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Processes
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Advances in Process Safety and Protection of Cyber-Physical Systems (CPS)
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Advances in Process Safety and Protection of Cyber-Physical Systems (CPS)

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 9025

Special Issue Editors

Prof. Dr. Shuang-Hua Yang

E-Mail Website
Guest Editor
College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
Interests: cyber-physical systems; safety and security; risk assessment
Prof. Dr. Yi Cao

E-Mail Website
Guest Editor
College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
Interests: process control; self-optimal control; process monitoring
Dr. Yulong Ding

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: industrial internet safety and security; machine learning

Special Issue Information

Dear Colleagues, 

Process safety has been a top priority in process systems engineering for decades. With the increase of process complexity and the involvement of the Internet in process industries, unintentional man-made disasters are happening more often and causing serious consequences. Fortunately, many innovative technologies are being deployed in real process industries, including mathematical-model-based approaches, machine learning algorithms, data driven approaches, etc. With the development of industrial Internet technologies, more and more traditional process industries are directly or indirectly connected with external networks, making them cyber-physical systems (CPSs)—that is, the “cyber” aspect is becoming a part of the industrial processes. Benefiting the advantages from the involvement of the Internet in CPSs, serious safety risks could be also created alongside the benefits. This Special Issue on “Advances in Process Safety and Protection of Cyber-Physical Systems” seeks high-quality works focusing on the latest developments in process safety and technologies of cyber-physical systems protection for general industrial processes. Topics include, but are not limited to:

  • Advanced process hazard analysis;
  • Model-based process risk assessment and prevention;
  • Machine learning for process fault diagnosis;
  • Process safety monitoring;
  • Safe control systems design;
  • Alarm management;
  • Safety protection in cyber-physical systems;
  • Safety requirements analysis;
  • Inherent safety of cyber-physical systems;
  • Endogenous safety of cyber-physical systems;
  • Intrusion detection and attacker localization for cyber-physical systems.

Prof. Dr. Shuang-Hua Yang
Prof. Dr. Yi Cao
Dr. Yulong Ding
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 safety
  • fault diagnosis
  • operation monitoring
  • cyber-physical systems
  • risk analysis
  • loss prevention

Published Papers (6 papers)

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Editorial

Jump to: Research

3 pages, 184 KiB  
Editorial
Advances in Process Safety and Protection of Cyber-Physical Systems
by Shuang-Hua Yang, Yi Cao and Yulong Ding
Processes 2023, 11(12), 3419; https://doi.org/10.3390/pr11123419 - 13 Dec 2023
Viewed by 699
Abstract
Safety has remained the foremost concern in process systems engineering for decades [...] Full article
(This article belongs to the Special Issue Advances in Process Safety and Protection of Cyber-Physical Systems (CPS))

