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Idelfonso B. R. Nogueira

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Associate Professor

Norwegian University of Science and Technology

Norway

Research Interests

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Idelfonso B. R. Nogueira is an Associate Professor at the Norwegian University of Science and Technology in Norway. His research areas encompass machine learning applications in chemical engineering, solid-state battery modeling, and process control. Recent publications include work on digital twins, the relationship between diffusion coefficients and ion conductivity in electrolytes, and frameworks for predicting product distribution in biomass pyrolysis.

Recent Grants

Grant: Open

A smart process for water harvesting from air and water distribution based on eco-friendly MOFs. Funding: RCN/Horizon Europe.

Open Date: 2024-04-01

Close Date: 2027-03-31

Grant: Open

Climate Changes and Scent Heritage: The Urgent Need for Capturing and Preserving Olfactory Landscapes in a Changing World

Open Date: 2024-01-01

Close Date: 2027-01-01

Grant: Close

Gas simulated moving bed technology for methane and nitrogen separation

Open Date: 2018-11-01

Close Date:

Grant: Close

Development of package process of reaction and separation in refinery and petrochemical industry.

Open Date: 2018-09-01

Close Date:

Grant: Close

Optimization and control of TMB, SMB and SMBR units and production of a green solvent

Open Date: 2017-01-01

Close Date: 2018-01-01

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Collaborators (11)

Lucília S Ribeiro

Assistant Professor

University of Porto

PORTUGAL

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Ana Ribeiro

Associate Professor

Universidade do Porto Faculdade de Engenharia

PORTUGAL

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Carine Menezes Rebello

NORWAY

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Alexandre F. P. Ferreira

RESEARCH ASSOCIATE

University of Porto

PORTUGAL

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Diogo Rodrigues

Assistant Professor

Universidade do Porto Faculdade de Engenharia

PORTUGAL

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Sigurd Skogestad

Professor

Norwegian University of Science and Technology

NORWAY

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Alírio E. Rodrigues

University of Porto

PORTUGAL

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Ivaldo Itabaiana

Full Professor

FRANCE

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Mumin Enis Leblebici

Assistant Professor

KU Leuven

BELGIUM

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Florence Vermeire

Assistant Professor

KU Leuven

BELGIUM

View details

Nadia Shardt

Associate Professor

Norwegian University of Science and Technology

NORWAY

View details

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Idelfonso B. R. Nogueira

Research Interests

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About

Recent Grants

Articles (47)

FRaPPÉ: Fragrance Raw Materials Performance Prediction Explained

Development of Symbolic Regression-Based ATR-FTIR Calibration Models

SMILE: Smell Maximisation In Low-cost Eau de parfum

Targeted Olfactory Molecule Generation for Vanilla Scents Using Generative Flow Networks

Symbolic Regression-Based Robust Control of Batch Crystallization: Design and PLC Implementation

A robust framework based on symbolic regression to develop controllers for crystallization processes

ExPUFFIN: Thermodynamic consistent viscosity prediction in an Extended Path-Unifying Feed-Forward Interfaced Network

Improving process systems engineering with specialized multi-agent large language models

Augmented reality in architectural heritage preservation: a comprehensive digitization approach validated in Salvador’s Pelourinho district – Brazil

Robust Min–Max MPC with Symbolic Regression Ensembles: Constraint Satisfaction for Uncertain Nonlinear Process Control

Offset-free Lyapunov-based stabilising robust nonlinear control using data-driven model: A nonlinear multi-model computationally efficient approach

Simultaneous material selection and process optimisation for multi-material PSA beds applied to post-combustion CO2 capture — When does adsorbent layering help?

Water Consumption in Hydrogen Production Through Electrolysis: Overview, State‐of‐the‐Art, and Future Trends

Comparative Real-Time Implementation of Standard and Input-Target MPC for ESP-Based Artificial Lift Systems in a Legacy Automation Architecture

RTO and advanced process control for electric submersible pumps using surrogate models

Particle Swarm Optimization for simultaneous design and optimization of heat pumps considering Mixed Integer problems

Virtual Plant–Model Pair as a Step Towards Real-Time Optimization of a Simulated Moving Bed System

Parameter estimation of a size-exclusion simulated moving bed for protein purification via meta-heuristic methods with estimability analysis and uncertainty evaluation

Modeling Slug Flow Dynamics in Offshore Wells using Universal Differential Equations

MAASC — Multi-Agentic Architecture Specialised for Control

The relationship between diffusion coefficient and ion conductivity in solid-state electrolytes using feedforward neural networks and symbolic regression

Machine learning framework to predict product distribution of lignocellulosic biomass pyrolysis

Digital twins in chemical engineering: An integrated framework for identification, implementation, online learning, and uncertainty assessment

Interpretable and uncertainty-aware machine learning for trustworthy prediction in batch crystallization

Simple control structure for stabilizing Core Annular Flow operation in heavy oil transportation

Adaptive digital twin for pressure swing adsorption systems: Integrating a novel feedback tracking system, online learning and uncertainty assessment for enhanced performance

Harnessing graph neural networks to craft fragrances based on consumer feedback

Optimizing CO<sub>2</sub> capture in pressure swing adsorption units: A deep neural network approach with optimality evaluation and operating maps for decision-making

PUFFIN: A path-unifying feed-forward interfaced network for vapor pressure prediction

Molecule Generation and Optimization for Efficient Fragrance Creation

A PLC-Embedded Implementation of a Modified Takagi–Sugeno–Kang-Based MPC to Control a Pressure Swing Adsorption Process

Ethylene Purification by Pressure Swing Adsorption with the Paraffin Selective Metal–Organic Framework─DUT-8

An integrated approach for parameter estimation of a propyl propionate synthesis model

Digital Transformation in the Chemical Industry: The Potential of Augmented Reality and Digital Twin

Introducing a new model for solid-state batteries: Parameter estimation and sensitivity analysis on diffusion, concentration, and electrochemical kinetics

Interpretable Scientific Machine Learning Approach for Correcting Phenomenological Models: Methodology Validation on an ESP Prototype

Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach

Process modeling and optimization of vacuum pressure swing adsorption for ethane and ethylene separation using Cu(Qc)2 MOF

Transfer Learning Approach to Develop Natural Molecules with Specific Flavor Requirements

Metaheuristic Framework for Material Screening and Operating Optimization of Adsorption-Based Heat Pumps

A performance indicator for the screening of adsorbent/desorbent pairs for gas‐phase simulated moving bed applications

Generating Flavor Molecules Using Scientific Machine Learning

A Reinforcement Learning Framework to Discover Natural Flavor Molecules

Novel Framework for Simulated Moving Bed Reactor Optimization Based on Deep Neural Network Models and Metaheuristic Optimizers: An Approach with Optimality Guarantee

Handling model uncertainty in control of a pressure swing adsorption unit for syngas purification: A multi-model zone control scheme-based robust model predictive control strategy

Improved modeling of crystallization processes by Universal Differential Equations

A Robust Learning Methodology for Uncertainty-Aware Scientific Machine Learning Models

Collaborators (11)

Lucília S Ribeiro

Ana Ribeiro

Carine Menezes Rebello

Alexandre F. P. Ferreira

Diogo Rodrigues

Sigurd Skogestad

Alírio E. Rodrigues

Ivaldo Itabaiana

Mumin Enis Leblebici

Florence Vermeire

Nadia Shardt

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