Journal articles: 'Gas networks'

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van der Linden, Ruud, Ryvo Octaviano, Huib Blokland, and Tom Busking. "Security of Supply in Gas and Hybrid Energy Networks." Energies 14, no. 4 (February 3, 2021): 792. http://dx.doi.org/10.3390/en14040792.

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Reliable energy supply becomes increasingly complex in hybrid energy networks, due to increasing amounts of renewable electricity and more dynamic demand. Accurate modeling of integrated electricity and gas distribution networks is required to quantify operational bottlenecks in these networks and to increase security of supply. In this paper, we propose a hybrid network solver to model integrated electricity and gas distribution networks. A stochastic method is proposed to calculate the security of supply throughout the networks, taking into account the likelihood of events, operational constraints and dynamic supply and demand. The stochastic method is evaluated on a real gas network case study. The calculated security of supply parameters provide insight into the most critical parts of the network and can be used for future network planning. The capabilities of the coupled hybrid energy network simulation are demonstrated on the real gas network coupled to a simplified electricity network. Results demonstrate how combined simulation of electricity and gas networks facilitate the control design and performance evaluation of regional hybrid energy networks.

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Ziehn, T., R. M. Law, P. J. Rayner, and G. Roff. "Designing optimal greenhouse gas monitoring networks for Australia." Geoscientific Instrumentation, Methods and Data Systems 5, no. 1 (January 19, 2016): 1–15. http://dx.doi.org/10.5194/gi-5-1-2016.

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Abstract. Atmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of ground-based observing stations that supply these measurements can be determined by network design. Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimise the uncertainty on the flux estimates for methane (CH4) and nitrous oxide (N2O), both individually and in a combined network using multiple objectives. Optimal networks are generated by adding up to five new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CH4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. Our results suggest that, while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size.

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Ziehn, T., R. M. Law, P. J. Rayner, and G. Roff. "Designing optimal greenhouse gas monitoring networks for Australia." Geoscientific Instrumentation, Methods and Data Systems Discussions 5, no. 2 (August 5, 2015): 247–83. http://dx.doi.org/10.5194/gid-5-247-2015.

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Abstract. Atmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of ground based observing stations that supply these measurements can be determined by network design. Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimize the uncertainty on the flux estimates for methane (CH4) and nitrous oxide (N2O), both individually and in a combined network using multiple objectives. Optimal networks are generated by adding up to 5 new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CH4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. Our results suggest that while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size.

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Chavan, Ashwini. "Design of Natural Gas Pipeline." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 15, 2021): 733–37. http://dx.doi.org/10.22214/ijraset.2021.37468.

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India today has an in depth network of underground pipelines used for the transportation and distribution of gas. Large factories, fertilizer factories and other industrial enterprises are the most consumers in PNG and today, however, with the rise in its popularity, it's currently utilized in the domestic sector similarly as a fuel within the automotive sector in large metropolitan cities. To bring gas to those end users within the boundaries of a significant city, it's necessary to create city gas distribution pipeline networks. India today has an intensive network of underground pipelines used for the transportation and distribution of fossil fuel. Large factories, fertilizer factories and other industrial enterprises are the most consumers in PNG and today, however, with the rise in its popularity, it's currently employed in the domestic sector additionally as a fuel within the automotive sector in large metropolitan cities. To bring gas to those end users within the boundaries of a significant city, it's necessary to create city gas distribution pipeline networks, these networks have already been founded within the cities of Delhi, Mumbai, Vadodara, Firozabad, Kanpur and plenty of more such networks are planned within the near future. Given the infrastructure and layout available in typical Indian cities, it becomes difficult to make such gas distribution networks without separate corridors for competing utilities. Reckoning on pressures, flow rates and economic criteria, these networks may be constructed with steel pipes, polyethylene (PE) pipes or a hybrid PE-steel pipe system. In contrast to borehole pipelines, which stretch for miles directly through open fields, the CGD network is more complex. These are located in densely populated areas, and an oversized number of network branches meet the wants of users in several locations in an exceedingly city. Although they're much smaller long and size than background pipelines, a city's network is far more dispersed and diverse. The rise within the number of branches means over the amount of sleeves, bends, reducers, fittings, etc. within the network, with the exception of the quantity of delivery points for the availability of fossil fuel. Due to the assorted activities of third parties other city agencies, the chance of injury and accidents is even on top of the substantial pipelines. of these factors require better security systems integrated into the network and therefore the need for special preparation to manage any emergency situation.

