World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
39
Citations
5560
World Ranking
7754
National Ranking
1385

Qunxiong Zhu 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 Qunxiong Zhu 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: 285 publications — 73rd percentile

73% of scientists in this discipline score the same or lower.

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

Qunxiong Zhu 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 Qunxiong Zhu 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: 39 D-Index — 24th percentile

24% of scientists in this discipline score the same or lower.

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

Overview

Qunxiong Zhu is affiliated with Beijing University of Chemical Technology in China and has a focused research career primarily in the fields of Engineering and Computer Science. Their scholarly output spans over numerous topics, especially in Control and Systems Engineering and Artificial Intelligence, alongside contributions to Mechanical Engineering, Analytical Chemistry, and Industrial and Manufacturing Engineering.

The researcher has contributed extensively to several key research topics including:

  • Fault Detection and Control Systems
  • Mineral Processing and Grinding
  • Spectroscopy and Chemometric Analyses
  • Advanced Algorithms and Applications
  • Machine Learning and ELM
  • Industrial Vision Systems and Defect Detection
  • Anomaly Detection Techniques and Applications

Qunxiong Zhu's work has been published in a variety of recurrent venues such as:

  • IEEE Transactions on Industrial Informatics
  • Industrial & Engineering Chemistry Research
  • IEEE Transactions on Instrumentation and Measurement
  • ISA Transactions
  • Journal of Process Control

Their recent papers include:

  • Novel manifold learning based virtual sample generation for optimizing soft sensor with small data, 2020, ISA Transactions
  • Fault diagnosis using novel AdaBoost based discriminant locality preserving projection with resamples, 2020, Engineering Applications of Artificial Intelligence
  • Novel virtual sample generation using conditional GAN for developing soft sensor with small data, 2021, Engineering Applications of Artificial Intelligence
  • Novel double-layer bidirectional LSTM network with improved attention mechanism for predicting energy consumption, 2021, ISA Transactions
  • Novel soft sensor development using echo state network integrated with singular value decomposition: Application to complex chemical processes, 2020, Chemometrics and Intelligent Laboratory Systems

The scientist collaborates frequently with several co-authors, most notably:

  • Yan-Lin He
  • Yuan Xu
  • Ning Zhang
  • Wei Ke

The range of topics, co-authors, and publication venues reflect a multifaceted research profile with an emphasis on developing and optimizing fault detection systems and soft sensors using machine learning approaches. This is underpinned by practical applications in chemical engineering processes and energy consumption prediction models.

Best Publications

  • Review: Multi-objective optimization methods and application in energy saving

    Yunfei Cui;Yunfei Cui;Zhiqiang Geng;Zhiqiang Geng;Qunxiong Zhu;Qunxiong Zhu;Yongming Han;Yongming Han

  • Energy efficiency analysis method based on fuzzy DEA cross-model for ethylene production systems in chemical industry

    Yongming Han;Yongming Han;Zhiqiang Geng;Zhiqiang Geng;Qunxiong Zhu;Qunxiong Zhu;Yixin Qu

  • A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries

    Hong-Fei Gong;Hong-Fei Gong;Zhong-Sheng Chen;Zhong-Sheng Chen;Qun-Xiong Zhu;Qun-Xiong Zhu;Yan-Lin He;Yan-Lin He

  • Adding rectifying/stripping section type heat integration to a pressure-swing distillation (PSD) process

    Kejin Huang;Lan Shan;Qunxiong Zhu;Jixin Qian

  • Fuzzy multi-attribute decision-making method based on eigenvector of fuzzy attribute evaluation space

    Unknown

  • A Novel Hybrid Method Integrating ICA-PCA With Relevant Vector Machine for Multivariate Process Monitoring

    Yuan Xu;Sheng-Qi Shen;Yan-Lin He;Qun-Xiong Zhu

  • Data driven soft sensor development for complex chemical processes using extreme learning machine

    Yan-Lin He;Yan-Lin He;Zhi-Qiang Geng;Zhi-Qiang Geng;Qun-Xiong Zhu;Qun-Xiong Zhu

  • Novel manifold learning based virtual sample generation for optimizing soft sensor with small data

    Xiao-Han Zhang;Yuan Xu;Yan-Lin He;Yan-Lin He;Qun-Xiong Zhu;Qun-Xiong Zhu

  • Novel virtual sample generation using conditional GAN for developing soft sensor with small data

