Ricardo Rendall

790 total citations · 1 hit paper
29 papers, 557 citations indexed

About

Ricardo Rendall is a scholar working on Control and Systems Engineering, Analytical Chemistry and Statistics, Probability and Uncertainty. According to data from OpenAlex, Ricardo Rendall has authored 29 papers receiving a total of 557 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Control and Systems Engineering, 12 papers in Analytical Chemistry and 10 papers in Statistics, Probability and Uncertainty. Recurrent topics in Ricardo Rendall's work include Fault Detection and Control Systems (17 papers), Spectroscopy and Chemometric Analyses (12 papers) and Advanced Statistical Process Monitoring (9 papers). Ricardo Rendall is often cited by papers focused on Fault Detection and Control Systems (17 papers), Spectroscopy and Chemometric Analyses (12 papers) and Advanced Statistical Process Monitoring (9 papers). Ricardo Rendall collaborates with scholars based in Portugal, United States and Netherlands. Ricardo Rendall's co-authors include Marco S. Reis, Leo H. Chiang, Iván Castillo, Mark N. Joswiak, Zhenyu Wang, Ana C. Pereira, Biagio Palumbo, Antonio Lepore, Margarida J. Quina and Tiago J. Rato and has published in prestigious journals such as Industrial & Engineering Chemistry Research, Chemical Engineering Science and Energy & Fuels.

In The Last Decade

Ricardo Rendall

28 papers receiving 530 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Recent trends on hybrid modeling for Industry 4.0

2021 188 citations Mark N. Joswiak, Iván Castillo et al. Computers & Chemical Engineering profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ricardo Rendall Portugal	13	302	148	115	99	68	29	557
Bo Lu United States	13	226 0.7×	112 0.8×	79 0.7×	54 0.5×	53 0.8×	34	560
Iván Castillo United States	14	461 1.5×	161 1.1×	92 0.8×	77 0.8×	74 1.1×	42	760
X.Z. Wang United Kingdom	13	265 0.9×	177 1.2×	134 1.2×	46 0.5×	67 1.0×	29	835
Orestes Llanes‐Santiago Cuba	17	587 1.9×	220 1.5×	77 0.7×	70 0.7×	31 0.5×	80	868
Jesus Flores‐Cerrillo Canada	16	784 2.6×	288 1.9×	137 1.2×	95 1.0×	68 1.0×	37	992
Xiangguang Chen China	15	449 1.5×	173 1.2×	92 0.8×	22 0.2×	33 0.5×	57	795
Fernando V. Lima United States	17	519 1.7×	194 1.3×	16 0.1×	34 0.3×	55 0.8×	89	859
Bao Lin Denmark	7	312 1.0×	163 1.1×	86 0.7×	22 0.2×	18 0.3×	9	473
Hyun-Woo Cho South Korea	9	118 0.4×	103 0.7×	88 0.8×	26 0.3×	20 0.3×	37	351

Countries citing papers authored by Ricardo Rendall

Since Specialization

Citations

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

Fields of papers citing papers by Ricardo Rendall

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ricardo Rendall. 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 Ricardo Rendall. The network helps show where Ricardo Rendall may publish in the future.

Co-authorship network of co-authors of Ricardo Rendall

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Wang, Zhenyu, et al.. (2024). Regularized Bayesian Fusion for Multimodal Data Integration in Industrial Processes. Industrial & Engineering Chemistry Research. 63(48). 20989–21000. 3 indexed citations

Rendall, Ricardo, et al.. (2024). Hybrid modeling for improved extrapolation and transfer learning in the chemical processing industry. Chemical Engineering Science. 300. 120568–120568. 10 indexed citations

Rendall, Ricardo, Mark N. Joswiak, Iván Castillo, et al.. (2023). A functional data-driven approach to monitor and analyze equipment degradation in multiproduct batch processes. Process Safety and Environmental Protection. 180. 868–882. 2 indexed citations

