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Safety
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Safety and Risk Management in Digitalized Process Systems
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Safety and Risk Management in Digitalized Process Systems

A special issue of Safety (ISSN 2313-576X).

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 7110

Special Issue Editor

Dr. Md Tanjin Amin

E-Mail Website
Guest Editor
Mary Kay O'Connor Process Safety Center (MKOPSC), Texas A&M University, College Station, TX 77843, USA
Interests: fault diagnosis; safety analysis; data-driven models; cyber-physical system safety; risk assessment; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Process safety has always been a concern, from the dawn of industrialization. Due to catastrophic accidents worldwide, the importance of this unique research field was perceived, and several phenomenal research works helped to significantly reduce the number of accidents and provide us with a safer world.

However, due the inception of Industry 4.0 and the increasing trend of process digitalization, the conventional safety analysis technqiues need to be upgraded to handle the new challenges. These new adoptions are creating more data. How these data are used will shape the future of process safety and risk management.

This Special Issue on “Safety and Risk Management in Digitalized Process Systems” aims to be a repository of state-of-the-art progress on inherent safety, hazard identification, safety culture, human factor, domino effect, fault detection and diagnosis, dynamic risk assessment and management, human–machine conflict, accident modelling, consequence modelling, resilience analysis, and security analysis of digitalized process systems. Both review and original research articles will be considered for this Special Issue.

Dr. Md Tanjin Amin
Guest Editor

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. Safety is an international peer-reviewed open access quarterly 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 1800 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

  • inherent safety
  • safety culture
  • human factor
  • fault detection and diagnosis
  • dynamic risk analysis
  • process safety management
  • resilience analysis

Published Papers (2 papers)

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Research

17 pages, 1975 KiB  
Article
A Deep-Learning Approach to Driver Drowsiness Detection
by Mohammed Imran Basheer Ahmed, Halah Alabdulkarem, Fatimah Alomair, Dana Aldossary, Manar Alahmari, Munira Alhumaidan, Shoog Alrassan, Atta Rahman, Mustafa Youldash and Gohar Zaman
Safety 2023, 9(3), 65; https://doi.org/10.3390/safety9030065 - 13 Sep 2023
Cited by 5 | Viewed by 5074
Abstract
Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. [...] Read more.
Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. To address the issue of road safety, the proposed model offers a method for evaluating the level of driver fatigue based on changes in a driver’s eyeball movement using a convolutional neural network (CNN). Further, with the help of CNN and VGG16 models, facial sleepiness expressions were detected and classified into four categories (open, closed, yawning, and no yawning). Subsequently, a dataset of 2900 images of eye conditions associated with driver sleepiness was used to test the models, which include a different range of features such as gender, age, head position, and illumination. The results of the devolved models show a high degree of accountability, whereas the CNN model achieved an accuracy rate of 97%, a precision of 99%, and recall and F-score values of 99%. The VGG16 model reached an accuracy rate of 74%. This is a considerable contrast between the state-of-the-art methods in the literature for similar problems. Full article
(This article belongs to the Special Issue Safety and Risk Management in Digitalized Process Systems)
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15 pages, 2728 KiB  
Article
Online Process Safety Performance Indicators Using Big Data: How a PSPI Looks Different from a Data Perspective
by Paul Singh, Coen van Gulijk and Neil Sunderland
Safety 2023, 9(3), 62; https://doi.org/10.3390/safety9030062 - 04 Sep 2023
Cited by 3 | Viewed by 1299
Abstract
This work presents a data-centric method to use IoT data, generated from the site, to monitor core functions of safety barriers on a batch reactor. The approach turns process safety performance indicators (PSPIs) into online, globally available safety indicators that eliminate variability in [...] Read more.
This work presents a data-centric method to use IoT data, generated from the site, to monitor core functions of safety barriers on a batch reactor. The approach turns process safety performance indicators (PSPIs) into online, globally available safety indicators that eliminate variability in human interpretation. This work also showcases a class of PSPIs that are reliable and time-dependent but only work in a digital online environment: profile PSPIs. It is demonstrated that the profile PSPI opens many new opportunities for leading indicators, without the need for complex mathematics. Online PSPI analyses were performed at the Syngenta Huddersfield Manufacturing Centre, Leeds Road, West Yorkshire, United Kingdom, and shared with their international headquarters in Basel, Switzerland. The performance was determined with industry software to extract time-series data and perform the calculations. The calculations were based on decades of IoT data stored in the AVEVA Factory Historian. Non-trivial data cleansing and additional data tags were required for the creation of relevant signal conditions and composite conditions. This work demonstrates that digital methods do not require gifted data analysts to report existing PSPIs in near real-time and is well within the capabilities of chemical (safety) engineers. Current PSPIs can also be evaluated in terms of their effectiveness to allow management to make decisions that lead to corrective actions. This improves significantly on traditional PSPI processes that, when reviewed monthly, lead to untimely decisions and actions. This approach also makes it possible to review PSPIs as they develop, receiving notifications of PSPIs when they reach prescribed limits, all with the potential to recommend alternative PSPIs that are more proactive in nature. Full article
(This article belongs to the Special Issue Safety and Risk Management in Digitalized Process Systems)
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