scholarly journals Risk Assessment of a Gas Plant (Unit 30 Skikda Refinery) Using Hazop &Bowtie Methods, Simulation of Dangerous Scenarios Using ALOHA Software

2020 ◽  
Vol 5 (1) ◽  
pp. 25-32
Author(s):  
Riad BENDIB ◽  
Elarkam MECHHOUD ◽  
Hanane BENDJAMA ◽  
Halima BOULKSIBAT
Keyword(s):  
Risk Assessment ◽  
Process Control ◽  
Crude Oil ◽  
Gas Separation ◽  
Chemical Processes ◽  
Industrial Plants ◽  
Operational Costs ◽  
Process Plant ◽  
Process Plants ◽  
Large Systems

Process plant and chemical processes are complex and large systems, consisting of thousands of devices interacting with each other [76][45]. The structure of this type of industrial plants lead to difficulties in process control, hence deviations from the operation objectives or desired states. These deviations creates abnormal situations that drive products out of specification, increased operational costs, shut downs and even worse they can cause accidents, which may lead to damages of equipment, environment and affect the health. identifying hazards is fundamental for ensuring the safe design and operation in process plants. Based on the idea of learning from accidents, several techniques and standards are available to identify hazardous situations and help companies to build up strategies to avoid the hazards. In this s work we present a study based on two methods BOWTIE and HAZOP methods applied for an LPG a plant for gas separation located in SKIKDA refinery which is considered as the most important refinery in Algeria where the treatment capacity reaches more than 15 millions tones per year of crude oil. Several recommendations raised from our study to improve the safety of the plant particularly since it is considered as an old plant start working since 1980. The study is completed by simulating the deduced dangerous scenarios using ALOHA software.

scholarly journals Risk Assessment and Deployment for Safety Showers and Eyewash Stations in the Process Plant Industry

2021 ◽  
Vol 18 (16) ◽  
pp. 8707
Author(s):  
Jae-Young Choi ◽  
Sang-Hoon Byeon
Keyword(s):  
Risk Assessment ◽  
Industrial Hygiene ◽  
Process Equipment ◽  
Process Safety ◽  
Plant Industry ◽  
Hazardous Chemicals ◽  
Safety Improvement ◽  
Process Plant ◽  
Process Plants ◽  
Operator Safety

Safety showers and eyewash stations are equipment used for primary washing if their operator is exposed to hazardous chemicals. Therefore, safety showers and eyewash stations should be installed to ensure operator safety in process plants with excessive hazardous chemicals. International guidelines related to safety showers and eyewash stations are introduced in ANSI Z358.1, BS EN 15154, and German DIN 12899-3:2009, but only mechanical specifications regarding safety showers and eyewash stations are suggested. As such, there are currently no engineering guidelines, books, or technical journal papers requiring safety showers or eyewash stations and their efficient deployment. Thus, this study conducted risk assessment from an industrial hygiene perspective, suggesting which process equipment requires a safety shower and eyewash, including their economical and efficient deployment for operator safety. In industry, safety showers and eyewash stations are considered part of the process safety field; this study attempted to contribute to the safety improvement of operators by applying risk assessment of the industrial hygiene field. More studies are needed that contribute to operators’ safety by incorporating industrial hygiene fields for other process safety fields, including safety showers and eyewash stations.

scholarly journals Resilience of Process Plant: What, Why, and How Resilience Can Improve Safety and Sustainability

2020 ◽  
Vol 12 (15) ◽  
pp. 6152 ◽  
Author(s):  
Hans Pasman ◽  
Kedar Kottawar ◽  
Prerna Jain
Keyword(s):  
Risk Assessment ◽  
Oil And Gas ◽  
Research Work ◽  
Business Environment ◽  
Warning Signals ◽  
Plant Safety ◽  
Chemical Manufacturing ◽  
Process Plant ◽  
Process Plants ◽  
Proactive Measures

Resilience is the ability to restore performance after sustaining serious damage by a usually unexpected threat. This paper analyzes resilience of process plants as there are oil and gas refining, chemical manufacturing, power-producing plants, and many more. Over the years, plant safety has shifted from retrospective to proactive measures. Safety is important from many points of view, such as protection of workforce and nearby population, but certainly too from an economical and sustainability aspect. Pro-action requires predictive insight of what in the process can go wrong because of internal or external disruptive disturbance. Over the years, to that end, much effort was spent developing risk assessment methods and management. However, risk assessment has proven to be fallible because of various uncertainties and not the least by overlooked or unknown threats. To protect against those upsetting threats, measures can be taken up to a certain limit. These start in designing error-tolerant equipment able to be receptive to early warning signals during operations, responding to those with ‘plasticity’ of mind (that is, an organization and its leadership especially able to think ‘outside-the box’ for coping with unexpected situations), and finally, to deploy effective emergency response and able to recover from damage quickly. The paper presents a summary/review of nearly a decade of research work at the Mary Kay O’Connor Process Safety Center at the Texas A&M University to develop the concept and the techniques to realize a resilient plant, so far with a focus on chemical plant. It is, however, still a ‘work-in-progress’; potential is large. Besides the conceptual details, cases are presented that show how human and technical factors, combined in a socio-technical system, can lead to a broader plant safety insight enabling more effective risk control and increased resilience. These cases have up to now only considered warning signals and possible management action, while still limited to internal threats. Hence, aspects of equipment design and recovery should be further considered, also in the light of the dynamics of present-day business environment.

