Argimiro R. Secchi

3.5k total citations
220 papers, 2.3k citations indexed

About

Argimiro R. Secchi is a scholar working on Control and Systems Engineering, Biomedical Engineering and Mechanical Engineering. According to data from OpenAlex, Argimiro R. Secchi has authored 220 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 88 papers in Control and Systems Engineering, 57 papers in Biomedical Engineering and 33 papers in Mechanical Engineering. Recurrent topics in Argimiro R. Secchi's work include Advanced Control Systems Optimization (68 papers), Fault Detection and Control Systems (39 papers) and Process Optimization and Integration (33 papers). Argimiro R. Secchi is often cited by papers focused on Advanced Control Systems Optimization (68 papers), Fault Detection and Control Systems (39 papers) and Process Optimization and Integration (33 papers). Argimiro R. Secchi collaborates with scholars based in Brazil, United States and Norway. Argimiro R. Secchi's co-authors include Nilo Sérgio Medeiros Cardozo, Maurício B. de Souza, Rafael de P. Soares, Evaristo C. Biscaia, Marco Antônio Záchia Ayub, Rosane Rech, Jovani L. Fávero, Hrvoje Jasak, Roberto C. Giordano and Jorge Otávio Trierweiler and has published in prestigious journals such as The Journal of Chemical Physics, SHILAP Revista de lepidopterología and Water Research.

In The Last Decade

Argimiro R. Secchi

203 papers receiving 2.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Argimiro R. Secchi Brazil 25 683 579 364 340 300 220 2.3k
Surendra Kumar India 29 568 0.8× 486 0.8× 182 0.5× 340 1.0× 346 1.2× 142 2.7k
Evaristo C. Biscaia Brazil 25 560 0.8× 450 0.8× 205 0.6× 301 0.9× 455 1.5× 122 2.1k
Gerhard Schembecker Germany 29 807 1.2× 716 1.2× 637 1.8× 998 2.9× 361 1.2× 174 3.0k
Sandro Macchietto United Kingdom 34 692 1.0× 1.9k 3.3× 435 1.2× 384 1.1× 511 1.7× 129 3.9k
N. Aziz Malaysia 17 1.2k 1.7× 384 0.7× 338 0.9× 369 1.1× 808 2.7× 89 2.7k
Achim Kienle Germany 29 627 0.9× 1.2k 2.2× 711 2.0× 538 1.6× 456 1.5× 234 2.9k
Bhaskar D. Kulkarni India 34 1.0k 1.5× 326 0.6× 535 1.5× 578 1.7× 529 1.8× 105 3.5k
Jean‐Pierre Corriou France 30 975 1.4× 755 1.3× 99 0.3× 358 1.1× 789 2.6× 139 2.8k
Dimitrios I. Gerogiorgis United Kingdom 23 540 0.8× 485 0.8× 289 0.8× 372 1.1× 526 1.8× 86 1.8k
Sandra Luz Martínez Vargas Mexico 13 673 1.0× 161 0.3× 152 0.4× 422 1.2× 441 1.5× 22 1.8k

Countries citing papers authored by Argimiro R. Secchi

Since Specialization
Citations

This map shows the geographic impact of Argimiro R. Secchi'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 Argimiro R. Secchi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Argimiro R. Secchi more than expected).

Fields of papers citing papers by Argimiro R. Secchi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Argimiro R. Secchi. 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 Argimiro R. Secchi. The network helps show where Argimiro R. Secchi may publish in the future.

