Idelfonso Nogueira has a Ph.D. in Chemical and Biological Engineering from the University of Porto in Portugal, a master's in Industrial Engineering from Federal University of Bahia, Brazil, and a master's in Chemical Engineering from the University of Porto in Portugal. Between 2016 and 2018, he was a visiting researcher at the Tampere University of Technology in the Automation Science and Engineering Department, Finland.

After working in the Industry in 2012, he realized that the traditional approach to Chemical Engineering would need a new standpoint to face the emerging industrial revolution. Since then, he has been actively working in the tendency of what was to be called Industry 4.0, promoting the development of Artificial Intelligence and Process Systems Engineering solutions to face modern industry challenges. To do so, he is cooperating with several universities, institutions, and companies worldwide to promote these topics while leading a team composed of 6 Ph.D. students and 5 master's students.

The research line he is promoting a new perspective in Process Systems Engineering, leading to novel technological contributions that provide a comprehensive view of modern society's complexity. He has published over 45 manuscripts in international peer-reviewed journals, two sections of books, participated as scientific committee member of several international conferences, and acted as a guest editor in several special issues. Furthermore, He has been promoting innovative teaching activities to prepare engineering students to face the Industry 4.0 challenges.


Scientific Machine Learning
Process System Engineering
Artificial Intelligence
Systems Control & Optimization
Cyber-physical Systems
Digital Twins


M.J. Regufe, V.V. Santana, A.F.P. Ferreira, A.M. Ribeiro, J.M. Loureiro, I.B.R. Nogueira. A hybrid modeling framework for membrane separation processes: Application to lithium-ion recovery from batteries. Processes, 9, 1939, 2021 C.M. Rebello, M.A.F. Martins, D.D. Santana, A.E. Rodrigues, J.M. Loureiro, A.M. Ribeiro, I.B.R. Nogueira. From a pareto front to pareto regions: A novel standpoint for multiobjective optimization. Mathematics, 9, 3152, 2021 C.M. Rebello, M.A.F. Martins, J.M. Loureiro, A.E. Rodrigues, A.M. Ribeiro, I.B.R. Nogueira. From an optimal point to an optimal region: A novel methodology for optimization of multimodal constrained problems and a novel constrained sliding particle swarm optimization strategy. Mathematics, 9, 1808, 2021 M.A.F. Martins, A.E. Rodrigues, J.M. Loureiro, A.M. Ribeiro, I.B.R. Nogueira. Artificial Intelligence-oriented economic non-linear model predictive control applied to a pressure swing adsorption unit: Syngas purification as a case study. Separation and Purification Technology, 276, 119333, 2021 V.V. Santana, M.A.F. Martins, J.M. Loureiro, A.M. Ribeiro, A.E. Rodrigues, I.B.R. Nogueira. Optimal fragrances formulation using a deep learning neural network architecture: A novel systematic approach. Computers and Chemical Engineering, 150, 107344, 2021


SiMoBedMethaSep - Simulated moving bed gas-phase for separation of methane and nitrogen. Reference: NORTE-01-0145-FEDER-029384. Funded by: FCT – Fundação para a Ciência e a Tecnologia, P2020|COMPETE - Projetos em Todos os Domínios Científicos. Starting Date: 2018/06/04. Duration: 36 months.
Idelfonso Nogueira