Process safety is becoming a greater focus of chemical plant design and operation due to the number of incidents involving dangerous chemical accidents. Since its creation nearly 20 years ago, the Chemical Safety Board (CSB) has investigated 130 safety incidents and provided over 800 safety recommendations to operating chemical facilities. Following a gas well blowout in 2018, the CSB gave a recommendation to the American Petroleum Institute (API) to establish recommended practice on alarm management. Similarly, in 2017, the CSB gave a recommendation to Arkema Inc. to update their emergency response training following a hurricane that caused a fire at one of their manufacturing sites. Many times, CSB-led investigations resulted in new regulations and standards that are enforced by the Occupational Safety and Health Administration (OSHA) or the Environmental Protection Agency (EPA). These critical recommendations positively impact not only the plant workers but also the surrounding community and the environment.
While these safety measures enhance industrial safety culture, it is important that process safety also be integrated into university-level engineering curricula to promote safety culture while future engineers are still developing. Integrating process safety into the curriculum prepares students by familiarizing them with the difficult decisions they will be required to make in professional practice. ABET, the engineering program accreditation body, acknowledges the value of early, appropriate training within their program guidelines “Criteria for Chemical Engineering Curriculum” which states that recognition and assessment of the hazards associated with chemical processes must be included in the curriculum for program accreditation. Based on this requirement, many institutions have taken the approach to integrate process safety into their curriculum using video case studies, adding entire courses to cover hazard identification, and including safety lectures in design courses. A common theme missing from these methods is instruction on how to approach, recognize, and navigate decisions within a process safety context; a lack of this situational awareness was noted as a key element in industrial process safety incidents.
Understanding how students approach process safety decisions is important for developing teaching methods and curriculum that will better prepare them for professional practice. As part of this study, we will measure how students rank criteria associated with process safety decisions, and how these prioritizations change after exposure to a process safety decision making intervention. Through this work, we hope to determine how process safety curriculum may be improved to help better prepare students for process safety decisions within industry.
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Predicting Natural Gas Pipeline Failures Caused by Natural Forces: An Artificial Intelligence Classification Approach
Pipeline networks are a crucial component of energy infrastructure, and natural force damage is an inevitable and unpredictable cause of pipeline failures. Such incidents can result in catastrophic losses, including harm to operators, communities, and the environment. Understanding the causes and impact of these failures is critical to preventing future incidents. This study investigates artificial intelligence (AI) algorithms to predict natural gas pipeline failures caused by natural forces, using climate change data that are incorporated into pipeline incident data. The AI algorithms were applied to the publicly available Pipeline and Hazardous Material Safety Administration (PHMSA) dataset from 2010 to 2022 for predicting future patterns. After data pre-processing and feature selection, the proposed model achieved a high prediction accuracy of 92.3% for natural gas pipeline damage caused by natural forces. The AI models can help identify high-risk pipelines and prioritize inspection and maintenance activities, leading to cost savings and improved safety. The predictive capabilities of the models can be leveraged by transportation agencies responsible for pipeline management to prevent pipeline damage, reduce environmental damage, and effectively allocate resources. This study highlights the potential of machine learning techniques in predicting pipeline damage caused by natural forces and underscores the need for further research to enhance our understanding of the complex interactions between climate change and pipeline infrastructure monitoring and maintenance.
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- Award ID(s):
- 2119691
- NSF-PAR ID:
- 10422258
- Date Published:
- Journal Name:
- Applied Sciences
- Volume:
- 13
- Issue:
- 7
- ISSN:
- 2076-3417
- Page Range / eLocation ID:
- 4322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/app13074322