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Javarone, Marco Alberto. "Fermionic networks." International Journal of Modern Physics C 27, no. 02 (December 23, 2015): 1650021. http://dx.doi.org/10.1142/s0129183116500212.

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We study the structure of fermionic networks, i.e. a model of networks based on the behavior of fermionic gases, and we analyze dynamical processes over them. In this model, particle dynamics have been mapped to the domain of networks, hence a parameter representing the temperature controls the evolution of the system. In doing so, it is possible to generate adaptive networks, i.e. networks whose structure varies over time. As shown in previous works, networks generated by quantum statistics can undergo critical phenomena as phase transitions and, moreover, they can be considered as thermodynamic systems. In this study, we analyze fermionic networks and opinion dynamics processes over them, framing this network model as a computational model useful to represent complex and adaptive systems. Results highlight that a strong relation holds between the gas temperature and the structure of the achieved networks. Notably, both the degree distribution and the assortativity vary as the temperature varies, hence we can state that fermionic networks behave as adaptive networks. On the other hand, it is worth to highlight that we did not finding relation between outcomes of opinion dynamics processes and the gas temperature. Therefore, although the latter plays a fundamental role in gas dynamics, on the network domain, its importance is related only to structural properties of fermionic networks.

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Kojima, Tatsuhiro, Wanuk Choi, and Masaki Kawano. "Gas Phase Single Crystal Growth of Coordination Networks." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1009. http://dx.doi.org/10.1107/s2053273314089906.

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Organic ligands and metal ions can produce several kinds of networks depending on experimental conditions, such as solvent, temperature, reaction speed, and so on.1, 2 While many MOF chemists have used solution phase reaction, recently some unique networking methods have been investigated, e.g. mechanochemical solid state reactions. Here we report a new method for single crystal growth of porous coordination networks via gas phase. In our previous work, we found that heating of interpenetrated network [(ZnI2)3(TPT)2]n (solvent) forms a crystalline powder, [(ZnI2)3(TPT)2]n (1, TPT = 2,4,6-tris(4-pyridyl)triazine).3 We determined a porous saddle-type structure of 1 by ab initio PXRD analysis. Interestingly, we could not prepare 1 by grinding and heating the starting powder materials of ZnI2 and TPT. Therefore, we attempted to prepare coordination networks via gas phase. On heating of ZnI2 and TPT together under reduced pressure in a glass ample at high temperature, single crystal growth of 1 was observed. The single crystal X-ray structure analysis revealed that 1 has the same structure as microcrystalline powder of 1. In gas phase, because there is no solvation effect, network topology is purely based on ligand interactivity and geometry of metal coordination. Therefore, saddle-type network is one of the possible patterns on the basis of geometry of only TPT and ZnI2 without guest molecules. To the best of our knowledge, this is the first example of single crystal growth of porous coordination network via gas phase. In summary, we successfully demonstrated the first gas phase single crystal growth of porous coordination network formation. In this presentation, we will discuss network design by gas phase reaction based on ligand interactivity focusing on weak intermolecular interaction.

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Hoppmann-Baum, Kai, Felix Hennings, Ralf Lenz, Uwe Gotzes, Nina Heinecke, Klaus Spreckelsen, and Thorsten Koch. "Optimal Operation of Transient Gas Transport Networks." Optimization and Engineering 22, no. 2 (February 16, 2021): 735–81. http://dx.doi.org/10.1007/s11081-020-09584-x.

