World's Best Scientists 2026 revealed!
Jaroslaw Krzywanski

Jaroslaw Krzywanski

D-Index & Metrics

Engineering and Technology

D-Index
45
Citations
4282
World Ranking
5659
National Ranking
8

Jaroslaw Krzywanski publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Jaroslaw Krzywanski sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 148 publications — 26th percentile

26% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Jaroslaw Krzywanski D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Jaroslaw Krzywanski sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 45 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Jaroslaw Krzywanski is affiliated with Jan Długosz University in Poland. Their research primarily spans the field of Engineering with a substantial focus on Mechanical Engineering, Biomedical Engineering, Renewable Energy, Sustainability and the Environment, Computational Mechanics, and Electrical and Electronic Engineering.

The scientist's research topics cover several specialized areas, including:

  • Adsorption and Cooling Systems
  • Heat Transfer and Optimization
  • Refrigeration and Air Conditioning Technologies
  • Thermochemical Biomass Conversion Processes
  • Chemical Looping and Thermochemical Processes
  • Oil, Gas, and Environmental Issues
  • Thermodynamic and Exergetic Analyses of Power and Cooling Systems

Frequent co-authors with whom Jaroslaw Krzywanski has collaborated include Marcin Sosnowski, W. Nowak, Karolina Grabowska, Anna Żyłka, and Dorian Skrobek. These collaborative relationships have contributed to a prolific publication record across various scientific venues.

Krzywanski's publication output is concentrated in several key journals, such as:

  • Energies
  • Materials
  • Energy
  • Entropy
  • SSRN Electronic Journal

Some recent notable papers are:

  • Chemical Looping Combustion: A Brief Overview, 2022, Energies
  • New hybrid photovoltaic-fuel cell system for green hydrogen and power production: Performance optimization assisted with Gaussian process regression method, 2024, International Journal of Hydrogen Energy
  • Prediction of Sorption Processes Using the Deep Learning Methods (Long Short-Term Memory), 2020, Energies
  • Implementation of deep learning methods in prediction of adsorption processes, 2022, Advances in Engineering Software
  • Advanced Computational Methods for Modeling, Prediction and Optimization-A Review, 2024, Materials

The scope of research blends experimental and computational techniques, with particular attention to modeling, prediction, and optimization methods in energy systems and engineering processes. The application of deep learning methods for adsorption and sorption process predictions features prominently in their recent work.

This scientific profile reflects an emphasis on interdisciplinary approaches within contemporary engineering challenges, especially focusing on sustainable energy technologies and thermal system advancements.

Best Publications

  • Polyhedral meshing in numerical analysis of conjugate heat transfer

    Marcin Sosnowski;Jaroslaw Krzywanski;Karolina Grabowska;Renata Gnatowska

  • Chemical Looping Combustion: A Brief Overview

    Unknown

  • Polyhedral meshing as an innovative approach to computational domain discretization of a cyclone in a fluidized bed CLC unit

    Marcin Sosnowski;Jaroslaw Krzywanski;Renata Gnatowska

  • Optimization of a three-bed adsorption chiller by genetic algorithms and neural networks

    J. Krzywanski;K. Grabowska;F. Herman;P. Pyrka

  • New hybrid photovoltaic-fuel cell system for green hydrogen and power production: Performance optimization assisted with Gaussian process regression method

    Unknown

  • Advanced Computational Methods for Modeling, Prediction and Optimization—A Review

    Unknown

  • Modeling of solid fuels combustion in oxygen-enriched atmosphere in circulating fluidized bed boiler: Part 1. The mathematical model of fuel combustion in oxygen-enriched CFB environment

    Jaroslaw Krzywanski;Tomasz Czakiert;Waldemar Muskala;Robert Sekret

  • Genetic algorithms and neural networks in optimization of sorbent enhanced H2 production in FB and CFB gasifiers

    Jaroslaw Krzywanski;Hongtao Fan;Yi Feng;Abdul Rahim Shaikh

  • A generalized model of SO2 emissions from large- and small-scale CFB boilers by artificial neural network approach