Research

Jump to: Editorial

18 pages, 5385 KiB  
Article
Valve Stiction Detection Method Based on Dynamic Slow Feature Analysis and Hurst Exponent
by Linyuan Shang, Yuyu Zhang and Hanyuan Zhang
Processes 2023, 11(7), 1913; https://doi.org/10.3390/pr11071913 - 26 Jun 2023
Viewed by 1348
Abstract
Valve stiction is the most common root of oscillation faults in process control systems, and it can cause the severe deterioration of control performance and system instability, ultimately impacting product quality and process safety. A new method for detecting valve stiction, based on [...] Read more.
Valve stiction is the most common root of oscillation faults in process control systems, and it can cause the severe deterioration of control performance and system instability, ultimately impacting product quality and process safety. A new method for detecting valve stiction, based on dynamic slow feature analysis (DSFA) and the Hurst exponent, is proposed in this paper. The proposed method first utilizes DSFA to extract slow features (SFs) from the preprocessed and reconstructed data of the controller output and the controlled process variable; then, it calculates the Hurst exponent of the slowest SF to quantify its long-term correlation; and, finally, it defines a new valve detection index to identify valve stiction. The results obtained from simulations and actual process case studies demonstrate that the proposed method, based on a DSFA–Hurst exponent, can effectively detect valve stiction in control loops. Full article
(This article belongs to the Special Issue Advances in Process Safety and Protection of Cyber-Physical Systems (CPS))
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19 pages, 7021 KiB  
Article
Ontology-Based Semantic Modeling of Coal Mine Roof Caving Accidents
by Lingzi Jin, Qian Liu and Yide Geng
Processes 2023, 11(4), 1058; https://doi.org/10.3390/pr11041058 - 31 Mar 2023
Cited by 3 | Viewed by 1155
Abstract
The frequency of roof-caving accidents ranks first among all coal mine accidents. However, the scattered knowledge system in this field and the lack of standardization exacerbate the difficulty of analyzing roof fall accidents. This study proposes an ontology-based semantic modeling method for roof [...] Read more.
The frequency of roof-caving accidents ranks first among all coal mine accidents. However, the scattered knowledge system in this field and the lack of standardization exacerbate the difficulty of analyzing roof fall accidents. This study proposes an ontology-based semantic modeling method for roof fall accidents to share and reuse roof fall knowledge for intelligent decision-making. The crucial concepts of roof fall accidents and the correlations between concepts are summarized by analyzing the roof fall knowledge, providing a standard framework to represent the prior knowledge in this field. Besides, the ontology modeling tool Protégé is used to construct the ontology. As for ontology-based deep information mining and semantic reasoning, semantic rules based on expert experience and data fusion technology are proposed to evaluate mines’ potential risks comprehensively. In addition, the roof-falling rules are formalized based on the Jena syntax to make the ontology uniformly expressed in the computer. The Jena reasoning engine is utilized to mine potential tacit knowledge and preventive measures or solutions. The proposed method is demonstrated using roof fall cases, which confirms its validity and practicability. Results indicate that this method can realize the storage, management, and sharing of roof fall accident knowledge. Furthermore, it can provide accurate and comprehensive experience knowledge for the roof fall knowledge requester. Full article
(This article belongs to the Special Issue Advances in Process Safety and Protection of Cyber-Physical Systems (CPS))
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12 pages, 696 KiB  
Article
Nonlinear Dynamic Process Monitoring Using Canonical Variate Kernel Analysis
by Simin Li, Shuang-hua Yang and Yi Cao
Processes 2023, 11(1), 99; https://doi.org/10.3390/pr11010099 - 29 Dec 2022
Cited by 5 | Viewed by 1411
Abstract
Most industrial systems today are nonlinear and dynamic. Traditional fault detection techniques show their limits because they can hardly extract both nonlinear and dynamic features simultaneously. Canonical variate analysis (CVA) shows its excellent monitoring performance in fault detection for dynamic processes but is [...] Read more.
Most industrial systems today are nonlinear and dynamic. Traditional fault detection techniques show their limits because they can hardly extract both nonlinear and dynamic features simultaneously. Canonical variate analysis (CVA) shows its excellent monitoring performance in fault detection for dynamic processes but is not applicable to nonlinear processes. Inspired by the CVA method, a novel nonlinear dynamic process monitoring method, namely, the “canonical variate kernel analysis” (CVKA), is proposed in this work. The way to extract nonlinear features is different from a traditional kernel canonical variate analysis (KCVA). In a sequential structure, the new approach firstly extracts the linear dynamic features from the data through the CVA method, followed by a kernel principal component analysis to extract nonlinear features from the CVA residual space. The new CVKA method is then applied to a TE process case study, proving the excellent performance of CVKA compared to other common approaches in dynamic nonlinear process monitoring for TE-like processes. Full article
