Calvin Tsay

1.1k total citations
37 papers, 725 citations indexed

About

Calvin Tsay is a scholar working on Control and Systems Engineering, Computational Theory and Mathematics and Electrical and Electronic Engineering. According to data from OpenAlex, Calvin Tsay has authored 37 papers receiving a total of 725 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Control and Systems Engineering, 8 papers in Computational Theory and Mathematics and 7 papers in Electrical and Electronic Engineering. Recurrent topics in Calvin Tsay's work include Advanced Control Systems Optimization (20 papers), Process Optimization and Integration (15 papers) and Fault Detection and Control Systems (8 papers). Calvin Tsay is often cited by papers focused on Advanced Control Systems Optimization (20 papers), Process Optimization and Integration (15 papers) and Fault Detection and Control Systems (8 papers). Calvin Tsay collaborates with scholars based in United States, United Kingdom and Germany. Calvin Tsay's co-authors include Michael Bâldea, Richard C. Pattison, Jesus Flores‐Cerrillo, Ruth Misener, Luís M. S. Dias, Marianthi Ierapetritou, Jan Kronqvist, Alexander Mitsos, Adel Mhamdi and Adrian Caspari and has published in prestigious journals such as Applied Energy, Expert Systems with Applications and Computer Methods in Applied Mechanics and Engineering.

In The Last Decade

Calvin Tsay

33 papers receiving 699 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Calvin Tsay United States 17 422 173 110 110 82 37 725
Fernando V. Lima United States 17 519 1.2× 194 1.1× 90 0.8× 97 0.9× 40 0.5× 89 859
Adrian Caspari Germany 15 385 0.9× 131 0.8× 101 0.9× 69 0.6× 35 0.4× 25 588
Richard C. Pattison United States 16 591 1.4× 200 1.2× 131 1.2× 104 0.9× 64 0.8× 24 816
Iván Castillo United States 14 461 1.1× 161 0.9× 35 0.3× 67 0.6× 40 0.5× 42 760
Mohammad Mansouri Iran 16 216 0.5× 271 1.6× 182 1.7× 79 0.7× 14 0.2× 54 739
John P. Eason United States 12 205 0.5× 213 1.2× 30 0.3× 62 0.6× 145 1.8× 20 549
Márcio A.F. Martins Brazil 16 323 0.8× 153 0.9× 32 0.3× 65 0.6× 23 0.3× 63 582
Jesus Flores‐Cerrillo Canada 16 784 1.9× 288 1.7× 65 0.6× 59 0.5× 15 0.2× 37 992
Grégory François Switzerland 16 583 1.4× 57 0.3× 100 0.9× 33 0.3× 69 0.8× 52 920

Countries citing papers authored by Calvin Tsay

Since Specialization
Citations

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

Fields of papers citing papers by Calvin Tsay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Calvin Tsay

This figure shows the co-authorship network connecting the top 25 collaborators of Calvin Tsay. A scholar is included among the top collaborators of Calvin Tsay 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 Calvin Tsay. Calvin Tsay 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.
Tsay, Calvin, et al.. (2025). Deep-learning-aided modifier adaptation: synergies with process intensification. Chemical Engineering and Processing - Process Intensification. 219. 110581–110581.
2.
Ruiz, Carlos, et al.. (2025). A quantile neural network framework for two-stage stochastic optimization. Expert Systems with Applications. 284. 127876–127876. 1 indexed citations
3.
Mercangöz, Mehmet, et al.. (2025). PC-Gym: Benchmark environments for process control problems. Computers & Chemical Engineering. 204. 109363–109363. 1 indexed citations
4.
Tsay, Calvin, et al.. (2025). Hierarchical RL-MPC for Demand Response Scheduling. IFAC-PapersOnLine. 59(6). 229–234.
5.
Tsay, Calvin, et al.. (2025). Training Neural ODEs Using Fully Discretized Simultaneous Optimization. IFAC-PapersOnLine. 59(6). 469–474. 1 indexed citations
6.
7.
Paulson, Joel A. & Calvin Tsay. (2024). Bayesian optimization as a flexible and efficient design framework for sustainable process systems. Current Opinion in Green and Sustainable Chemistry. 51. 100983–100983. 18 indexed citations
8.
Krause, Andreas, Robert E. Lee, Ruth Misener, et al.. (2024). Transition Constrained Bayesian Optimization via Markov Decision Processes. 88194–88235. 1 indexed citations
9.
Tsay, Calvin & Staffan Qvist. (2023). Integrating process and power grid models for optimal design and demand response operation of giga‐scale green hydrogen. AIChE Journal. 69(12). 8 indexed citations
10.
Tsay, Calvin, et al.. (2023). Constrained continuous-action reinforcement learning for supply chain inventory management. Computers & Chemical Engineering. 181. 108518–108518. 12 indexed citations
11.
Tsay, Calvin, et al.. (2021). Economic Optimization of Carbon Capture Processes Using Ionic Liquids: Toward Flexibility in Capture Rate and Feed Composition. ACS Sustainable Chemistry & Engineering. 9(13). 4823–4839. 19 indexed citations
12.
Tsay, Calvin, et al.. (2019). Optimal demand response scheduling of an industrial air separation unit using data-driven dynamic models. Computers & Chemical Engineering. 126. 22–34. 79 indexed citations
13.
Tsay, Calvin & Michael Bâldea. (2019). 110th Anniversary: Using Data to Bridge the Time and Length Scales of Process Systems. Industrial & Engineering Chemistry Research. 58(36). 16696–16708. 32 indexed citations
14.
Tsay, Calvin & Michael Bâldea. (2019). Integrating production scheduling and process control using latent variable dynamic models. Control Engineering Practice. 94. 104201–104201. 30 indexed citations
15.
Tsay, Calvin & Michael Bâldea. (2019). Fast and efficient chemical process flowsheet simulation by pseudo-transient continuation on inertial manifolds. Computer Methods in Applied Mechanics and Engineering. 348. 935–953. 8 indexed citations
16.
Tsay, Calvin & Michael Bâldea. (2018). Scenario-Free Optimal Design under Uncertainty of the PRICO Natural Gas Liquefaction Process. Industrial & Engineering Chemistry Research. 57(17). 5868–5880. 10 indexed citations
17.
Dias, Luís M. S., Richard C. Pattison, Calvin Tsay, Michael Bâldea, & Marianthi Ierapetritou. (2018). A simulation-based optimization framework for integrating scheduling and model predictive control, and its application to air separation units. Computers & Chemical Engineering. 113. 139–151. 63 indexed citations
18.
Tsay, Calvin, et al.. (2018). A survey of optimal process design capabilities and practices in the chemical and petrochemical industries. Computers & Chemical Engineering. 112. 180–189. 35 indexed citations
19.
Tsay, Calvin, et al.. (2017). A superstructure-based design of experiments framework for simultaneous domain-restricted model identification and parameter estimation. Computers & Chemical Engineering. 107. 408–426. 12 indexed citations
20.
Pattison, Richard C., Calvin Tsay, & Michael Bâldea. (2017). Pseudo-transient models for multiscale, multiresolution simulation and optimization of intensified reaction/separation/recycle processes: Framework and a dimethyl ether production case study. Computers & Chemical Engineering. 105. 161–172. 31 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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