Ana M. Ribeiro

5.2k total citations
158 papers, 4.1k citations indexed

About

Ana M. Ribeiro is a scholar working on Mechanical Engineering, Biomedical Engineering and Materials Chemistry. According to data from OpenAlex, Ana M. Ribeiro has authored 158 papers receiving a total of 4.1k indexed citations (citations by other indexed papers that have themselves been cited), including 96 papers in Mechanical Engineering, 52 papers in Biomedical Engineering and 40 papers in Materials Chemistry. Recurrent topics in Ana M. Ribeiro's work include Carbon Dioxide Capture Technologies (69 papers), Membrane Separation and Gas Transport (47 papers) and Catalytic Processes in Materials Science (26 papers). Ana M. Ribeiro is often cited by papers focused on Carbon Dioxide Capture Technologies (69 papers), Membrane Separation and Gas Transport (47 papers) and Catalytic Processes in Materials Science (26 papers). Ana M. Ribeiro collaborates with scholars based in Portugal, Brazil and South Korea. Ana M. Ribeiro's co-authors include Alírio E. Rodrigues‬, José M. Loureiro, Alexandre Ferreira, Carlos A. Grande, Idelfonso B. R. Nogueira, Filipe V. S. Lopes, Maria João Regufe, João C. Santos, Jong‐San Chang and Nuno S. Graça and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Catalysis B: Environmental and Chemical Engineering Journal.

In The Last Decade

Ana M. Ribeiro

155 papers receiving 4.0k citations

Peers

Ana M. Ribeiro

CATAL

Johan Wärnå Finland

Shohreh Fatemi Iran

José M. Loureiro Portugal

Seyed Mojtaba Sadrameli Iran

Xiaoxun Ma China

Carlos A. Grande Portugal

Saeed Shirazian Iran

Min Liu China

Hong Li China

Zheng‐Hong Luo China

Johan Wärnå Finland

Ana M. Ribeiro

1.6k ×0.6

600 ×0.5

1.4k ×1.2

2.3k ×2.0

703 ×1.2

CATAL

Citations per year, relative to Ana M. Ribeiro Ana M. Ribeiro (= 1×) peers Johan Wärnå

Countries citing papers authored by Ana M. Ribeiro

Since Specialization

Citations

This map shows the geographic impact of Ana M. Ribeiro's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ana M. Ribeiro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ana M. Ribeiro more than expected).

Fields of papers citing papers by Ana M. Ribeiro

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ana M. Ribeiro. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ana M. Ribeiro. The network helps show where Ana M. Ribeiro may publish in the future.

Co-authorship network of co-authors of Ana M. Ribeiro

This figure shows the co-authorship network connecting the top 25 collaborators of Ana M. Ribeiro. A scholar is included among the top collaborators of Ana M. Ribeiro based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ana M. Ribeiro. Ana M. Ribeiro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Ribeiro, Ana M., et al.. (2024). A PLC-Embedded Implementation of a Modified Takagi–Sugeno–Kang-Based MPC to Control a Pressure Swing Adsorption Process. Processes. 12(8). 1738–1738.

Cho, Kyung Ho, Ji Woong Yoon, U‐Hwang Lee, et al.. (2024). Multi-objective optimization of ANN-based vacuum pressure swing adsorption process for ethane and ethylene separation. Journal of Industrial and Engineering Chemistry. 143. 221–239. 3 indexed citations

Regufe, Maria João, et al.. (2024). Methane upgrading on pelletised Maxsorb activated carbon by gas-phase simulated moving bed. Adsorption. 31(1). 1 indexed citations

Queiroz, Luciano Paganucci de, Idelfonso B. R. Nogueira, & Ana M. Ribeiro. (2024). Flavor Engineering: A comprehensive review of biological foundations, AI integration, industrial development, and socio-cultural dynamics. Food Research International. 196. 115100–115100. 21 indexed citations