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AbstractIn this paper, we describe an algorithmic framework for the optimal operation of transient gas transport networks consisting of a hierarchical MILP formulation together with a sequential linear programming inspired post-processing routine. Its implementation is part of the KOMPASS decision support system, which is currently used in an industrial setting. Real-world gas transport networks are controlled by operating complex pipeline intersection areas, which comprise multiple compressor units, regulators, and valves. In the following, we introduce the concept of network stations to model them. Thereby, we represent the technical capabilities of a station by hand-tailored artificial arcs and add them to network. Furthermore, we choose from a predefined set of flow directions for each network station and time step, which determines where the gas enters and leaves the station. Additionally, we have to select a supported simple state, which consists of two subsets of artificial arcs: Arcs that must and arcs that cannot be used. The goal is to determine a stable control of the network satisfying all supplies and demands. The pipeline intersections, that are represented by the network stations, were initially built centuries ago. Subsequently, due to updates, changes, and extensions, they evolved into highly complex and involved topologies. To extract their basic properties and to model them using computer-readable and optimizable descriptions took several years of effort. To support the dispatchers in controlling the network, we need to compute a continuously updated list of recommended measures. Our motivation for the model presented here is to make fast decisions on important transient global control parameters, i.e., how to route the flow and where to compress the gas. Detailed continuous and discrete technical control measures realizing them, which take all hardware details into account, are determined in a subsequent step. In this paper, we present computational results from the KOMPASS project using detailed real-world data.

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Gugat, Martin, Falk M. Hante, Markus Hirsch-Dick, and Günter Leugering. "Stationary states in gas networks." Networks and Heterogeneous Media 10, no. 2 (April 2015): 295–320. http://dx.doi.org/10.3934/nhm.2015.10.295.

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K. Banda, Mapundi, Michael Herty, and Axel Klar. "Gas flow in pipeline networks." Networks & Heterogeneous Media 1, no. 1 (2006): 41–56. http://dx.doi.org/10.3934/nhm.2006.1.41.

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Ramchandani, N. L., and J. O. Gray. "Economic Optimisation of Gas Networks." IFAC Proceedings Volumes 26, no. 2 (July 1993): 399–405. http://dx.doi.org/10.1016/s1474-6670(17)48498-9.

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Edukov, D. A., and V. A. Edukov. "Gas Consumption Networks Development Problems." IOP Conference Series: Materials Science and Engineering 698 (December 18, 2019): 055028. http://dx.doi.org/10.1088/1757-899x/698/5/055028.

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Zhila, Viktor, and Aleksey Klochko. "Method of determining the optimal version of the piezometer of gas distribution networks of medium and high pressure." E3S Web of Conferences 263 (2021): 04027. http://dx.doi.org/10.1051/e3sconf/202126304027.

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The article deals with the issues of determining the optimal type of piezometric pressure graph in high-and medium-pressure gas pipelines in the design of gas distribution networks. Along with the most commonly used ways to reduce the cost of building gas distribution networks by finding the optimal configuration of gas pipelines, which give a great economic and environmental effect, it is necessary to rationally use the pressure drop provided to the network. This step in engineering practice allows you to additionally get money savings. The use of various schemes for supplying consumers with gas in cities and villages also has a great economic effect, but modern equipment allows you to make the transition to a single-stage scheme of gas distribution networks for cities. When designing gas distribution networks, the available pressure drop can be used by implementing various technical solutions, but the correct choice of pressures in the nodal points contributes to an economic effect. The designed configuration of the gas distribution network with the adopted type of piezometric graph can ensure reliable operation for a long time. The results obtained are recommended for use in the design of gas distribution networks of cities, other settlements and distribution networks of industrial enterprises

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HU, LI-DAN, NING-DE JIN, and ZHONG-KE GAO. "CHARACTERIZATION OF HORIZONTAL GAS–LIQUID TWO-PHASE FLOW USING MARKOV MODEL-BASED COMPLEX NETWORK." International Journal of Modern Physics C 24, no. 05 (May 2013): 1350028. http://dx.doi.org/10.1142/s0129183113500289.

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Horizontal gas–liquid two-phase flow widely exists in many physical systems and chemical engineering processes. Compared with vertical upward gas–liquid two-phase flow, investigations on dynamic behavior underlying horizontal gas–liquid flows are quite limited. Complex network provides a powerful framework for time series analysis of complex dynamical systems. We use a network generation method based on Markov transition probability to infer directed weighted complex networks from signals measured from horizontal gas–liquid two-phase flow experiment and find that the networks corresponding to different flow patterns exhibit different network structure. To investigate the dynamic characteristics of horizontal gas–liquid flows, we construct a number of complex networks under different flow conditions, and explore the network indices for each constructed network. In addition, we investigate the sample entropy of different flow patterns. Our results suggest that the network statistic can well represent the complexity in the transition among different flow patterns and further allows characterizing the interface fluctuation behavior in horizontal gas–liquid two-phase flow.

