Search results

The increasingly improved automotive vehicle production technology has allowed consumers to purchase passenger cars and commercial vehicles at reasonably affordable prices. This has resulted in greater demands for the construction and installation of motor fuel dispensing facilities, or simply “gas stations”, in virtually all urban districts worldwide. However, unlike most consumer products, which are designed to be fire resistant, highly flammable gasoline dispensed at these facilities is intended and formulated to burn. Therefore, fire safety of motor fuel dispensing facilitates becomes a major concern since these facilities form one of the greatest fire hazards that millions of people visit everyday. This paper seeks to address this issue.

This paper reviews the current international and local legislation and requirements on fire safety in gas station facilities, and presents the findings of a case study conducted to evaluate fire prevention measures and fire protection equipment in five gas station facilities located in the growing City of Al‐Khobar, Saudi Arabia, where dispensing of motor fuel is carried out in an outside shaded area by station attendants.

The evaluation revealed that none of the five gas stations is complying with all fire safety regulations. Recommendations are made to improve fire safety at such facilities by regular inspection and evaluation.

Although the results are limited to Saudi Arabia the approach taken could be extended to other geographical areas.

The paper is of practical value to facility managers responsible for day‐to‐day operations of gas station facilities.

The paper reviews literature from North America as well as that pertaining to the legislation in the country where the case study facilities are located.

Gas transmission pipelines are at constant risk of gas leakage or fire due to various atmospheric environments, corrosion on pipe metal surfaces and other external factors. This study aims to reduce the human and financial risks associated with gas transmission by regularly monitoring pipeline performance, controlling situations and preventing disasters.

Facility managers can monitor the status of gas transmission lines in real-time by integrating sensor information into a building information modeling (BIM) 3D model. Using the Monitoring Panel plugin, coded in C# programming language and operated through Navisworks software, the model provides up-to-date information on pipeline safety and performance.

By collecting project information on the BIM and installing critical sensors, this approach allows facility manager to observe the real-time safety status of gas pipelines. If any risks of gas leakage or accidents are identified by the sensors, the BIM model quickly shows the location of the incident, enabling facility managers to make the best decisions to reduce financial and life risks. This intelligent gas transmission pipeline approach changes traditional risk management and inspection methods, minimizing the risk of explosion and gas leakage in the environment.

This research distinguishes itself from related work by integrating sensor data into a BIM model for real-time monitoring and providing facility managers with up-to-date safety information. By leveraging intelligent gas transmission pipelines, the system enables quick identification and location of potential hazards, reducing financial and human risks associated with gas transmission.

The purpose of this paper is to assess the level of gas supply security enjoyed by the three Baltic States: Latvia, Lithuania and Estonia.

The research is predominantly qualitative and is based on information gathered at interviews, policy analysis and analysis of national statistics on the structure of gas consumption.

The research finds that the three Baltic States enjoy a relatively low level of gas supply security and that of the three, Lithuania, with more extensive dual fuel obligations, is most secure.

The paper presents work on countries in a previously under‐researched region and provides a detailed and tailored analysis of their gas supply security situations and policies.

Gas‐cooled solar heat exchanger units (panels) are envisaged for installation in solar tower power plants. The thermal stresses arising in the panel in solar operation are high and are in part unique, so that design and development of this hot component has to rely on experimental studies. The present paper describes experimental studies done on a panel. For the tests a 3 MW hot‐gas test facility built primarily for the testing of hot components is used. Solar operating conditions are simulated on a panel prototype and the resulting thermal loads measured on critical panel components. Planning and optimizing of the test program as well as the evaluation of test results have been supported with finite element computing. The test results presented here show what stresses are exerted on the components by the solar‐specific operating conditions. Moreover, it is shown how rapidly changing unsteady thermal loads at high temperatures can be precisely detected and evaluated. In combination with efficient modern methods of calculation (e.g. finite element analysis) these results can be also be used to solve similar problems on other hot components.

The purpose of this paper is to develop a decision support tool for risk-based maintenance scheduling for a large heavily equipped gas sweetening unit in a Liquefied Natural Gas (LNG) plant. Two conflicting objectives, i.e., total maintenance cost and the reliability, are considered in the tool. The tool is tested with the real plant data and suggests several Pareto-optimal schedules for a decision maker to choose from. The financial impacts are assessed.

A bi-objective scheduling optimization model is developed for maintenance scheduling using a risk-based framework. The model is developed integrating genetic algorithm and simulation-based optimization to find Pareto-optimal schedules. The model delivered true Pareto front optimal solutions for given plant-specific data. The two conflicting objectives: the minimization of total expenditures incurred on maintenance-related activities and improving the total reliability are considered.

For large and complex processing facilities such as LNG plant, a shutdown of facility generates a significant financial impact, resulting in millions of dollars in production loss. The developed risk-based equipment selection strategy helps to minimize such an event of production loss by generating a thorough maintenance strategy for inspection, repair, overhaul or replacement schedule of the unit without initiating the shutdown. The proposed model has been successfully applied to obtain an optimize maintenance schedule for a gas sweetening unit.

