Vinicius Viena Santana

About

Vinícius is a passionate and dedicated professional in data science, machine learning, and process systems engineering. Initially educated in Brazil, he earned a degree in Chemical Engineering from the Federal University of Bahia (UFBA), a master's degree in Industrial Engineering from PEI UFBA, and a master's degree in Chemical Engineering from the University of Porto. His career took him to SENAI CIMATEC's High-Performance Computing Center, where he managed large datasets and applied machine learning models for wind and solar energy forecasting. He completed his Ph.D. at the University of Porto with institutional support from the MIT Portugal program under the supervision of Professor Idelfonso Nogueira (NTNU), Chris Rackauckas (Julia Lab, MIT), and Ana Mafalda Ribeiro (UPorto). His doctoral work focused on the application of data science in Industrial AI, particularly in Scientific Machine Learning (SciML) and Hybrid Modeling. Currently, he is a postdoctoral researcher at the Norwegian University of Science and Technology, focusing on Reinforcement Learning for chemical process design, specifically in the design of flowsheets for blue hydrogen production. This work is part of FME Hydrogeni, a Norwegian consortium that brings together leading universities, research centers, and industrial partners, dedicated to integrated solutions for the production, transport, and storage of hydrogen.

Research

My research integrates Process Systems Engineering with Machine Learning to enhance chemical process design and optimization. I specialize in creating hybrid models for systems governed by partial differential equations and use reinforcement learning for automated flowsheet generation. A significant part of my work involves applying scientific machine learning methods to complex engineering problems. Additionally, I have a strong interest in programming, particularly in using the Julia language for its efficiency in scientific computing.

Process Systems Engineering

AI-assisted process system synthesis for blue hydrogen production

This project aims to reduce energy costs, decrease CO2 emissions, and integrate renewable energy sources in blue hydrogen production by innovating design and operational processes with Machine Learning.

Publications

2025

Rebello, Carine de Menezes; Costa, Erbet Almeida; Abreu, Odilon Santana Luiz De; Reges, Galdir; Santana, Vinicius Viena; Mendes, Tefilo Paiva Guimares. (2025) Application of Recurrent Neural Networks for Predictive Modeling of Electrical Submersible Pumps in Oil Extraction. IFAC-PapersOnLine
Academic article

2024

Santana, Vinicius; Rebello, Carine Menezes; Queiroz, Luana P.; Ribeiro, Ana Mafalda; Shardt, Nadia; Nogueira, Idelfonso B. R.. (2024) PUFFIN: A path-unifying feed-forward interfaced network for vapor pressure prediction. Chemical Engineering Science (CES)
Academic article
Rodrigues, Bruno; Santana, Vinicius Viena; Murins, Sandris; Nogueira, Idelfonso B. R.. (2024) Molecule Generation and Optimization for Efficient Fragrance Creation. Industrial & Engineering Chemistry Research
Academic article
Costa, Erbet Almeida; Rebello, Carine de Menezes; Santana, Vinicius Viena; Nogueira, Idelfonso B. R.. (2024) Machine learning multi-step-ahead modelling with uncertainty assessment. IFAC-PapersOnLine
Academic article
Costa, Erbet Almeida; Rebello, Carine Menezes; Santana, Vinicius Viena; Reges, Galdir; Silva, Tiago de Oliveira; Abreu, Odilon Santana Luiz de. (2024) An uncertainty approach for Electric Submersible Pump modeling through Deep Neural Network. Heliyon
Academic article
Rodrigues, Bruno; Santana, Vinicius Viena; Queiroz, Luana P.; Rebello, Carine de Menezes; Nogueira, Idelfonso B. R.. (2024) Harnessing graph neural networks to craft fragrances based on consumer feedback. Computers and Chemical Engineering
Academic article

2023

Lima, Fernando; Rebello, Carine Menezes; Costa, Erbet Almeida; Santana, Vinicius; Moares, Marcellus; Barreto, Amaro. (2023) Improved modeling of crystallization processes by Universal Differential Equations. Chemical engineering research & design
Academic article
Costa, Erbet Almeida; Rebello, Carine de Menezes; Santana, Vinicius Viena; Nogueira, Idelfonso B. R.. (2023) Physics-informed neural network uncertainty assessment through Bayesian inference. IFAC-PapersOnLine
Academic article

Journal publications

Rebello, Carine de Menezes; Costa, Erbet Almeida; Abreu, Odilon Santana Luiz De; Reges, Galdir; Santana, Vinicius Viena; Mendes, Tefilo Paiva Guimares. (2025) Application of Recurrent Neural Networks for Predictive Modeling of Electrical Submersible Pumps in Oil Extraction. IFAC-PapersOnLine
Academic article
Santana, Vinicius; Rebello, Carine Menezes; Queiroz, Luana P.; Ribeiro, Ana Mafalda; Shardt, Nadia; Nogueira, Idelfonso B. R.. (2024) PUFFIN: A path-unifying feed-forward interfaced network for vapor pressure prediction. Chemical Engineering Science (CES)
Academic article
Rodrigues, Bruno; Santana, Vinicius Viena; Murins, Sandris; Nogueira, Idelfonso B. R.. (2024) Molecule Generation and Optimization for Efficient Fragrance Creation. Industrial & Engineering Chemistry Research
Academic article
Costa, Erbet Almeida; Rebello, Carine de Menezes; Santana, Vinicius Viena; Nogueira, Idelfonso B. R.. (2024) Machine learning multi-step-ahead modelling with uncertainty assessment. IFAC-PapersOnLine
Academic article
Costa, Erbet Almeida; Rebello, Carine Menezes; Santana, Vinicius Viena; Reges, Galdir; Silva, Tiago de Oliveira; Abreu, Odilon Santana Luiz de. (2024) An uncertainty approach for Electric Submersible Pump modeling through Deep Neural Network. Heliyon
Academic article
Rodrigues, Bruno; Santana, Vinicius Viena; Queiroz, Luana P.; Rebello, Carine de Menezes; Nogueira, Idelfonso B. R.. (2024) Harnessing graph neural networks to craft fragrances based on consumer feedback. Computers and Chemical Engineering
Academic article
Lima, Fernando; Rebello, Carine Menezes; Costa, Erbet Almeida; Santana, Vinicius; Moares, Marcellus; Barreto, Amaro. (2023) Improved modeling of crystallization processes by Universal Differential Equations. Chemical engineering research & design
Academic article
Costa, Erbet Almeida; Rebello, Carine de Menezes; Santana, Vinicius Viena; Nogueira, Idelfonso B. R.. (2023) Physics-informed neural network uncertainty assessment through Bayesian inference. IFAC-PapersOnLine
Academic article

Teaching

Courses

Supervision

* 2023 - 2024:

- June Marie Berge (Supervisor)

- Aman Sant Singh Basra (Supervisor)

- Jenny Steen - Hansen (Co-Supervisor)

- Kristoffer Elstad Hansen (Co-supervisor)

- Alma Maria Thindberg, Ane-Kristine Kjølner, Johanne Nordheim Tveter, Marie Hahn Næss (Co-supervisor) - TKP4170 Process Design Project (1st place in poster award)

Språkvelger

Vinicius Viena Santana