Process Safety

Chakrabarty, Arnab; Mannan, Sam; Çağın, Tahir

doi:10.1016/b978-0-12-396975-0.00002-4

Cited by 11 publications

(5 citation statements)

References 72 publications

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“…The SAM used for vapor cloud explosion is quantified through the TNT-equivalency method and the determined probability of failure. , The SAM (λ t ) is a function of flow rate ( F s15, t ), release time ( t r ), generic probability of seal failure ( P s,f ), the predicted probability of mechanical failure ( P m,f ), and other parameters reported in Table S2 in the Supporting Information. It is noted that a linear approximation to () is implemented within the range of flow rates corresponding to the design bounds.…”

Section: Resultsmentioning

confidence: 99%

Data-Driven Prescriptive Maintenance: Failure Prediction Using Ensemble Support Vector Classification for Optimal Process and Maintenance Scheduling

Gordon

Burnak

Onel

et al. 2020

Ind. Eng. Chem. Res.

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Maintenance can improve the availability of aging production systems and prevent process safety incidents. However, because of system complexity, resource allocation is nontrivial. This research developed and applied a framework to obtain optimal future-failure aware and safety-conscious production and maintenance schedules. Ensembles of nonlinear support vector machine classification models were leveraged to predict the time and probability of future equipment failure from equipment condition data. Multiobjective optimization of expected profit and a safety metric was then used to determine optimal process and maintenance schedules. The results of this research were that the ensemble models had an average accuracy and an F1-score of 0.987, that the ensemble models were more accurate and sensitive than the individual classifiers by 3 percentage points, and that the Pareto-optimal process and maintenance schedules were obtained, providing alternative solutions to the decision maker. This research described optimal resource allocation to help improve safety and system effectiveness.

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Section: Resultsmentioning

confidence: 99%

Data-Driven Prescriptive Maintenance: Failure Prediction Using Ensemble Support Vector Classification for Optimal Process and Maintenance Scheduling

Gordon

Burnak

Onel

et al. 2020

Ind. Eng. Chem. Res.

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“…France is the third highest country in Europe with Chemery, Lussagnet, and Izaute being the most active gas storage sites [123]. The UGS activities are dangerous because of the potential occurrence of gas leaks from pipelines, which could provoke fires or flammable clouds (flash fire) [101] and ground deformation because of the high rate of gas injection or gas pressure, e.g., [39,69].…”

Section: Discussionmentioning

confidence: 99%

“…Just few monitoring contributions focus on other techniques, such as seismic reflection and ambient seismic noise (9.4%), mainly used to monitor the pilot site of Ketzin located in Germany, e.g., [21,50], and the Aquistore CCS project of Weyburn (Canada), e.g., [52][53][54][55], and GPS/GNSS (6.9%), e.g., [60]. UGS activities can be dangerous because of the potential for gas leaks from pipelines, which could provoke fires or flammable clouds (flash fires) [101] and ground deformation caused by a high rate of gas injection or gas pressure [39]. In fact, methane is highly flammable when mixed with air at certain concentrations, making gas leakage at the ground surface a severe safety hazard and threat to surface infrastructure [102].…”

mentioning

confidence: 99%

Review of the Monitoring Applications Involved in the Underground Storage of Natural Gas and CO2

2022

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Natural gas is an indispensable resource not evenly distributed in the world. The gas supply chain is characterized by large imbalances between supply and demand, where the underground gas storage (UGS) application plays a key role for creating strategic reserves, taking advantage of geological structures. On the contrary, human activities will require clean energy with near-zero greenhouse gas emissions to be environmentally viable. A key element of this strategy is the carbon capture and storage (CCS) application useful for confining CO2 into the geosphere to reduce anthropogenic emissions. The development of appropriate injection methods and long-term monitoring systems for leak detection of the underground storage of natural gas and CO2 is important to prevent negative effects, such as ground deformations and micro seismic events. In this work, a variety of monitoring applications were gathered and critically analyzed for a total of 60 scientific contributions spanning the world. This bibliographic work shows an analytical and statistical overview of the most common use of UGS and CCS, representing the different goals of these two applications and analyzing the main monitoring techniques used in the gathered contributions. Currently, UGS monitoring requires further development, especially through multidisciplinary approaches useful for identifying possible effects on the surface and gas leaks at depth; meanwhile, CCS solutions are still at the experimental stage, also because of the high costs for large-scale applications that still need specific research. The state of the art of these two very different practices can improve the further development of new monitoring approaches or additional methods.

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“…This is because some experimental works are costly, time-consuming, and risky to accomplish. In total, there are three types of theoretical models including empirical and phenomenological models and numerical investigation, also known as computational fluid dynamics (CFD) [12,13]. CFD simulation is an effective and practical tool for the prediction of fluid flow as well as heat and mass transfer in microreactors.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of N‐(3‐Aminio‐4 methylphenyl)benzamide Synthesis in a Microreactor

Cao,

Ghadiri

2023

Chem Eng & Technol

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N‐(3‐Amino‐4‐methylphenyl)benzamide is an intermediate product for the production of several medicines. Microreactor technology is a new concept for the continuous synthesis of chemicals. The computational fluid dynamics method was used to simulate the flow synthesis of N‐(3‐amino‐4‐methylphenyl)benzamide. There was good agreement between experimental data and modeling outputs. The effect of operating parameters such as temperature and residence time on system performance was investigated. The results revealed that an increase in temperature from 30 to 70 °C led to the enhancement of N‐(3‐amino‐4‐methylphenyl)benzamide production from about 42 to 76 % at a residence time of 420 s. It was found that the residence time and temperature have the largest influence on N‐(3‐amino‐4‐methylphenyl)benzamide production. Byproduct production was also increased by increasing the temperature and residence time.

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Process Safety

Cited by 11 publications

References 72 publications

Data-Driven Prescriptive Maintenance: Failure Prediction Using Ensemble Support Vector Classification for Optimal Process and Maintenance Scheduling

Data-Driven Prescriptive Maintenance: Failure Prediction Using Ensemble Support Vector Classification for Optimal Process and Maintenance Scheduling

Review of the Monitoring Applications Involved in the Underground Storage of Natural Gas and CO2

Modeling and Simulation of N‐(3‐Aminio‐4 methylphenyl)benzamide Synthesis in a Microreactor

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