Idelfonso B. R. Nogueira

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Associate Professor

Norwegian University of Science and Technology

Norway

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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

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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

Grant: Close

Optimization and Control of TMB, SMB and SMBR units

Open Date: 2014-11-01

Close Date: 2018-04-01

Grant: Close

Modelling and control of polymerization reactors

Open Date: 2013-05-01

Close Date: 2014-09-01

Idelfonso B. R. Nogueira

Research Interests

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About

Recent Grants

Articles (27)

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 (5)

Ana Ribeiro

Carine Menezes Rebello

Alexandre F. P. Ferreira

Sigurd Skogestad

Alírio E. Rodrigues

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