World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
7551
World Ranking
6251
National Ranking
376

Overview

Jie Zhang is affiliated with Newcastle University in the United Kingdom and has a significant body of work in the field of engineering, with a particular focus on control systems and applied environmental technologies. Their research output spans 265 publications primarily centered on engineering and its applications.

The main subfields of Jie Zhang's expertise include Control and Systems Engineering, Mechanical Engineering, Artificial Intelligence, Biomedical Engineering, and Water Science and Technology. These areas reflect a multidisciplinary approach bridging traditional engineering disciplines with modern technological advancements.

Jie Zhang's research interests cover a range of specific topics, including:

  • Fault Detection and Control Systems
  • Advanced Control Systems Optimization
  • Machine Learning and Extreme Learning Machine (ELM)
  • Adsorption and biosorption for pollutant removal
  • Metallurgical Processes and Thermodynamics
  • Solar-Powered Water Purification Methods
  • Advanced Photocatalysis Techniques

Their recent published papers illustrate a combination of experimental, modeling, and machine learning methodologies applied to environmental and energy-related challenges. Notable recent publications are:

  • "Jujube stones based highly efficient activated carbon for methylene blue adsorption: Kinetics and isotherms modeling, thermodynamics and mechanism study, optimization via response surface methodology and machine learning approaches" (2022), published in Process Safety and Environmental Protection
  • "Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network" (2021), published in Applied Energy
  • "Modeling the organic matter of water using the decision tree coupled with bootstrap aggregated and least-squares boosting" (2022), published in Environmental Technology & Innovation
  • "Milk Source Identification and Milk Quality Estimation Using an Electronic Nose and Machine Learning Techniques" (2020), published in Sensors
  • "Mixed coagulant-flocculant optimization for pharmaceutical effluent pretreatment using response surface methodology and Gaussian process regression" (2022), published in Process Safety and Environmental Protection

Jie Zhang frequently collaborates with several co-authors, including Hichem Tahraoui, Abdeltif Amrane, Mohammed Kebir, Aymen Amine Assadi, and Lotfi Mouni, reflecting a sustained collaborative network in their research endeavors.

Their work has been consistently disseminated through a range of scientific journals, with multiple publications appearing in venues such as:

  • Water
  • Processes
  • Catalysts
  • SSRN Electronic Journal
  • arXiv (Cornell University)

Best Publications

  • Recurrent neuro-fuzzy networks for nonlinear process modeling

    Jie Zhang;A.J. Morris

  • Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM)

    Wei Cong Leong;Alireza Bahadori;Jie Zhang;Z. Ahmad

  • Performance monitoring of processes with multiple operating modes through multiple PLS models

    Shi Jian Zhao;Jie Zhang;Yong Mao Xu

  • A batch-to-batch iterative optimal control strategy based on recurrent neural network models

    Zhihua Xiong;Jie Zhang

  • Process performance monitoring using multivariate statistical process control

    E.B. Martin;A.J. Morris;J. Zhang

  • Developing robust non-linear models through bootstrap aggregated neural networks

    Jie Zhang

  • Artificial Intelligence techniques applied as estimator in chemical process systems - A literature survey

    Jarinah Mohd Ali;M.A. Hussain;Moses O. Tade;Jie Zhang

  • Structure-constrained low-rank representation.

    Kewei Tang;Risheng Liu;Zhixun Su;Jie Zhang

  • Inferential Estimation of Polymer Quality Using Stacked Neural Networks

    J. Zhang;E.B. Martin;A.J. Morris;C. Kiparissides

  • A sequential learning approach for single hidden layer neural networks

    Jie Zhang;A. J. Morris

  • Fuzzy neural networks for nonlinear systems modelling

    J. Zhang;A.J. Morris

  • Improved on-line process fault diagnosis through information fusion in multiple neural networks

    Jie Zhang

  • Product Quality Trajectory Tracking in Batch Processes Using Iterative Learning Control Based on Time-Varying Perturbation Models

    Zhihua Xiong;Jie Zhang

  • Batch-to-batch optimal control of a batch polymerisation process based on stacked neural network models

    Jie Zhang

  • Prediction of polymer quality in batch polymerisation reactors using robust neural networks

    J. Zhang;A.J. Morris;E.B. Martin;C. Kiparissides

  • On-line multivariate statistical monitoring of batch processes using Gaussian mixture model

    Tao Chen;Jie Zhang

  • Process monitoring using non-linear statistical techniques

    J Zhang;E.B Martin;A.J Morris

  • Process modelling and fault diagnosis using fuzzy neural networks

    Jie Zhang;Julian Morris

  • SMI 2013: Point cloud normal estimation via low-rank subspace clustering

    Jie Zhang;Junjie Cao;Xiuping Liu;Jun Wang

  • A Reliable Neural Network Model Based Optimal Control Strategy for a Batch Polymerization Reactor

    Jie Zhang

  • Long-term prediction models based on mixed order locally recurrent neural networks

    J. Zhang;A.J. Morris;E.B. Martin

Frequent Co-Authors

A.J. Morris
A.J. Morris University of Newcastle Australia
Karl Herrup
Karl Herrup Hong Kong University of Science and Technology
Huaxi Xu
Huaxi Xu Xiamen University
Guojun Bu
Guojun Bu Hong Kong University of Science and Technology
Alireza Bahadori
Alireza Bahadori Southern Cross University
M. Maral Mouradian
M. Maral Mouradian Rutgers, The State University of New Jersey
Costas Kiparissides
Costas Kiparissides Aristotle University of Thessaloniki
Dorothy E. Vatner
Dorothy E. Vatner Rutgers, The State University of New Jersey
Meihong Wang
Meihong Wang University of Sheffield
Risheng Liu
Risheng Liu Dalian University of Technology

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