Cho, Kyung Ho, Ji Woong Yoon, Alexandre Ferreira, et al.. (2023). Process modeling and optimization of vacuum pressure swing adsorption for ethane and ethylene separation using Cu(Qc)2 MOF. Separation and Purification Technology. 326. 124711–124711. 5 indexed citations

Nogueira, Idelfonso B. R., et al.. (2023). A performance indicator for the screening of adsorbent/desorbent pairs for gas‐phase simulated moving bed applications. AIChE Journal. 69(6). 3 indexed citations

Luna‐Triguero, Azahara, José Manuel Vicent‐Luna, Mihalis N. Tsampas, et al.. (2023). An Efficient Strategy for Electroreduction Reactor Outlet Fractioning into Valuable Products. Industrial & Engineering Chemistry Research. 62(22). 8847–8863. 3 indexed citations

Regufe, Maria João, et al.. (2023). Propane and Propylene Separation with Carbon Dioxide at Mild Temperatures by Gas-Phase Simulated Moving Bed in Binderfree Zeolite 13X. Industrial & Engineering Chemistry Research. 62(32). 12600–12612. 2 indexed citations

Rodrigues‬, Alírio E., et al.. (2023). A framework for predicting odor threshold values of perfumes by scientific machine learning and transfer learning. Heliyon. 9(10). e20813–e20813. 4 indexed citations

10.

Rebello, Carine Menezes, et al.. (2023). Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach. Chemical Engineering Science. 282. 119223–119223. 10 indexed citations

11.

Rebello, Carine Menezes, et al.. (2023). A Reinforcement Learning Framework to Discover Natural Flavor Molecules. Foods. 12(6). 1147–1147. 15 indexed citations

12.

Martins, Márcio A.F., José M. Loureiro, Ana M. Ribeiro, et al.. (2023). Novel Framework for Simulated Moving Bed Reactor Optimization Based on Deep Neural Network Models and Metaheuristic Optimizers: An Approach with Optimality Guarantee. ACS Omega. 8(7). 6463–6475. 2 indexed citations

13.

Nogueira, Idelfonso B. R., et al.. (2022). A novel optimization approach for material screening in the adsorption process separation context based on rigorous models: Separation of a mixture of N2 and CO2 in a Pressure Swing Adsorption Unit as a case study. IFAC-PapersOnLine. 55(20). 211–216. 1 indexed citations

14.

Ferreira, Alexandre, et al.. (2022). Gas-Phase Simulated Moving Bed for Methane/Nitrogen Separation Using a Commercial Activated Carbon. Industrial & Engineering Chemistry Research. 61(34). 12739–12753. 6 indexed citations

15.

Rebello, Carine Menezes, et al.. (2022). Mapping Uncertainties of Soft-Sensors Based on Deep Feedforward Neural Networks through a Novel Monte Carlo Uncertainties Training Process. Processes. 10(2). 409–409. 7 indexed citations

16.

Faria, Rui P. V., et al.. (2021). Design and Optimization for Simulated Moving Bed: Varicol Approach. IFAC-PapersOnLine. 54(3). 542–547. 2 indexed citations

17.

Faria, Rui P. V., et al.. (2020). Process re‐intensification strategy for butyl acrylate manufacturing: Enhancement, scaling‐up and economical evaluation. 2(3). 2 indexed citations

18.

Faria, Rui P. V., et al.. (2020). Pervaporation and Sorption Enhanced Reactive Cyclic Processes: The Butyl Acrylate Case Study. Industrial & Engineering Chemistry Research. 59(7). 2817–2827. 2 indexed citations

19.

Martins, Vanessa F. D., Ana M. Ribeiro, Marta G. Plaza, et al.. (2015). Gas-phase simulated moving bed: Propane/propylene separation on 13X zeolite. Journal of Chromatography A. 1423. 136–148. 55 indexed citations

20.

Loureiro, José M., et al.. (2014). Methanol production by bi‐reforming. The Canadian Journal of Chemical Engineering. 93(3). 510–526. 32 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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