(This article belongs to the Special Issue Advances in Process Safety and Protection of Cyber-Physical Systems (CPS))
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19 pages, 2004 KiB  
Article
A Novel Multi-Sensor Data-Driven Approach to Source Term Estimation of Hazardous Gas Leakages in the Chemical Industry
by Ziqiang Lang, Bing Wang, Yiting Wang, Chenxi Cao, Xin Peng, Wenli Du and Feng Qian
Processes 2022, 10(8), 1633; https://doi.org/10.3390/pr10081633 - 17 Aug 2022
Cited by 1 | Viewed by 1662
Abstract
Source term estimation (STE) is crucial for understanding and addressing hazardous gas leakages in the chemical industry. Most existing methods basically use an atmospheric transport and dispersion (ATD) model to predict the concentrations of hazardous gas leakages from different possible sources, compare the [...] Read more.
Source term estimation (STE) is crucial for understanding and addressing hazardous gas leakages in the chemical industry. Most existing methods basically use an atmospheric transport and dispersion (ATD) model to predict the concentrations of hazardous gas leakages from different possible sources, compare the predicted results with multi-sensor data, and use the deviations to search and derive information on the real sources of leakages. Although performing well in principle, complicated computations and the associated computer time often make these methods difficult to apply in real time. Recently, many machine learning methods have also been proposed for the purpose of STE. The idea is to build offline a machine-learning-based STE model using data generated with a high-fidelity ATD model and then apply the machine learning model to multi-sensor data to perform STE in real time. The key to the success of a machine-learning-based STE is that the machine-learning-based STE model has to cover all possible scenarios of concern, which is often difficult in practice because of unpredictable environmental conditions and the inherent robust problems with many supervised machine learning methods. In order to address challenges with the existing STE methods, in the present study, a novel multi-sensor data-driven approach to STE of hazardous gas leakages is proposed. The basic idea is to establish a multi-sensor data-driven STE model from historical multi-sensor observations that cover the situations known as the independent hazardous-gas-leakage scenarios (IHGLSs) in a chemical industry park of concern. Then the established STE model is applied to online process multi-sensor data and perform STE for the chemical industry park in real time. The new approach is based on a rigorous analysis of the relationship between multi-sensor data and sources of hazardous gas leakages and derived using advanced data science, including unsupervised multi-sensor data clustering and analysis. As an example of demonstration, the proposed approach is applied to perform STE for hazardous gas-leakage scenarios wherein a Gaussian plume model can be used to describe the atmospheric transport and dispersion. Because of no need of ATD-model-based online optimization and supervised machine learning, the new approach can potentially overcome many problems with existing methods and enable STE to be literally applied in engineering practice. Full article
(This article belongs to the Special Issue Advances in Process Safety and Protection of Cyber-Physical Systems (CPS))
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16 pages, 2785 KiB  
Article
Combined Grey Wolf Optimizer Algorithm and Corrected Gaussian Diffusion Model in Source Term Estimation
by Yizhe Liu, Yu Jiang, Xin Zhang, Yong Pan and Yingquan Qi
Processes 2022, 10(7), 1238; https://doi.org/10.3390/pr10071238 - 22 Jun 2022
Cited by 8 | Viewed by 1914
Abstract
It is extremely critical for an emergency response to quickly and accurately use source term estimation (STE) in the event of hazardous gas leakage. To determine the appropriate algorithm, four swarm intelligence optimization (SIO) algorithms including Gray Wolf optimizer (GWO), particle swarm optimization [...] Read more.
It is extremely critical for an emergency response to quickly and accurately use source term estimation (STE) in the event of hazardous gas leakage. To determine the appropriate algorithm, four swarm intelligence optimization (SIO) algorithms including Gray Wolf optimizer (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and ant colony optimization (ACO) are selected to be applied in STE. After calculation, all four algorithms can obtain leak source parameters. Among them, GWO and GA have similar computational efficiency, while ACO is computationally inefficient. Compared with GWO, GA and PSO, ACO requires larger population and more iterations to ensure accuracy of source parameters. Most notably, the convergence factor of GWO is self-adaptive, which is in favor of obtaining accurate results with lower population and iterations. On this basis, combination of GWO and a modified Gaussian diffusion model with surface correction factor is used to estimate the emission source term in this work. The calculation results demonstrate that the corrected Gaussian plume model can improve the accuracy of STE, which is promising for application in emergency warning and safety monitoring. Full article
(This article belongs to the Special Issue Advances in Process Safety and Protection of Cyber-Physical Systems (CPS))
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