Zhu, Wenbo, Iván Castillo, Zhenyu Wang, et al.. (2022). Benchmark study of reinforcement learning in controlling and optimizing batch processes. Civil War Book Review. 4(2). 5 indexed citations

Reis, Marco S., et al.. (2021). Improving the sensitivity of statistical process monitoring of manifolds embedded in high-dimensional spaces: The truncated-Q statistic. Chemometrics and Intelligent Laboratory Systems. 215. 104369–104369. 9 indexed citations

Zhu, Wenbo, Ricardo Rendall, Iván Castillo, et al.. (2021). Control of A Polyol Process Using Reinforcement Learning. IFAC-PapersOnLine. 54(3). 498–503. 7 indexed citations

Joswiak, Mark N., Iván Castillo, Zhenyu Wang, et al.. (2021). Recent trends on hybrid modeling for Industry 4.0. Computers & Chemical Engineering. 151. 107365–107365. 188 indexed citations breakdown →

Ma, Yan, Zhenyu Wang, Iván Castillo, et al.. (2021). Reinforcement Learning-Based Fed-Batch Optimization with Reaction Surrogate Model. Civil War Book Review. 2581–2586. 5 indexed citations

Wang, Zhenyu, Iván Castillo, Ricardo Rendall, et al.. (2021). Multi-source Heterogeneous Data Fusion for Toxin Level Quantification. IFAC-PapersOnLine. 54(3). 67–72. 3 indexed citations

10.

Rendall, Ricardo, et al.. (2020). Multirate fusion of data sources with different quality. IFAC-PapersOnLine. 53(2). 194–199.

11.

Reis, Marco S., et al.. (2019). Predicting ships' CO₂emissions using feature‐oriented methods. Applied Stochastic Models in Business and Industry. 36(1). 110–123. 8 indexed citations

12.

Rendall, Ricardo & Marco S. Reis. (2018). Which regression method to use? Making informed decisions in “data-rich/knowledge poor” scenarios – The Predictive Analytics Comparison framework (PAC). Chemometrics and Intelligent Laboratory Systems. 181. 52–63. 23 indexed citations

13.

Rendall, Ricardo, Iván Castillo, Bo Lu, et al.. (2018). Image-based manufacturing analytics: Improving the accuracy of an industrial pellet classification system using deep neural networks. Chemometrics and Intelligent Laboratory Systems. 180. 26–35. 19 indexed citations

14.

Rendall, Ricardo, et al.. (2018). Wide spectrum feature selection (WiSe) for regression model building. Computers & Chemical Engineering. 121. 99–110. 13 indexed citations

15.

Rendall, Ricardo, et al.. (2017). A Unifying and Integrated Framework for Feature Oriented Analysis of Batch Processes. Industrial & Engineering Chemistry Research. 56(30). 8590–8605. 19 indexed citations

16.

Lepore, Antonio, et al.. (2017). A comparison of advanced regression techniques for predicting ship CO₂ emissions. Quality and Reliability Engineering International. 33(6). 1281–1292. 30 indexed citations

17.

Rendall, Ricardo, Ana C. Pereira, & Marco S. Reis. (2016). Advanced predictive methods for wine age prediction: Part I – A comparison study of single-block regression approaches based on variable selection, penalized regression, latent variables and tree-based ensemble methods. Talanta. 171. 341–350. 23 indexed citations

18.

Rato, Tiago J., et al.. (2016). A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part I—Assessing Detection Strength. Industrial & Engineering Chemistry Research. 55(18). 5342–5358. 20 indexed citations

19.

Rendall, Ricardo, et al.. (2016). Assessment and Prediction of Lubricant Oil Properties Using Infrared Spectroscopy and Advanced Predictive Analytics. Energy & Fuels. 31(1). 179–187. 30 indexed citations

20.

Reis, Marco S., et al.. (2015). Challenges in the Specification and Integration of Measurement Uncertainty in the Development of Data-Driven Models for the Chemical Processing Industry. Industrial & Engineering Chemistry Research. 54(37). 9159–9177. 21 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.

Explore authors with similar magnitude of impact