    J. Krzywanski;T. Czakiert;A. Blaszczuk;R. Rajczyk

  • Effect of bed particle size on heat transfer between fluidized bed of group b particles and vertical rifled tubes

    Artur Blaszczuk;Wojciech Nowak;Jaroslaw Krzywanski

  • Prediction of Sorption Processes Using the Deep Learning Methods (Long Short-Term Memory)

    Dorian Skrobek;Jaroslaw Krzywanski;Marcin Sosnowski;Anna Kulakowska

  • Implementation of deep learning methods in prediction of adsorption processes

    Unknown

  • Towards enhanced heat and mass exchange in adsorption systems: The role of AutoML and fluidized bed innovations

    Unknown

  • Construction of an innovative adsorbent bed configuration in the adsorption chiller - Selection criteria for effective sorbent-glue pair

    Karolina Grabowska;Jaroslaw Krzywanski;Wojciech Nowak;Marta Wesolowska

  • Safety and environmental reasons for the use of Ni-, Co-, Cu-, Mn- and Fe-based oxygen carriers in CLC/CLOU applications: An overview

    Kamil Idziak;Tomasz Czakiert;Jaroslaw Krzywanski;Anna Zylka

  • Modelling of CO2, CO, SO2, O2 and NOx emissions from the oxy-fuel combustion in a circulating fluidized bed

    J. Krzywański;T. Czakiert;W. Muskała;W. Nowak

  • CO2 Capture by Virgin Ivy Plants Growing Up on the External Covers of Houses as a Rapid Complementary Route to Achieve Global GHG Reduction Targets

    Unknown

  • A generalized model of SO2 emissions from large- and small-scale CFB boilers by artificial neural network approach Part 2. SO2 emissions from large- and pilot-scale CFB boilers in O2/N2, O2/CO2 and O2/RFG combustion atmospheres

    J. Krzywanski;T. Czakiert;A. Blaszczuk;R. Rajczyk

  • Heat and mass transfer prediction in fluidized beds of cooling and desalination systems by AI approach

    Unknown

  • A comparison of fuzzy logic and cluster renewal approaches for heat transfer modeling in a 1296 t/h CFB boiler with low level of flue gas recirculation

    Artur Błaszczuk;Jarosław Krzywański

  • Strategic-level performance enhancement of a 660 MWe supercritical power plant and emissions reduction by AI approach

    Waqar Muhammad Ashraf;Waqar Muhammad Ashraf;Ghulam Moeen Uddin;Syed Muhammad Arafat;Syed Muhammad Arafat;Jaroslaw Krzywanski

  • Experimental Study of Three-Bed Adsorption Chiller with Desalination Function

    Karol Sztekler;Wojciech Kalawa;Wojciech Nowak;Lukasz Mika

  • Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

    Waqar Muhammad Ashraf;Ghulam Moeen Uddin;Syed Muhammad Arafat;Sher Afghan

  • A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods

    Jaroslaw Krzywanski

  • Modeling of bed-to-wall heat transfer coefficient in a large-scale CFBC by fuzzy logic approach

    Jaroslaw Krzywanski;Wojciech Nowak

  • Modeling of heat transfer coefficient in the furnace of CFB boilers by artificial neural network approach

    Jaroslaw Krzywanski;Wojciech Nowak

  • A 1.5D model of a complex geometry laboratory scale fuidized bed clc equipment

    Jaroslaw Krzywanski;A. Żylka;T. Czakiert;K. Kulicki

  • Modeling of a re-heat two-stage adsorption chiller by AI approach

    Jaroslaw Krzywanski;Karolina Grabowska;Marcin Sosnowski;Anna Żyłka

Frequent Co-Authors

Muhammad Farooq
Muhammad Farooq Sultan Qaboos University
Qinhui Wang
Qinhui Wang Zhejiang University
Mengxiang Fang
Mengxiang Fang Zhejiang University

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