysis - A critical overview and mapping on the complexity scale

Marco S. Reis, Leo H. Chiang, Ricardo R. Rendall, 2019, Comput. Chem. Eng..

A Unifying and Integrated Framework for Feature Oriented Analysis of Batch Processes

Bo Lu, Marco S. Reis, Ricardo Rendall, 2017 .

Development of Generalized Platforms for the Analysis of Complex Datasets

Pedro M. Saraiva, Marco S. Reis, Véronique M. Gomes, 2012, Qual. Reliab. Eng. Int..

A Spectral AutoML approach for industrial soft sensor development: Validation in an oil refinery plant

Tiago J. Rato, Marco S. Reis, Daniela C. M. de Souza, 2021, Comput. Chem. Eng..

Multiresolution interval partial least squares: A framework for waveband selection and resolution optimization

Tiago J. Rato, Marco S. Reis, M. Reis, 2019, Chemometrics and Intelligent Laboratory Systems.

Finding the optimal time resolution for batch-end quality prediction: MRQP – A framework for multi-resolution quality prediction

Geert Gins, Jan Van Impe, Marco S. Reis, 2018 .

Platforms for Automatic PAT Soft Sensor Development and Analysis

Tiago J. Rato, M. Reis, 2020 .

A comparison of advanced regression techniques for predicting ship CO2 emissions

Biagio Palumbo, Marco S. Reis, Antonio Lepore, 2017, Qual. Reliab. Eng. Int..

Multiscale and Multi-Granularity Process Analytics: A Review

Marco S. Reis, M. Reis, 2019, Processes.

Network‐induced supervised learning: Network‐induced classification (NI‐C) and network‐induced regression (NI‐R)

Marco S. Reis, M. Reis, 2013 .

Integration of data uncertainty in linear regression and process optimization

Pedro M. Saraiva, Marco S. Reis, P. Saraiva, 2005 .

Predicting the Lifetime of Lithium-Ion Batteries: Integrated feature extraction and modeling through sequential Unsupervised-Supervised Projections (USP)

Benben Jiang, M. Reis, 2022, Chemical Engineering Science.

Identifying Strong Statistical Bias in the Local Structure of Metabolic Networks - The Metabolic Network of Saccharomyces Cerevisiae as a Test Case

Pedro Martins, Marco S. Reis, Armindo Salvador, 2015, BIOINFORMATICS.

Advanced predictive methods for wine age prediction: Part II - A comparison study of multiblock regression approaches.

Ana C Pereira, Marco S Reis, Ana C. Pereira, 2017, Talanta.

Multirate fusion of data sources with different quality

Leo H. Chiang, Ricardo R. Rendall, M. Reis, 2020 .

A Comparison Study of Single‐Scale and Multiscale Approaches for Data‐Driven and Model‐Based Online Denoising

Marco S. Reis, Ricardo R. Rendall, M. Reis, 2014, Qual. Reliab. Eng. Int..

A systematic comparison of PCA-based statistical process monitoring methods for high-dimensional, time-dependent processes

Bart De Ketelaere, Mia Hubert, Tiago J. Rato, 2016 .

A systematic PAT Soft Sensor screening and development methodology applied to the prediction of free fatty acids in industrial biodiesel production

Tiago J. Rato, Marco S. Reis, Diogo M.G. Neves, 2020 .

Building Optimal Multiresolution Soft Sensors for Continuous Processes

Tiago J. Rato, Marco S. Reis, M. Reis, 2018 .

A comparative study of linear regression methods in noisy environments

Pedro M. Saraiva, Marco S. Reis, P. Saraiva, 2004 .

Bayesian predictive optimization of multiple and profile response systems in the process industry: A review and extensions

Marco S. Reis, Enrique Del Castillo, E. Castillo, 2020 .

Predicting ships' CO 2 emissions using feature‐oriented methods

Biagio Palumbo, Marco S. Reis, Ricardo Rendall, 2019, Applied Stochastic Models in Business and Industry.

Bayesian Optimization for automatic tuning of digital multi-loop PID controllers

Lino O. Santos, M. Reis, João P.L. Coutinho, 2023, Computers and Chemical Engineering.

A physics-informed Run-to-Run control framework for semiconductor manufacturing

Jacques Pinaton, Jakey Blue, Marco S. Reis, 2020, Expert Syst. Appl..

Multiscale statistical process control using wavelet packets

Pedro M. Saraiva, Bhavik R. Bakshi, Marco S. Reis, 2008 .

Multiscale Statistical Process Control of Paper Surface Profiles

Pedro M. Saraiva, Marco S. Reis, P. Saraiva, 2006 .

The Challenges of PCA-Based Statistical Process Monitoring: An Overview and Solutions

Bart De Ketelaere, Tiago J. Rato, Marco S. Reis, 2014 .

Improving the sensitivity of statistical process monitoring of manifolds embedded in high-dimensional spaces: The truncated-Q statistic

Tiago J. Rato, Marco S. Reis, Ricardo Rendall, 2021, Chemometrics and Intelligent Laboratory Systems.

Mechanistic Modeling and Simulation for Process Data Generation

Marco S. Reis, Andrey Romanenko, Natércia C. P. Fernandes, 2019, Industrial & Engineering Chemistry Research.

Multiscale statistical process control with multiresolution data

Pedro M. Saraiva, Marco S. Reis, P. Saraiva, 2006 .

Incorporating Systems Structure in Data-Driven High-Dimensional Predictive Modeling

Marco S. Reis, M. Reis, 2018 .

An integrated multiresolution framework for quality prediction and process monitoring in batch processes

Tiago J. Rato, Marco S. Reis, M. Reis, 2020, Journal of Manufacturing Systems.

Data Quality and Denoising: A Review

Pedro M. Saraiva, Bhavik R. Bakshi, Marco S. Reis, 2020 .