    Qun-Xiong Zhu;Kun-Rui Hou;Zhong-Sheng Chen;Zi-Shu Gao

  • Energy saving and prediction modeling of petrochemical industries: A novel ELM based on FAHP

    ZhiQiang Geng;ZhiQiang Geng;Lin Qin;Lin Qin;YongMing Han;YongMing Han;QunXiong Zhu;QunXiong Zhu

  • Energy and environment efficiency analysis based on an improved environment DEA cross-model: Case study of complex chemical processes

    ZhiQiang Geng;ZhiQiang Geng;JunGen Dong;JunGen Dong;YongMing Han;YongMing Han;QunXiong Zhu;QunXiong Zhu

  • Multiscale Nonlinear Principal Component Analysis (NLPCA) and Its Application for Chemical Process Monitoring

    Zhiqiang Geng;Qunxiong Zhu

  • Novel double-layer bidirectional LSTM network with improved attention mechanism for predicting energy consumption.

    Yan-Lin He;Lei Chen;Yanlu Gao;Jia-Hui Ma

  • A novel virtual sample generation method based on a modified conditional Wasserstein GAN to address the small sample size problem in soft sensing

    Unknown

  • Multi-objective Particle Swarm Optimization Hybrid Algorithm: An Application on Industrial Cracking Furnace

    Chengfei Li;Qunxiong Zhu;Zhiqiang Geng

  • Design and control of an ideal heat-integrated distillation column (ideal HIDIC) system separating a close-boiling ternary mixture

    Kejin Huang;Lan Shan;Qunxiong Zhu;Jixin Qian

  • Fault diagnosis using novel AdaBoost based discriminant locality preserving projection with resamples

    Yan-Lin He;Yan-Lin He;Yang Zhao;Yang Zhao;Xiao Hu;Xiao Hu;Xiao-Na Yan;Xiao-Na Yan

  • A novel and effective nonlinear interpolation virtual sample generation method for enhancing energy prediction and analysis on small data problem: A case study of Ethylene industry

    Yan-Lin He;Yan-Lin He;Ping-Jiang Wang;Ping-Jiang Wang;Ming-Qing Zhang;Ming-Qing Zhang;Qun-Xiong Zhu;Qun-Xiong Zhu

  • Novel soft sensor development using echo state network integrated with singular value decomposition: Application to complex chemical processes

    Yan-Lin He;Yan-Lin He;Ye Tian;Ye Tian;Yuan Xu;Yuan Xu;Qun-Xiong Zhu;Qun-Xiong Zhu

  • Improved Locality Preserving Projections Based on Heat-Kernel and Cosine Weights for Fault Classification in Complex Industrial Processes

    Unknown

  • Rough set-based heuristic hybrid recognizer and its application in fault diagnosis

    Zhiqiang Geng;Qunxiong Zhu

  • Text Classification Using Novel Term Weighting Scheme-Based Improved TF-IDF for Internet Media Reports

    Zhiying Jiang;Zhiying Jiang;Bo Gao;Bo Gao;Yanlin He;Yanlin He;Yongming Han;Yongming Han

  • Review: Energy efficiency evaluation of complex petrochemical industries

    Yongming Han;Yongming Han;Hao Wu;Hao Wu;Zhiqiang Geng;Zhiqiang Geng;Qunxiong Zhu;Qunxiong Zhu

  • Energy optimization and prediction of complex petrochemical industries using an improved artificial neural network approach integrating data envelopment analysis

    Yong-Ming Han;Yong-Ming Han;Zhi-Qiang Geng;Zhi-Qiang Geng;Qun-Xiong Zhu;Qun-Xiong Zhu

  • Energy management and optimization modeling based on a novel fuzzy extreme learning machine: Case study of complex petrochemical industries

    Yongming Han;Yongming Han;Qing Zeng;Qing Zeng;Zhiqiang Geng;Zhiqiang Geng;Qunxiong Zhu;Qunxiong Zhu

Frequent Co-Authors

Zhiqiang Geng
Zhiqiang Geng Beijing University of Chemical Technology
Abbas Rajabifard
Abbas Rajabifard University of Melbourne
Gao Huang
Gao Huang Tsinghua University
Nengcheng Chen
Nengcheng Chen Wuhan University
Nael H. El-Farra
Nael H. El-Farra University of California, Davis
Ahmet Palazoglu
Ahmet Palazoglu University of California, Davis
Zoltan K. Nagy
Zoltan K. Nagy Purdue University West Lafayette

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