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Journals
Processes
Special Issues
Process Systems Engineering for Complex Industrial Systems
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Process Systems Engineering for Complex Industrial Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 2752

Special Issue Editors

Dr. Junyao Xie

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Guest Editor
Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Interests: process control; transfer learning; estimation; distributed parameter systems; processes systems engineering
Prof. Dr. Wanke Yu

E-Mail Website
Guest Editor
School of Automation, China University of Geosciences, No. 388, Lumo Road, Wuhan, China
Interests: process monitoring; fault diagnosis; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Shumei Zhang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, No. 135, Yaguan Road, Tianjin, China
Interests: multivariate statistical analysis; process monitoring; deep neural network
Special Issues, Collections and Topics in MDPI journals
Dr. Yiyang Chen

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215031, China
Interests: intelligent control; artificial intelligence; image processing; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid advancement of modern industrial engineering, process systems engineering plays an increasingly important role in addressing the increasing system complexity and enhancing profitability, safety, and sustainability. Conventional techniques and solutions are often insufficient to deal with the multiple scales and plantwide networks of complex industrial systems. To address the complexity of modern industrial systems, advanced model-based and data-driven methods, algorithms, and solutions are needed for accurate modeling, monitoring, planning, and control.

The special issue on “Process Systems Engineering for Complex Industrial Systems” seeks high-quality works focusing on the recent advances in process systems engineering for industrial applications, especially the model-based, data-driven and hybrid techniques for process modeling, condition monitoring, fault detection and diagnosis, quality prediction and soft sensing, regulation and control design, etc. Moreover, new problems and future research directions on process systems engineering are also welcome.

Topics include, but are not limited to:

  • Model-based process modeling
  • Data-driven process modeling
  • State and parameter estimation
  • Condition monitoring, fault detection and diagnosis
  • Software sensor modeling
  • Process control and regulation
  • Thermodynamics-based process modeling and control
  • Machine learning-based modeling, estimation, and control
  • Process data analytics and multivariate statistical analysis
  • Hybrid methods and model calibration
  • Applications in chemical, biological, manufacturing, and energy systems

Dr. Junyao Xie
Prof. Dr. Wanke Yu
Dr. Shumei Zhang
Dr. Yiyang Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • process systems engineering
  • modeling
  • control
  • estimation
  • systems analysis
  • methods
  • algorithms
  • tools
  • design

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

16 pages, 4463 KiB  
Article
Risk Assessment Approach of Electronic Component Selection in Equipment R&D Using the XGBoost Algorithm and Domain Knowledge
by Chuanwen Wu, Shumei Zhang, Xiaoli Bao, Yang Wang, Zhikun Miao and Huixin Liu
Processes 2024, 12(8), 1716; https://doi.org/10.3390/pr12081716 - 15 Aug 2024
Viewed by 678
Abstract
Risk management in electronic component selection is crucial for ensuring inherent system quality dependability in aerospace equipment research and development (R&D). Therefore, it is of great significance to conduct rapid and accurate risk assessment research of electronic components based on engineering practice. This [...] Read more.
Risk management in electronic component selection is crucial for ensuring inherent system quality dependability in aerospace equipment research and development (R&D). Therefore, it is of great significance to conduct rapid and accurate risk assessment research of electronic components based on engineering practice. This article utilizes the extreme gradient boosting (XGBoost) algorithm and domain knowledge to assess electronic component selection risk. Firstly, an innovative risk assessment system is established for electronic component selection based on business materials analysis and investigation by questionnaire. Then, the values of factors in the system are quantified based on domain knowledge and empirical formulae. Finally, an XGBoost-based risk assessment model is constructed that can explore learning strategies and develop latent features by integrating multiple decision trees. The model is compared against the random forest (RF), support vector machine (SVM) and decision tree (DT) algorithms. Accuracy, precision, recall, and F1 score are used as evaluation indexes. The results obtained from the above algorithms illustrate the effectiveness of the proposed method in electronic component selection risk assessment. Full article
(This article belongs to the Special Issue Process Systems Engineering for Complex Industrial Systems)
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17 pages, 5681 KiB  
Article
Soft Sensor Modeling Method Considering Higher-Order Moments of Prediction Residuals
by Fangyuan Ma, Cheng Ji, Jingde Wang, Wei Sun and Ahmet Palazoglu
Processes 2024, 12(4), 676; https://doi.org/10.3390/pr12040676 - 28 Mar 2024
Viewed by 1255
Abstract
Traditional data-driven soft sensor methods can be regarded as an optimization process to minimize the predicted error. When applying the mean squared error as the objective function, the model tends to be trained to minimize the global errors of overall data samples. However, [...] Read more.
Traditional data-driven soft sensor methods can be regarded as an optimization process to minimize the predicted error. When applying the mean squared error as the objective function, the model tends to be trained to minimize the global errors of overall data samples. However, there are deviations in data from practical operation, in which the model performance in the estimation of the local variations in the target parameter worsens. This work presents a solution to this challenge by considering higher-order moments of prediction residuals, which enables the evaluation of deviations of the residual distribution from the normal distribution. By embedding constraints on the distribution of residuals into the objective function, the model tends to converge to the state where both stationary and deviation data can be accurately predicted. Data from the Tennessee Eastman process and an industrial cracking furnace are considered to validate the performance of the proposed modeling method. Full article
(This article belongs to the Special Issue Process Systems Engineering for Complex Industrial Systems)
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