Debangsu Bhattacharyya

3.7k total citations
148 papers, 2.8k citations indexed

About

Debangsu Bhattacharyya is a scholar working on Mechanical Engineering, Control and Systems Engineering and Biomedical Engineering. According to data from OpenAlex, Debangsu Bhattacharyya has authored 148 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Mechanical Engineering, 57 papers in Control and Systems Engineering and 51 papers in Biomedical Engineering. Recurrent topics in Debangsu Bhattacharyya's work include Carbon Dioxide Capture Technologies (48 papers), Advanced Control Systems Optimization (34 papers) and Process Optimization and Integration (29 papers). Debangsu Bhattacharyya is often cited by papers focused on Carbon Dioxide Capture Technologies (48 papers), Advanced Control Systems Optimization (34 papers) and Process Optimization and Integration (29 papers). Debangsu Bhattacharyya collaborates with scholars based in United States, India and France. Debangsu Bhattacharyya's co-authors include Raghunathan Rengaswamy, Stephen E. Zitney, Yuan Jiang, Richard Turton, David C. Miller, Benjamin Omell, Eric Liese, Charles Tong, Pragasen Pillay and Qiang Zhang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Energy & Environmental Science and Journal of Power Sources.

In The Last Decade

Debangsu Bhattacharyya

146 papers receiving 2.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Debangsu Bhattacharyya United States 29 1.2k 851 705 677 592 148 2.8k
Chang He China 24 555 0.5× 444 0.5× 461 0.7× 333 0.5× 313 0.5× 126 2.1k
M. M. Faruque Hasan United States 32 1.3k 1.1× 573 0.7× 343 0.5× 1.1k 1.6× 399 0.7× 83 3.0k
Chonghun Han South Korea 33 1.6k 1.3× 612 0.7× 294 0.4× 1.4k 2.1× 265 0.4× 177 3.5k
Giorgio Cau Italy 32 1.4k 1.2× 557 0.7× 689 1.0× 421 0.6× 213 0.4× 100 2.7k
Yan Cao China 38 2.2k 1.8× 907 1.1× 587 0.8× 170 0.3× 359 0.6× 107 3.4k
Truls Gundersen Norway 33 2.0k 1.7× 672 0.8× 339 0.5× 1.4k 2.1× 311 0.5× 136 3.6k
Emanuele Martelli Italy 33 1.4k 1.2× 668 0.8× 1.4k 2.0× 754 1.1× 245 0.4× 113 3.4k
Ennio Macchi Italy 39 3.1k 2.6× 1.1k 1.3× 975 1.4× 434 0.6× 416 0.7× 145 4.7k
Ali Vatani Iran 32 1.5k 1.3× 569 0.7× 294 0.4× 210 0.3× 268 0.5× 79 2.6k
Tatiana Morosuk Germany 47 5.0k 4.1× 1.3k 1.6× 905 1.3× 669 1.0× 581 1.0× 189 6.9k

Countries citing papers authored by Debangsu Bhattacharyya

Since Specialization
Citations

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

Fields of papers citing papers by Debangsu Bhattacharyya

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Debangsu Bhattacharyya

This figure shows the co-authorship network connecting the top 25 collaborators of Debangsu Bhattacharyya. A scholar is included among the top collaborators of Debangsu Bhattacharyya 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 Debangsu Bhattacharyya. Debangsu Bhattacharyya 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
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Bhattacharyya, Debangsu, et al.. (2025). Development of mass, energy, and thermodynamics constrained steady-state and dynamic neural networks for interconnected chemical systems. Chemical Engineering Science. 309. 121506–121506. 1 indexed citations
3.
Chen, Yujuan, Brandon Robinson, Changle Jiang, et al.. (2025). Improved Efficiency of CO2 Conversion to Olefins in a Thermal–Microwave Hybrid Heating Reactor. ACS Sustainable Chemistry & Engineering. 13(17). 6355–6366. 1 indexed citations
4.
Gupta, Deepak, et al.. (2025). Mass-Constrained hybrid Gaussian radial basis neural networks: Development, training, and applications to modeling nonlinear dynamic noisy chemical processes. Computers & Chemical Engineering. 197. 109080–109080. 2 indexed citations
5.
Bhattacharyya, Debangsu, et al.. (2024). Development of algorithms for augmenting and replacing conventional process control using reinforcement learning. Computers & Chemical Engineering. 190. 108826–108826. 4 indexed citations
6.
Bhattacharyya, Debangsu, et al.. (2024). Estimation-based model predictive control with objective prioritization for mutually exclusive objectives: Application to a power plant. Journal of Process Control. 141. 103268–103268. 6 indexed citations
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Tewari, Kshitij, et al.. (2024). Unlocking Syngas Synthesis from the Catalytic Gasification of Lignocellulose Pinewood: Catalytic and Pressure Insights. ACS Sustainable Chemistry & Engineering. 12(11). 4718–4730. 8 indexed citations
9.
Bhattacharyya, Debangsu, et al.. (2024). Optimization of Solid Oxide Electrolysis Cell Systems Accounting for Long-Term Performance and Health Degradation. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 3. 448–454. 3 indexed citations
10.
Bhattacharyya, Debangsu, et al.. (2024). On the development of steady-state and dynamic mass-constrained neural networks using noisy transient data. Computers & Chemical Engineering. 187. 108722–108722. 11 indexed citations
11.
Bergman, Richard, Troy Runge, Seyed Hashem Mousavi‐Avval, et al.. (2024). Techno-economic and environmental impacts assessments of sustainable aviation fuel production from forest residues. Sustainable Energy & Fuels. 8(19). 4602–4616. 15 indexed citations
12.
Bhattacharyya, Debangsu, et al.. (2024). Nonlinear state estimation of a power plant superheater by using the extended Kalman filter for differential algebraic equation systems. Applied Thermal Engineering. 251. 123471–123471. 10 indexed citations
13.
Bhattacharyya, Debangsu, et al.. (2023). Optimal nonlinear dynamic sparse model selection and Bayesian parameter estimation for nonlinear systems. Computers & Chemical Engineering. 180. 108502–108502. 10 indexed citations
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Zantye, Manali S., et al.. (2023). THESEUS: A techno-economic design, integration and downselection framework for energy storage. Energy Conversion and Management. 284. 116976–116976. 8 indexed citations
16.
Bhattacharyya, Debangsu, et al.. (2023). Hybrid Series/Parallel All-Nonlinear Dynamic-Static Neural Networks: Development, Training, and Application to Chemical Processes. Industrial & Engineering Chemistry Research. 62(7). 3221–3237. 18 indexed citations
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Bai, Xinwei, et al.. (2021). Multiscale Modeling of a Direct Nonoxidative Methane Dehydroaromatization Reactor with a Validated Model for Catalyst Deactivation. Industrial & Engineering Chemistry Research. 60(13). 4903–4918. 6 indexed citations
19.
Miller, David C., et al.. (2016). Mathematical modeling of a moving bed reactor for post‐combustion CO2 capture. AIChE Journal. 62(11). 3899–3914. 21 indexed citations
20.
Jiang, Yuan & Debangsu Bhattacharyya. (2016). Process modeling of direct coal-biomass to liquids (CBTL) plants with shale gas utilization and CO2 capture and storage (CCS). Applied Energy. 183. 1616–1632. 26 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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