Works matching AU Nogueira, Idelfonso B. R.
Results: 14
From a Pareto Front to Pareto Regions: A Novel Standpoint for Multiobjective Optimization.
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- Mathematics (2227-7390), 2021, v. 9, n. 24, p. 3152, doi. 10.3390/math9243152
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- Article
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.
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- Mathematics (2227-7390), 2021, v. 9, n. 15, p. 1808, doi. 10.3390/math9151808
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- Article
A First Approach towards Adsorption-Oriented Physics-Informed Neural Networks: Monoclonal Antibody Adsorption Performance on an Ion-Exchange Column as a Case Study.
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- ChemEngineering, 2022, v. 6, n. 2, p. 21, doi. 10.3390/chemengineering6020021
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A Reinforcement Learning Framework to Discover Natural Flavor Molecules.
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- Foods, 2023, v. 12, n. 6, p. 1147, doi. 10.3390/foods12061147
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- Article
A performance indicator for the screening of adsorbent/desorbent pairs for gas‐phase simulated moving bed applications.
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- AIChE Journal, 2023, v. 69, n. 6, p. 1, doi. 10.1002/aic.18074
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Enantiomers and Their Resolution.
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- Encyclopedia, 2022, v. 2, n. 1, p. 151, doi. 10.3390/encyclopedia2010011
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Using scientific machine learning to develop universal differential equation for multicomponent adsorption separation systems.
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- Canadian Journal of Chemical Engineering, 2022, v. 100, n. 9, p. 2279, doi. 10.1002/cjce.24495
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Artificial Intelligence and Cyber-Physical Systems: A Review and Perspectives for the Future in the Chemical Industry.
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- AI, 2021, v. 2, n. 3, p. 429, doi. 10.3390/ai2030027
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A PLC-Embedded Implementation of a Modified Takagi–Sugeno–Kang-Based MPC to Control a Pressure Swing Adsorption Process.
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- Processes, 2024, v. 12, n. 8, p. 1738, doi. 10.3390/pr12081738
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Mapping Uncertainties of Soft-Sensors Based on Deep Feedforward Neural Networks through a Novel Monte Carlo Uncertainties Training Process.
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- Processes, 2022, v. 10, n. 2, p. 409, doi. 10.3390/pr10020409
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Machine Learning-Based Dynamic Modeling for Process Engineering Applications: A Guideline for Simulation and Prediction from Perceptron to Deep Learning.
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- Processes, 2022, v. 10, n. 2, p. 250, doi. 10.3390/pr10020250
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A Hybrid Modeling Framework for Membrane Separation Processes: Application to Lithium-Ion Recovery from Batteries.
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- Processes, 2021, v. 9, n. 11, p. 1939, doi. 10.3390/pr9111939
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A Complete Heterogeneous Model for the Production of n-Propyl Propionate Using a Simulated Moving Bed Reactor.
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- Separations (2297-8739), 2022, v. 9, n. 2, p. N.PAG, doi. 10.3390/separations9020043
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Digital Transformation in the Chemical Industry: The Potential of Augmented Reality and Digital Twin.
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- Applied Sciences (2076-3417), 2024, v. 14, n. 24, p. 11607, doi. 10.3390/app142411607
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- Article