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Zhila, Viktor, and Aleksey Klochko. "The economic effect of the implementation of the differential pressure in the gas distribution." E3S Web of Conferences 263 (2021): 04028. http://dx.doi.org/10.1051/e3sconf/202126304028.

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This paper demonstrates the problem of calculating a rational piezometric pressure distribution graph in high-and medium-pressure gas distribution networks. These issues are particularly relevant in the design of gas distribution networks. In addition to the often used in practice design opportunities to reduce the cost of building gas distribution networks, rational choice of the optimal configuration of gas pipelines, which gives us the opportunity to get a large economic, as well as the effect of preserving the environment, it is also possible to use the pressure drop in the network. In total, with a rational choice of the scheme of organizing gas supply to consumers in localities, it is possible to get the maximum economic effect. It is important that modern equipment of this type allows to implement a single-stage scheme of gas distribution networks. Construction of natural gas pipelines we can use the available pressure drop in various technical solutions, but the reasonable distribution of the pressure drop between the network sections allows us to obtain a positive economic effect. The algorithm shown is proposed for use in the design of gas distribution networks of settlements of all types, and in the supply of natural gas to industrial enterprises.

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Fentaye, Amare D., Aklilu T. Baheta, and Syed Ihtsham Ul-Haq Gilani. "Gas turbine gas-path fault identification using nested artificial neural networks." Aircraft Engineering and Aerospace Technology 90, no. 6 (September 3, 2018): 992–99. http://dx.doi.org/10.1108/aeat-01-2018-0013.

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Purpose The purpose of this paper is to present a quantitative fault diagnostic technique for a two-shaft gas turbine engine applications. Design/methodology/approach Nested artificial neural networks (NANNs) were used to estimate the progressive deterioration of single and multiple gas-path components in terms of mass flow rate and isentropic efficiency indices. The data required to train and test this method are attained from a thermodynamic model of the engine under steady-state conditions. To evaluate the tolerance of the method against measurement uncertainties, Gaussian noise values were considered. Findings The test results revealed that this proposed method is capable of quantifying single, double and triple component faults with a sufficiently high degree of accuracy. Moreover, the authors confirmed that NANNs have derivable advantages over the single structure-based methods available in the public domain, particularly over those designed to perform single and multiple faults together. Practical implications This method can be used to assess engine’s health status to schedule its maintenance. Originality/value For complicated gas turbine diagnostic problems, the conventional single artificial neural network (ANN) structure-based fault diagnostic technique may not be enough to get robust and accurate results. The diagnostic task can rather be better done if it is divided and shared with multiple neural network structures. The authors thus used seven decentralized ANN structures to assess seven different component fault scenarios, which enhances the fault identification accuracy significantly.

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Moetamedzadeh, Hamid Reza, Esmaeel Khanmirza, Ali Pourfard, and Reza Madoliat. "Intelligent nonlinear model predictive control of gas pipeline networks." Transactions of the Institute of Measurement and Control 41, no. 16 (August 7, 2019): 4569–89. http://dx.doi.org/10.1177/0142331219864190.

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In gas pipeline networks, the set-points should be carefully tuned to minimize the fuel consumption of compressor stations and meet the network requirements. In practice, the real demand has some variations over the forecasted one and consequently utilizing an appropriate controller to minimize the fuel consumption and manage the network variations is inevitable. The model predictive control is a great choice for systems with long delay such as gas networks. In this paper, an intelligent nonlinear model predictive control of a gas pipeline plant is proposed. It models the plant in fully transient state by a multi-layer perceptron neural network. The prediction power of the neural network is used to predict the plant output over a receding horizon. Initially, the network is trained offline and is then paralyzed with the real plant for online training. The proposed strategy consists of two main stages. In the first stage, the compressor set-points are optimized in the open loop condition considering the forecasted demand over a receding horizon and the resulting output pressures are chosen as the reference trajectories for the closed loop system. In the second stage, the controller is applied to compensate the demand variations. The optimization task is carried out using particle swarm optimization gravitation search algorithm (PSOGSA). Numerical results confirm the accuracy and robustness of the proposed controller in the presence of demand variations, noise and uncertainties.

