Risk Assessment and Safety Management in the Manufacturing Process

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

Deadline for manuscript submissions: closed (22 December 2023) | Viewed by 1839

Special Issue Editors


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Guest Editor
Politecnico di Torino, Turin, Italy
Interests: risk assessment; risk management; safety; process plant; dynamic risk assessment; process model

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Guest Editor Assistant
Science and Technology Applied Department (DISAT), Multiphase Systems and Chemical Engineering, Safety, Reliability and Risks Centre (SAfeR), Politecnico di Torino (Polito), Turin, Italy
Interests: environmental and quality improvement; risk-based approach in the field of process industry and its incidence with the territory

Special Issue Information

Dear Colleagues,

The topic of safety is relevant to all types of production environments. From process safety to occupational safety or product safety, safety can be applied at different levels. A number of new challenges have been faced by safety today, including the introduction of new production technologies (e.g. additive manufacturing or process intensification) as well as new monitoring opportunities for critical equipment (such as Internet of Things systems - IoT), the impact of climate change (as the increase of NaTech events), and logistics issues (as the Covid19 pandemic). 

Modern safety research must investigate new types of risk assessment methodologies and risk management techniques to address these challenges. For modern process plants, in fact, the resilience of the plant is becoming an important element of the safety concept, as is the ability of the system to handle disruptions.

This Special Issue on “Risk Assessment and Safety Management in the Manufacturing Process” aims at gaining a deeper insight into the modern concept of safety and how it can adapt to the new challenges present in the modern process industry. 

In particular papers related to the risk assessment methodologies and the management of safety developed to address new technologies and equipment, climate change effect, and the supply chain risks, will be included in this special issue.

Suitable topics include but are not limited to:

  • Risk Assessment advanced methodologies;
  • Safety Management systems;
  • Resilience of the manufacturing process;
  • Risk in the supply chain;
  • Climate change related hazards;
  • Emerging risk;
  • Dynamic risk assessment;
  • Cyber security in the process and manufacturing plant;
  • Na-Tech risk.

Dr. Gabriele Baldissone
Guest Editor
David Castro
Guest Editor Assistant

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

  • risk assessment
  • risk management
  • safety
  • resilience

Published Papers (2 papers)

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Research

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33 pages, 1475 KiB  
Article
Enhancing Control Room Operator Decision Making
by Joseph Mietkiewicz, Ammar N. Abbas, Chidera W. Amazu, Gabriele Baldissone, Anders L. Madsen, Micaela Demichela and Maria Chiara Leva
Processes 2024, 12(2), 328; https://doi.org/10.3390/pr12020328 - 02 Feb 2024
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Abstract
In the dynamic and complex environment of industrial control rooms, operators are often inundated with numerous tasks and alerts, leading to a state known as task overload. This condition can result in decision fatigue and increased reliance on cognitive biases, which may compromise [...] Read more.
In the dynamic and complex environment of industrial control rooms, operators are often inundated with numerous tasks and alerts, leading to a state known as task overload. This condition can result in decision fatigue and increased reliance on cognitive biases, which may compromise the decision-making process. To mitigate these risks, the implementation of decision support systems (DSSs) is essential. These systems are designed to aid operators in making swift, well-informed decisions, especially when their judgment may be faltering. Our research presents an artificial intelligence (AI)-based framework utilizing dynamic influence diagrams and reinforcement learning to develop a powerful decision support system. The foundation of this AI framework is the creation of a robust, interpretable, and effective DSS that aids control room operators during critical process disturbances. By incorporating expert knowledge, the dynamic influence diagram provides a comprehensive model that captures the uncertainties inherent in complex industrial processes. It excels in anomaly detection and recommending optimal actions. Furthermore, this model is improved through a strategic collaboration with reinforcement learning, which refines the recommendations to be more context-specific and accurate. The primary goal of this AI framework is to equip operators with a live, reliable DSS that significantly enhances their response during process upsets. This paper describes the development of the AI framework and its implementation in a simulated control room environment. Our results show that the DSS can improve operator performance and reduce cognitive workload. However, it also uncovers a trade-off with situation awareness, which may decrease as operators become overly dependent on the system’s guidance. Our study highlights the necessity of balancing the advantages of decision support with the need to maintain operator engagement and understanding during process operations. Full article
(This article belongs to the Special Issue Risk Assessment and Safety Management in the Manufacturing Process)
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Review

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26 pages, 4715 KiB  
Review
Exploring Safety of Machineries and Training: An Overview of Current Literature Applied to Manufacturing Environments
by Maria Elena Del Giudice, Mahnaz Sharafkhani, Mario Di Nardo, Teresa Murino and Maria Chiara Leva
Processes 2024, 12(4), 684; https://doi.org/10.3390/pr12040684 - 28 Mar 2024
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Abstract
A machine is described as an assembly that has a drive system installed or is planned to have a drive system installed and that is constituted of linked elements or components, at least one of which moves, that are connected for a particular [...] Read more.
A machine is described as an assembly that has a drive system installed or is planned to have a drive system installed and that is constituted of linked elements or components, at least one of which moves, that are connected for a particular application (ISO12100). Different types of risks are present in machines, and exposure to them can cause harm or even death. When risk has been adequately reduced, machinery safety considers a machine’s ability to complete its intended duty throughout its life cycle. A literature review was carried out using “safety of machinery” as a keyword, which produced an analysis of 29 papers published from 2008 to 2024. The papers were examined through bibliometric analysis of the year of publication, country, citation statistics, and study of the keywords. These studies were classified into accident analysis papers, papers focused on the normative, papers that addressed risk assessment tools, and papers that conducted quantitative research. In addition, a more in-depth analysis of the articles associated with the keywords with the highest number of occurrences was carried out. Lastly, studies with quantitative analyses were analysed to identify new possible aspects that it is necessary to investigate. Full article
(This article belongs to the Special Issue Risk Assessment and Safety Management in the Manufacturing Process)
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