A future work may consider the state-dependent models for various failure modes that will result in obtaining a better representation of the model. The proposed scheduling can further be extended to multi-criteria scheduling including availability, resource limitation and inflationary condition. A comparative analysis with other meta-heuristic techniques such as harmony search algorithm, tabu search, and simulated annealing will further help in confirming the schedule obtained from this application.

Maintenance scheduling using a conventional approach for special equipment generally does not consider the conflicting objectives. This research addresses this aspect using a bi-objective model. The usefulness of risk-based method is to assist in minimizing the financial and safety risk exposure to the operating companies, but some variation in results is expected due to varying risk matrix for different organizations.

Managing two objectives, i.e., minimizing the cost of maintenance-related activities, while at the same time maximizing the overall reliability dramatically, helps in mitigating adverse safety and financial risk due to fires, explosions, fatality and excessive maintenance cost.

Research develops a decision support tool for managing conflicting objectives for an LNG process. This research highlights the impact of utilizing the simulation-based approach coupled with risk-based equipment selection for complex processing unit or plant maintenance scheduling optimization.

Asset intensive process industries are under immense pressure to achieve promised return on investments and production targets. This can be accomplished by ensuring the highest level of availability, reliability and utilization of the critical equipment in processing facilities. In order to achieve designed availability, asset characterization and maintainability play a vital role. The most appropriate and effective way to characterize the assets in a processing facility is based on risk and consequence of failure. The paper aims to discuss these issues.

In this research, a risk-based stochastic modeling approach using a Markov decision process is investigated to assess a processing unit's availability, which is referred as the risk-based availability Markov model (RBAMM). RBAMM will not only provide a realistic and effective way to identify critical assets in a plant but also a method to estimate availability for efficient planning purposes and resource optimization.

A unique risk matrix and methodology is proposed to determine the critical equipment with direct impact on the availability, reliability and safety of the process. A functional block diagram is then developed using critical equipment to perform efficient modeling. A Markov process is utilized to establish state diagrams and create steady-state equations to calculate the availability of the process. RBAMM is applied to natural gas absorption process to validate the proposed methodology. In the conclusion, other benefits and limitations of the proposed methodology are discussed.

A new risk-based methodology integrated with Markov model application of the methodology is demonstrated using a real-life application.

The purpose of this paper is to improve the accuracy of evaluation efficiency by constructing parallel structures considering the main components of industrial pollutants, and then to consider some external influence factors to eliminate random errors.

In this paper, data transformation has been used to deal with undesirable output, and a model with a parallel structure based on the three-stage data envelopment analysis model to calculate the efficiency scores of different division in pollution treatment has been composed.

The analysis shows that the external environmental factors and random factors of the economy and society greatly affect the efficiency of industrial pollutant treatment; moreover, there is an imbalance between regions in China in the treatment of industrial pollutants.

Optimal improvement requires each province to take targeted measures to improve its efficiency of pollutant treatment measures, which are tailored to specific situations and determined by efficiency analysis in this paper.

This paper aims to provide details of the major optical gas sensing techniques and their applications.

Following an introduction, this paper first identifies the major gas sensing technologies and provides an overview of optical sensing techniques. The sources and impact of the gases most frequently sensed by optical methods are listed. Three non-absorption-based and nine absorption-based methods and their main applications are then described in detail. Brief concluding comments are drawn.

All manner of optical gas sensing techniques have been commercialised and while the majority are absorption-based, several other methods also play a significant role. Some optical gas sensors offer advanced capabilities such as remote monitoring, the creation of 2D and 3D distribution maps, detection of parts per trillion levels and even the visualisation of gases in real time. They play a vital role in protecting workers from hazardous gases, controlling and minimising air pollution and monitoring the atmospheric environment, as well as being used in the food, medical, process, power generation and other industries.

This paper provides a detailed insight into optical gas sensing techniques and their uses.

Natural gas makes up about 50 per cent of Pakistan's energy consumption, which is decreasing at an alarming pace, making Karachi – the largest city of Pakistan – one of the major victims of natural gas shortfall. The purpose of this paper is to model natural gas demand and supply using geographic information systems.

Sui Southern Gas Company datasets were used to derive annual per capita gas usages and to analyze the capacity of gas supplies. Union Council is taken as a smallest and basic analytical administrative spatial unit for appraisal of badly affected, with insufficient, low and better gas supply regions.

Results show that the per capita demand for natural gas is higher in high class residential areas while supply is lowest for upper middle and lower middle class residential area populations. Only 19 per cent of the total residential areas are expected to be free from gas shortfalls. About 53 per cent of residential areas are facing the problem of low gas availability; and 3 per cent and 11 per cent areas are under stress of badly affected and insufficient gas supply, respectively.

This research shows an example of constructing a Geographic Information System (GIS)‐based gas demand and supply model that can be used for building strategic guidelines for top‐level planners, engineers and decision makers at the micro‐level. This is not a “deep” paper, but it works in identifying a problem and presenting a quantitative way forward. This paper will have significant impact both on society and academia, as this innovative technology and its implementation on gas utility has happened for the first time in Pakistan. Furthermore, in the international literature this has also been in infancy and will open new avenues of research, especially in developing countries. The work would certainly be applicable for other consumer‐based utilities such as electricity, telecommunication, sewerage and municipal water and they would all benefit from it substantially.