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Vorobev, Sergey, Anton Kolosnitsyn, and Ilya Minarchenko. "The clique approach to identifying critical elements in gas transmission networks." E3S Web of Conferences 289 (2021): 03009. http://dx.doi.org/10.1051/e3sconf/202128903009.

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We consider the gas transmission network operating on the territory of the Russian Federation. This network includes gas fields, gas consumers, nodal compressor stations, underground gas storages, which, depending on the given scenario of the system operation, can act as gas sources or gas consumers. The nodes are connected by means of gas pipelines. Because natural gas is used in heat and power engineering and electricity, the gas transmission network may be exposed to terrorist threats, and the actions of intruders may be directed both at gas production facilities and gas pipelines. To simulate intruders attacks, a model of the attacker-defender type was proposed. In this model, the defender, represented by the system operator, solves the problem of finding the maximum flow to meet the needs of gas consumers. The attacker, in turn, attempts to minimize the maximum flow in the gas transmission network by excluding either nodes or gas pipelines. Gas transmission networks in Russia and Europe are very extensive, ramified, and have many bridges and reserve gas pipelines. Therefore, to inflict maximum damage to the system, attacks on cliques, that is, on several interconnected objects, are modelled. The article presents the results of test calculations, in which we identify the most significant combinations of objects in the gas transmission network in terms of the potential threat from terrorist attacks.

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Mccabe, Kevin A., Stephen J. Rassenti, Stanley S. Reynolds, and Vernon L. Smith. "Economics: Markets, competition, and efficiency in natural gas pipeline networks." Natural Gas 6, no. 3 (April 16, 2008): 23–26. http://dx.doi.org/10.1002/gas.3410060310.

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Berger, Diego, and Carol Braester. "Gas-water displacement through fracture networks." Water Resources Research 36, no. 11 (November 2000): 3205–10. http://dx.doi.org/10.1029/2000wr900056.

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Thunvik, Roger. ""Gas migration in discrete fracture networks." Water Resources Research 26, no. 10 (1990): 2425–34. http://dx.doi.org/10.1029/90wr01222.

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Thunvik, Roger, and Carol Braester. "Gas migration in discrete fracture networks." Water Resources Research 26, no. 10 (October 1990): 2425–34. http://dx.doi.org/10.1029/wr026i010p02425.

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Vlachos, Dimitrios, and John Avaritsiotis. "Fuzzy neural networks for gas sensing." Sensors and Actuators B: Chemical 33, no. 1-3 (July 1996): 77–82. http://dx.doi.org/10.1016/0925-4005(96)01917-x.

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Mallinson, J., A. E. Fincham, S. P. Bull, J. S. Rollett, and Man Lam Wong. "Methods for optimizing gas transmission networks." Annals of Operations Research 43, no. 8 (August 1993): 443–54. http://dx.doi.org/10.1007/bf02024841.

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Walasek, Janusz. "Gas-Like Theory of Polymer Networks." Macromolecular Theory and Simulations 21, no. 7 (June 20, 2012): 482–91. http://dx.doi.org/10.1002/mats.201200002.

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Han, Lu, Chongchong Yu, Kaitai Xiao, and Xia Zhao. "A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification." Sensors 19, no. 9 (April 26, 2019): 1960. http://dx.doi.org/10.3390/s19091960.

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This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is proposed. Then, five kinds of convolutional neural networks—VGG-16, VGG-19, ResNet18, ResNet34 and ResNet50—were used to classify and compare five kinds of mixed gases. By adjusting the parameters of the convolutional neural networks, the final gas recognition rate is 96.67%. The experimental results show that the method can classify the gas data quickly and effectively, and effectively combine the gas time series data with classical convolutional neural networks, which provides a new idea for the identification of mixed gases.

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Weimann, A., D. Scheibe, P. Schröder, and K. Unterseer. "On-Line Simulation of Gas Networks with Changing Gas Qualities." IFAC Proceedings Volumes 22, no. 6 (July 1989): 471–75. http://dx.doi.org/10.1016/s1474-6670(17)54421-3.

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Cheng, Hui. "Dynamic Genetic Algorithms with Hyper-Mutation Schemes for Dynamic Shortest Path Routing Problem in Mobile Ad Hoc Networks." International Journal of Adaptive, Resilient and Autonomic Systems 3, no. 1 (January 2012): 87–98. http://dx.doi.org/10.4018/jaras.2012010105.

