John P. Eason

733 total citations
20 papers, 549 citations indexed

About

John P. Eason is a scholar working on Control and Systems Engineering, Mechanical Engineering and Electrical and Electronic Engineering. According to data from OpenAlex, John P. Eason has authored 20 papers receiving a total of 549 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Control and Systems Engineering, 7 papers in Mechanical Engineering and 5 papers in Electrical and Electronic Engineering. Recurrent topics in John P. Eason's work include Advanced Control Systems Optimization (8 papers), Process Optimization and Integration (8 papers) and Thermodynamic and Exergetic Analyses of Power and Cooling Systems (5 papers). John P. Eason is often cited by papers focused on Advanced Control Systems Optimization (8 papers), Process Optimization and Integration (8 papers) and Thermodynamic and Exergetic Analyses of Power and Cooling Systems (5 papers). John P. Eason collaborates with scholars based in United States, China and Norway. John P. Eason's co-authors include Selen Cremaschi, Lorenz T. Biegler, Xiao Feng, Haoshui Yu, Yufei Wang, Xi Chen, Alexander W. Dowling, David C. Miller, Truls Gundersen and Bethany L. Nicholson and has published in prestigious journals such as Applied Energy, Energy Conversion and Management and Energy.

In The Last Decade

John P. Eason

19 papers receiving 539 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
John P. Eason United States	12	213	205	145	104	76	20	549
Calvin Tsay United States	17	173 0.8×	422 2.1×	82 0.6×	19 0.2×	34 0.4×	37	725
J. Viswanathan United States	5	83 0.4×	628 3.1×	117 0.8×	35 0.3×	24 0.3×	10	779
Yidong Lang Germany	12	75 0.4×	230 1.1×	75 0.5×	31 0.3×	23 0.3×	28	592
Mohammed Saad Faizan Bangi United States	9	70 0.3×	259 1.3×	22 0.2×	86 0.8×	54 0.7×	11	496
M. Remy Belgium	14	102 0.5×	240 1.2×	29 0.2×	29 0.3×	29 0.4×	40	447
Adrian Caspari Germany	15	131 0.6×	385 1.9×	35 0.2×	23 0.2×	16 0.2×	25	588
David Rincón United States	13	71 0.3×	664 3.2×	28 0.2×	73 0.7×	25 0.3×	31	858
Sudeepta Mondal United States	11	126 0.6×	33 0.2×	49 0.3×	29 0.3×	50 0.7×	36	367
Quan Long United States	12	77 0.4×	31 0.2×	130 0.9×	14 0.1×	167 2.2×	23	517
Yicheng Zhou China	13	54 0.3×	56 0.3×	266 1.8×	31 0.3×	468 6.2×	31	657

Countries citing papers authored by John P. Eason

Since Specialization

Citations

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

Fields of papers citing papers by John P. Eason

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John P. Eason. 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 John P. Eason. The network helps show where John P. Eason may publish in the future.

Co-authorship network of co-authors of John P. Eason

This figure shows the co-authorship network connecting the top 25 collaborators of John P. Eason. A scholar is included among the top collaborators of John P. Eason 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 John P. Eason. John P. Eason 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

Eason, John P., et al.. (2023). Hose-based cross-layer backbone network design with Benders decomposition. 333–345. 2 indexed citations

Dangui, Vinayak, et al.. (2023). Targeted Defragmentation of a Production Optical Network. 1–3. 1 indexed citations

Eason, John P., et al.. (2021). Multistage nonlinear model predictive control for pumping treatment in hydraulic fracturing. AIChE Journal. 68(3). 11 indexed citations

Li, Can, et al.. (2020). Shale gas pad development planning under price uncertainty. AIChE Journal. 66(6). 9 indexed citations

Eason, John P., et al.. (2020). Operational Optimization of Polymerization Reactors with Computational Fluid Dynamics and Embedded Molecular Weight Distribution Using the Iterative Surrogate Model Method. Industrial & Engineering Chemistry Research. 59(19). 9165–9179. 9 indexed citations

Eason, John P., et al.. (2020). Nonlinear Model Predictive Control of the Hydraulic Fracturing Process. IFAC-PapersOnLine. 53(2). 11428–11433. 2 indexed citations

Chen, Xi, et al.. (2019). Dynamic optimization for grade transition processes using orthogonal collocation on molecular weight distribution. AIChE Journal. 65(4). 1198–1210. 14 indexed citations

Eason, John P. & Lorenz T. Biegler. (2018). Advanced trust region optimization strategies for glass box/black box models. AIChE Journal. 64(11). 3934–3943. 25 indexed citations

Eason, John P.. (2018). A Trust Region Filter Algorithm for Surrogate-based Optimization. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University). 2 indexed citations

10.

Yu, Haoshui, John P. Eason, Lorenz T. Biegler, Xiao Feng, & Truls Gundersen. (2018). Process optimization and working fluid mixture design for organic Rankine cycles (ORCs) recovering compression heat in oxy-combustion power plants. Energy Conversion and Management. 175. 132–141. 20 indexed citations

11.

Wan, Wei, John P. Eason, Bethany L. Nicholson, & Lorenz T. Biegler. (2017). Parallel cyclic reduction decomposition for dynamic optimization problems. Computers & Chemical Engineering. 120. 54–69. 10 indexed citations

12.

Eason, John P., et al.. (2017). Energy-efficient CO 2 liquefaction for oxy-combustion power plant with ASU-CPU integration enhanced by cascaded sub-ambient energy utilization and waste heat recovery. International journal of greenhouse gas control. 61. 124–137. 5 indexed citations

13.

Yu, Haoshui, John P. Eason, Lorenz T. Biegler, & Xiao Feng. (2017). Process integration and superstructure optimization of Organic Rankine Cycles (ORCs) with heat exchanger network synthesis. Computers & Chemical Engineering. 107. 257–270. 55 indexed citations

14.

Eason, John P. & Lorenz T. Biegler. (2016). A trust region filter method for glass box/black box optimization. AIChE Journal. 62(9). 3124–3136. 49 indexed citations

15.

Yu, Haoshui, John P. Eason, Lorenz T. Biegler, & Xiao Feng. (2016). Simultaneous heat integration and techno-economic optimization of Organic Rankine Cycle (ORC) for multiple waste heat stream recovery. Energy. 119. 322–333. 68 indexed citations

16.

Yu, Haoshui, Xiao Feng, Yufei Wang, Lorenz T. Biegler, & John P. Eason. (2016). A systematic method to customize an efficient organic Rankine cycle (ORC) to recover waste heat in refineries. Applied Energy. 179. 302–315. 37 indexed citations

17.

Eason, John P., et al.. (2016). Development of a first-principles hybrid boiler model for oxy-combustion power generation system. International journal of greenhouse gas control. 46. 136–157. 12 indexed citations

18.

Eason, John P. & Selen Cremaschi. (2014). A multi-objective superstructure optimization approach to biofeedstocks-to-biofuels systems design. Biomass and Bioenergy. 63. 64–75. 15 indexed citations

19.

Dowling, Alexander W., et al.. (2014). Coal Oxycombustion Power Plant Optimization Using First Principles and Surrogate Boiler Models. Energy Procedia. 63. 352–361. 13 indexed citations

20.

Eason, John P. & Selen Cremaschi. (2014). Adaptive sequential sampling for surrogate model generation with artificial neural networks. Computers & Chemical Engineering. 68. 220–232. 190 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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