Editorial

Editoria

Multi-objective Optimization of Firefighting Strategies in Process Plants

Ideal firefighting strategies at process plants would include simultaneous suppressing and cooling of all the burning and exposed units, respectively, if firefighting resources are sufficient. As a result, the primary fires can be contained and their escalation into secondary fires via domino effect can be prevented until the fires are fully extinguished. However, when the number of burning units to suppress and exposed units to cool exceeds the firefighting capacity of a process plant, it is not feasible to conduct an ideal firefighting. Consequently, the plant owners need to conduct an optimal firefighting to address the following question: When all the burning and exposed units cannot be considered in firefighting, which ones should be prioritized and included in firefighting so that the risk of fire propagation in the plant can be minimized? For process plants that are close to other land-use developments (residential communities, infrastructures, etc.), conducting an optimal firefighting can become more challenging as it should minimize not only the risk of domino effect within the plant (onsite risk) but also risks posed to the nearby land-use developments (offsite risks). In the present study, considering onsite and offsite risks that may arise from domino effects in process plants, a methodology is developed based on goal programming – a multi-objective optimization technique – for identifying optimal firefighting strategies. Given limited firefighting resources, the developed methodology helps determine which units to suppress and which ones to cool in order to minimize as many risks as possible

Vulnerability Assessment of Process Vessels in the Event of Hurricanes

Hurricanes are multi-hazard natural hazards that can cause severe damage to chemical and process plants via individual or combined impact of strong winds, torrential rainfall, floods, and hitting waves especially in coastal areas. To assess and manage the vulnerability of process plants, failure modes and respective failure probabilities both before and after implementing safety measures should be assessed. However, due to the uncertainties arising from interdependent failure modes and lack of accurate and sufficient historical data, most conventional quantitative risk assessment techniques deliver inaccurate results, which in turn lead to inaccurate risk assessment and thus ineffective or non-cost-effective risk management strategies. Bayesian network (BN) is a probabilistic technique for reasoning under uncertainty with a variety of applications is system safety, reliability engineering, and risk assessment. In this chapter, applications of BN to vulnerability assessment and management of process vessels in the event of hurricanes are demonstrated and discussed

Review of Geochimical, Isotopic and Fluid Inclusions Studies in Ramand Region (Qazvin Province)

Ramand copper deposit is an example of vein-bearing deposits with volcanic host located in the Urumieh-Dokhtar zone. The deposit host is an Eocene volcanic sequence and the main host’s rock is the rhyolite mineral. The main minerals are chalcopyrite, pyrite, covellite and natural gold; and the tailings minerals include quartz, calcite and sericite. The average grade of gold in silica veins is 133.5 ppb, the average grade of copper is about 3.5% and the average grade of molybdenum is 135 ppm. Quartz-sulfide hydrothermal veins contain biphasic fluid-rich fluid inclusions and monophasic fluid-rich fluid inclusions. The homogenization temperature ranged from 73 to 307 ° C with an average of 141 ° C and in all samples, homogenization was carried out through the liquid phase and salinity variations ranged from 1.75 to 4.74 with an average of 3.65 wt% NaCl equivalent. Quartz and calcite oxygen isotope values range between 4.4 to 9.4 per thousand. Isotopic data indicate that the ore-generating fluids in the Ramand ore deposit have relatively low salinity and atmospheric-magmatic origin. According to this study, Ramand’s mineralization range is the result of hydrothermal activity in the area where mineralization with simple mineralogical characteristics has occurred in siliceous veins and sub-veinsO depósito de cobre de Ramand é um exemplo de depósitos contendo veias de origem vulcânica localizado na zona de Urumieh-Dokhtar. O depósito é uma sequência de origem vulcânica do Eoceno e a rocha principal encontrada na área é o riolito. Os principais minerais são calcopirita, pirita, covellite e ouro natural; e os minerais de rejeitos incluem quartzo, calcita e sericita. O teor médio de ouro nas veias de sílica é de 133,5 ppb, o teor médio de cobre é de cerca de 3,5% e o teor médio de molibdênio é de 135 ppm. As veias hidrotermais de sulfeto de quartzo contêm inclusões fluidas ricas em líquidos bifásicos e inclusões fluidas ricas em líquidos monofásicos. A temperatura de homogeneização variou de 73 a 307°C com uma média de 141°C e em todas as amostras, a homogeneização foi realizada na fase líquida e as variações de salinidade variaram de 1,75 a 4,74 com uma média de 3,65% em peso de NaCl equivalent

