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Your search keyword '"Ribeiro, Ana M."' showing total 540 results
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540 results on '"Ribeiro, Ana M."'

2. A nested loop for simultaneous model topology screening, parameters estimation, and identification of the optimal number of experiments: Application to a Simulated Moving Bed unit

Author

Santos, Rodrigo V. A., Rebello, Carine M., Prudente, Anderson, Santana, Vinicius V., Ribeiro, Ana M., Rodrigues, Alirio E., Loureiro, Jose M., Pontes, Karen V., and Nogueira, Idelfonso B. R.

Subjects
Physics - Chemical Physics, Statistics - Applications
Abstract

Simulated Moving Bed (SMB) chromatography is a well-known technique for the resolution of several high-value-added compounds. Parameters identification and model topology definition are arduous when one is dealing with complex systems such as a Simulated Moving Bed unit. Moreover, the large number of experiments necessary might be an expansive-long process. Hence, this work proposes a novel methodology for parameter estimation, screening the most suitable topology of the models sink-source (defined by the adsorption isotherm equation) and defining the minimum number of experiments necessary to identify the model. Therefore, a nested loop optimization problem is proposed with three levels considering the three main goals of the work: parameters estimation; topology screening by isotherm definition; minimum number of experiments necessary to yield a precise model. The proposed methodology emulated a real scenario by introducing noise in the data and using a Software-in-the-Loop (SIL) approach. Data reconciliation and uncertainty evaluation add robustness to the parameter estimation adding precision and reliability to the model. The methodology is validated considering experimental data from literature apart from the samples applied for parameter estimation, following a cross-validation. The results corroborate that it is possible to carry out trustworthy parameter estimation directly from an SMB unit with minimal system knowledge.

Published
2022

3. A new Reinforcement Learning framework to discover natural flavor molecules

Author

Queiroz, Luana P., Rebello, Carine M., Costa, Erbet A., Santana, Vinícius V., Rodrigues, Bruno C. L., Rodrigues, Alírio E., Ribeiro, Ana M., and Nogueira, Idelfonso B. R.

Subjects
Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

The flavor is the focal point in the flavor industry, which follows social tendencies and behaviors. The research and development of new flavoring agents and molecules are essential in this field. On the other hand, the development of natural flavors plays a critical role in modern society. In light of this, the present work proposes a novel framework based on Scientific Machine Learning to undertake an emerging problem in flavor engineering and industry. Therefore, this work brings an innovative methodology to design new natural flavor molecules. The molecules are evaluated regarding the synthetic accessibility, the number of atoms, and the likeness to a natural or pseudo-natural product., Comment: 24 pages, 8 figures

Published
2022

4. A Robust Scientific Machine Learning for Optimization: A Novel Robustness Theorem

Author

Queiroz, Luana P., Rebello, Carine M., Costa, Erber A., Santana, Vinicius V., Rodrigues, Alirio E., Ribeiro, Ana M., and Nogueira, Idelfonso B. R.

Subjects
Mathematics - Optimization and Control, Computer Science - Machine Learning, Statistics - Methodology
Abstract

Scientific machine learning (SciML) is a field of increasing interest in several different application fields. In an optimization context, SciML-based tools have enabled the development of more efficient optimization methods. However, implementing SciML tools for optimization must be rigorously evaluated and performed with caution. This work proposes the deductions of a robustness test that guarantees the robustness of multiobjective SciML-based optimization by showing that its results respect the universal approximator theorem. The test is applied in the framework of a novel methodology which is evaluated in a series of benchmarks illustrating its consistency. Moreover, the proposed methodology results are compared with feasible regions of rigorous optimization, which requires a significantly higher computational effort. Hence, this work provides a robustness test for guaranteed robustness in applying SciML tools in multiobjective optimization with lower computational effort than the existent alternative.

Published
2022

28. Perspectives of Scaling Up the Use of Zeolites for Selective Separations from Lab to Industry

Author

Martins, Vanessa F. D., Ribeiro, Ana M., Ferreira, Alexandre F. P., Rodrigues, Alírio E., Mingos, David Michael P., Series Editor, Cardin, Christine, Editorial Board Member, Duan, Xue, Editorial Board Member, Gade, Lutz H., Editorial Board Member, Gómez-Hortigüela Sainz, Luis, Editorial Board Member, Lu, Yi, Editorial Board Member, Macgregor, Stuart A., Editorial Board Member, Pariente, Joaquin Perez, Editorial Board Member, Schneider, Sven, Editorial Board Member, Stalke, Dietmar, Editorial Board Member, Valencia, Susana, editor, and Rey, Fernando, editor

Published
2020
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