Co-authorship network of co-authors of Argimiro R. Secchi

This figure shows the co-authorship network connecting the top 25 collaborators of Argimiro R. Secchi. A scholar is included among the top collaborators of Argimiro R. Secchi 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 Argimiro R. Secchi. Argimiro R. Secchi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Secchi, Argimiro R., et al.. (2025). Controlling Paracetamol Batch Crystallization in Ethanol by Reinforcement Learning. IFAC-PapersOnLine. 59(6). 373–378. 1 indexed citations
2.
3.
Souza, Maurício B. de, et al.. (2024). Data-driven modeling and multi-objective optimization of a continuous kraft pulping digester. Process Safety and Environmental Protection. 207. 505–517. 1 indexed citations
4.
Secchi, Argimiro R., et al.. (2024). A density functional theory study on interactions in water-bridged dimeric complexes of lignin. Physical Chemistry Chemical Physics. 26(12). 9234–9252. 2 indexed citations
5.
Grover, Martha A., et al.. (2024). Controlling Paracetamol Unseeded Batch Crystallization with NMPC and Inverse Model. IFAC-PapersOnLine. 58(14). 31–36. 2 indexed citations
6.
Secchi, Argimiro R., et al.. (2024). Enhanced Hybrid Model for Gas-Lifted Oil Production. IFAC-PapersOnLine. 58(14). 7–12.
7.
Grover, Martha A., et al.. (2024). Neural Network Inverse Model Controllers for Paracetamol Unseeded Batch Cooling Crystallization. Industrial & Engineering Chemistry Research. 63(45). 19613–19627. 4 indexed citations
8.
Secchi, Argimiro R., et al.. (2023). Globally optimal distillation column design using set trimming and enumeration techniques. Computers & Chemical Engineering. 174. 108254–108254. 2 indexed citations
9.
Melo, Prı́amo A., et al.. (2022). Tuning of Model Predictive Controllers Based on Hybrid Optimization. Processes. 10(2). 351–351. 6 indexed citations
10.
Secchi, Argimiro R., et al.. (2022). Nonlinear dynamic analysis and numerical continuation of periodic orbits in high-index differential–algebraic equation systems. Nonlinear Dynamics. 108(2). 1495–1507. 2 indexed citations
11.
Campos, M., et al.. (2021). Model predictive control with dead-time compensation applied to a gas compression system. Journal of Petroleum Science and Engineering. 203. 108580–108580. 7 indexed citations
12.
Souza, Maurício B. de, et al.. (2021). Correction to: Addressing the lack of measurements in the subsea environment by using a model scheduling Kalman filer coupled with a robust adaptive MPC. Brazilian Journal of Chemical Engineering. 38(2). 421–421. 1 indexed citations
13.
Souza, Maurício B. de, et al.. (2020). Addressing the lack of measurements in the subsea environment by using a model scheduling Kalman filter coupled with a robust adaptive MPC. Brazilian Journal of Chemical Engineering. 38(4). 683–703. 5 indexed citations
14.
Secchi, Argimiro R., et al.. (2019). NMPC integrated with optimization layer in offshore production. IFAC-PapersOnLine. 52(1). 502–507. 4 indexed citations
15.
Krishnamoorthy, Dinesh, et al.. (2019). Model Predictive Control with Adaptive Strategy Applied to an Electric Submersible Pump in a Subsea Environment. IFAC-PapersOnLine. 52(1). 784–789. 24 indexed citations
16.
Secchi, Argimiro R., et al.. (2019). CO2 Subsea Separation: Concept & Control Strategies. IFAC-PapersOnLine. 52(1). 790–795. 5 indexed citations
17.
Melo, Prı́amo A., et al.. (2019). Tuning of Model Predictive Control Based on Hybrid Optimization. IFAC-PapersOnLine. 52(1). 136–141. 2 indexed citations
18.
Secchi, Argimiro R., et al.. (2019). Machine learning models to support reservoir production optimization. IFAC-PapersOnLine. 52(1). 498–501. 24 indexed citations
19.
Furlan, Felipe F., et al.. (2018). A Kriging-based approach for conjugating specific dynamic models into whole plant stationary simulations. Computers & Chemical Engineering. 119. 190–194. 5 indexed citations
20.
Secchi, Argimiro R., et al.. (2015). Model Predictive Control with quality requirements on petroleum production platforms. Journal of Petroleum Science and Engineering. 137. 10–21. 15 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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