Optimal Evacuation of Process Plants in Case of Tank Fires

Effective firefighting and evacuation as integral parts of emergency response in petrochemical plants play a key role in protecting human lives in the event of major tank fires. Compared to firefighting, however, studies devoted to planning and optimizing evacuation plans in the event of tank fires, and particularly concurrent tank fires, have been very few. In the present study, considering the thermal dose as the main cause of casualties in outdoor fires, an innovative methodology is developed to identify proactive evacuation plans for credible fire scenarios. The methodology consists of three main parts: (1) For a given fire scenario (e.g., a single or multiple tank fires), the tank terminal is modelled as a thermal graph in which the weight of each node presents the corresponding heat flux, and the weight of each edge presents the thermal dose between the connected nodes; (2) Dijkstra’s algorithm is used to find the shortest paths (a series of connected edges with the least total thermal dose) to the safe spots (e.g., shelters); (3) Considering the limited capacities of safe spots, mathematical programming is used to identify the number of evacuees to be assigned to each safe spot so as to minimize the total risk of casualties during evacuation. Application of the methodology to an illustrative process plant resulted in intuitive evacuation plans, which is indicative of the methodology’s validity, particularly in the absence of similar studies for comparison and validation purposes

An Approach to Update the Failure Rates of Safety Barriers Based on Operating Experience

Hazardous events in process plants like the leakage of dangerous substances can result in severe damage, and such an event is often defined as the TOP event of a fault tree analysis (FTA) in a quantitative risk analysis. The TOP event probability can then be calculated if the basic events probabilities are provided. These probabilities are often determined based on generic reliability data which do not necessarily reflect the operational and environmental characteristics of a plant of interest. This paper presents an approach based on Bayesian network (BN) analysis to explicitly include experience data collected during the plant operation to make the generic probabilities more plant specific. The approach is illustrated via a pressure vessel containing a toxic substance in an Ammonia production plant. In this case study, the failure rate distribution in the BN is updated as the new information becomes available during plant operation. The results show that the suggested approach effectively reflects the operating experience of a specific plant.publishedVersio

A Graph Theoretic Approach to Optimal Firefighting in Oil Terminals

Effective firefighting of major fires in fuel storage plants can effectively prevent or delay fire spread (domino effect) and eventually extinguish the fire. If the number of firefighting crew and equipment is sufficient, firefighting will include the suppression of all the burning units and cooling of all the exposed units. However, when available resources are not adequate, fire brigades would need to optimally allocate their resources by answering the question “which burning units to suppress first and which exposed units to cool first?„ until more resources become available from nearby industrial plants or residential communities. The present study is an attempt to answer the foregoing question by developing a graph theoretic methodology. It has been demonstrated that suppression and cooling of units with the highest out-closeness index will result in an optimum firefighting strategy. A comparison between the outcomes of the graph theoretic approach and an approach based on influence diagram has shown the efficiency of the graph approach

Dynamic safety analysis using advanced approaches

Process systems are prone to accidents as they deal with hazardous material at high temperature and/or pressure. Process plants are also characterized as complex systems where a dense cluster of pipes and equipment may cause a chain of accidents. Therefore, implementation and maintenance of safety measures through risk assessment is crucial to maintain risk below the acceptance criteria. Risk assessment methodologies such as quantitative risk analysis (QRA) and probabilistic safety analysis (PSA) comprise different steps among which accident scenario analysis is a common task. Accident scenario analysis includes accident sequence modeling and associated consequence assessment. Among many techniques available to conduct accident scenario analysis, bow-tie (BT) and Bayesian network (BN) are the most popular. Both techniques are graphical methods illustrating an accident scenario completely and taking advantage of robust probabilistic reasoning engines. BT technique addresses causes and consequences of an accident scenario in a transparent manner that is readily tractable and communicable with stakeholders. However, it suffers limitations of being static and unable to model conditional dependencies. These limitations significantly reduce BT's efficacy to do dynamic risk analysis. In the present study, Bayesian updating and real-time monitoring of operational parameters in the form of physical reliability models are used to overcome these limitations. Physical reliability models provided the analyst with a deeper insight into the behavior of risk while Bayes' rule helps to capture variations over time and to learn from experiences. Bayesian network is an alternative technique to conventional methods such as fault tree and bow-tie, with ample potential in risk assessment and safety analysis. Mapping fault tree and bow-tie into Bayesian network, it is shown that how conditional dependencies, multi-state variables, common cause failures can be considered and most importantly, probability updating can be conducted. Advanced aspects of Bayesian networks such as object-oriented Bayesian networks (OOBN) and discrete-time Bayesian networks (DTBN) are examined in this study. The former decomposes a large network to sub-models with desired level of abstraction, facilitating the modeling and capturing of dependencies. The latter explicitly takes time into account to model sequential failures by means of dynamic gates. To improve the performance of DTBN, an innovative algorithm is introduced to reduce the size of probability tables. Further, two new relationships are developed for dynamic gates cold spare and sequential enforcing gates to make them compatible with most distribution functions. Applying Bayesian networks in the field of domino effects, both propagation pattern and probability of domino effect at different stages are calculated. In this study, the efficacy of BN in safety analysis and accident scenario modeling of a variety of applications such as loss of well control, risk-based design of safety systems and domino effect is examined