On the Use of Proper Fragility Models for Quantitative Seismic Risk Assessment of Process Plants in Seismic Prone Areas

2017 ◽  
Author(s):  
Silvia Alessandri ◽  
Antonio C. Caputo ◽  
Daniele Corritore ◽  
Giannini Renato ◽  
Fabrizio Paolacci ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Seismic Risk ◽  
Structural Damage ◽  
Fragility Curves ◽  
Probabilistic Method ◽  
Quantitative Risk Assessment ◽  
Actual Process ◽  
Consequence Analysis ◽  
Process Plant ◽  
Process Plants

Quantitative Risk Assessment (QRA) is a classical method for the calculation of risk in process plants, which is based on the logic of the consequence analysis. This intrinsically probabilistic method has been thought for classical accident conditions, where the damage events and the relevant consequences start from a preselected component and a standard loss of containment (LOC) and follow all possible scenarios for the calculation of individual and societal risk. This final risk metric is usually expressed in terms of probability of fatality in a specific location of the surrounding area or a certain number of fatalities in the area surrounding the accident. In presence of Na-Tech events, like earthquakes, a multi-source condition can be caused by multi-damage conditions simultaneously involving more than one equipment, which in turn can generate a multiple-chain of events and consequences. In literature, several attempts of modifying the classic QRA approach to account for this important aspect have been formalized without converging toward a unified approach. In this paper, a fragility-based method for Quantitative Seismic Risk Analysis (QSRA) of a process plant is investigated. This method takes into account all possible damage/losses of containment conditions in the most critical equipment, e.g., storage tanks. Fragility curves, which are analytically evaluated for each unit with respect to its seismic damage conditions, are utilized inside the procedure. The Monte Carlo Simulation (MCS) method is then used with the aim to follow all steps of QSRA. In particular, starting from the seismic hazard curve of the site where the plant is placed, a multi-level approach is proposed. In this approach, the first level is represented by the components seismically damaged, whereas the following levels are treated through a classical consequence analysis, including the propagation of multiple simultaneous and interacting chains of accidents. These latter are applied by defining proper correspondences for all relevant equipment between structural damage (i.e., limit states) and LOC events. The application of the method to an actual process plant permits to investigate its high potentiality and the dependency of the risk assessment from the proper fragility models.

scholarly journals Crude Oil Contaminated Sites: Evaluation by Using Risk Assessment Approach

2017 ◽  
Vol 9 (8) ◽  
pp. 1365 ◽  
Author(s):  
Diana Cocârţă ◽  
Mihaela Stoian ◽  
Aykan Karademir
Keyword(s):  
Risk Assessment ◽  
Crude Oil ◽  
Contaminated Sites ◽  
Assessment Approach

scholarly journals Towards Semi-Automatic Generation of a Steady State Digital Twin of a Brownfield Process Plant

2020 ◽  
Vol 10 (19) ◽  
pp. 6959
Author(s):  
Seppo Sierla ◽  
Lotta Sorsamäki ◽  
Mohammad Azangoo ◽  
Antti Villberg ◽  
Eemeli Hytönen ◽  
...  
Keyword(s):  
Simulation Model ◽  
Great Majority ◽  
Automatic Generation ◽  
Digital Twin ◽  
Design Alternatives ◽  
Process Plant ◽  
Process Plants ◽  
Engineering Effort ◽  
Process Measurements

Researchers have proposed various models for assessing design alternatives for process plant retrofits. Due to the considerable engineering effort involved, no such models exist for the great majority of brownfield process plants, which have been in operation for years or decades. This article proposes a semi-automatic methodology for generating a digital twin of a brownfield plant. The methodology consists of: (1) extracting information from piping and instrumentation diagrams, (2) converting the information to a graph format, (3) applying graph algorithms to preprocess the graph, (4) generating a simulation model from the graph, (5) performing manual expert editing of the generated model, (6) configuring the calculations done by simulation model elements and (7) parameterizing the simulation model according to recent process measurements in order to obtain a digital twin. Since previous work exists for steps (1–2), this article focuses on defining the methodology for (3–5) and demonstrating it on a laboratory process. A discussion is provided for (6–7). The result of the case study was that only few manual edits needed to be made to the automatically generated simulation model. The paper is concluded with an assessment of open issues and topics of further research for this 7-step methodology.

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