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In recent years, the static shortest path (SP) routing problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks (ANNs), genetic algorithms (GAs), particle swarm optimization (PSO), etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless mesh network (WMN), etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. This paper proposes to use two types of hyper-mutation GAs to solve the dynamic SP routing problem in MANETs. The authors consider MANETs as target systems because they represent new generation wireless networks. The experimental results show that the two hyper-mutation GAs can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.

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Fasihizadeh, Maryam, Mohsen V. Sefti, and Hassan M. Torbati. "Improving gas transmission networks operation using simulation algorithms: Case study of the National Iranian Gas Network." Journal of Natural Gas Science and Engineering 20 (September 2014): 319–27. http://dx.doi.org/10.1016/j.jngse.2014.07.018.

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Gugat, Martin, and David Wintergerst. "Transient Flow in Gas Networks: Traveling waves." International Journal of Applied Mathematics and Computer Science 28, no. 2 (June 1, 2018): 341–48. http://dx.doi.org/10.2478/amcs-2018-0025.

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Abstract In the context of gas transportation, analytical solutions are helpful for the understanding of the underlying dynamics governed by a system of partial differential equations. We derive traveling wave solutions for the one-dimensional isothermal Euler equations, where an affine linear compressibility factor is used to describe the correlation between density and pressure. We show that, for this compressibility factor model, traveling wave solutions blow up in finite time. We then extend our analysis to networks under appropriate coupling conditions and derive compatibility conditions for the network nodes such that the traveling waves can travel through the nodes. Our result allows us to obtain an explicit solution for a certain optimal boundary control problem for the pipeline flow.

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Zeng, Jun, Chaoxu Sun, Zhenjun Zhu, Jiangling Wu, and Hongsheng Chen. "Uncertainty Analysis for Natural Gas Transport Pipeline Network Layout: A New Methodology Based on Monte Carlo Method." Journal of Advanced Transportation 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/9213648.

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Natural gas plays an increasing important role in the China’s energy revolution. The rapid market development and refined government regulation demand improvements in the natural gas transport pipeline network. Therefore, it is of great theoretical and practical significance to conduct a study regarding the layout of pipeline networks. To reflect the comprehensive benefits of pipeline projects and obtain global optimal solution, this study introduces the dominance degree model (DDM). Aiming at optimizing the layout of natural gas transport pipeline networks, this paper studies the uncertainty of the DDM and the corresponding method for network layout. This study proposes an uncertainty analysis based on the Monte Carlo method to quantify the uncertainty of the DDM and its influential factors. Finally, the methodology is applied to the real case of a natural gas transport pipeline project in Zhejiang Province, China. The calculation results suggest that the methodology appropriately addresses the problem of pipeline network layout for natural gas transport. This has important implications for other potential pipeline networks not only in the Zhejiang Province but also throughout China and beyond.

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Antenucci, Andrea, and Giovanni Sansavini. "Adequacy and security analysis of interdependent electric and gas networks." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 2 (October 30, 2017): 121–39. http://dx.doi.org/10.1177/1748006x17715953.

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In this article, adequacy and security assessments on the coupled operations of the electric and gas networks are performed. Extreme operating conditions and fault of components are considered as events that can impact the interdependent systems. The electric and gas networks are represented by an event-based direct current power flow model and by a transient one-dimensional mass flow model, respectively. Furthermore, the automations and safety strategies enforced by transmission system operators are represented within an original modelling approach. A quantitative analysis is performed with reference to the simplified energy infrastructures of Great Britain. Results highlight the contingencies which can jeopardize security and identify the components that are prone to fail and induce large gas pressure instabilities and loss of supply, and the locations in the gas grid that are susceptible to pressure violation. Moreover, a simulated 30% increase of the peak gas demand in 2015 is a limit for safe operations of the gas network, but the coupled systems are robust enough to avoid the spread of a cascading failure across networks. These results allow preventing critical operating conditions induced by the interaction between networks and can guide safety-based decisions on system reinforcements and the development of mitigating actions.

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Klochko, Aleksey, and Asmik Klochko. "Influence of the iteration step size to finding solutions." MATEC Web of Conferences 251 (2018): 03031. http://dx.doi.org/10.1051/matecconf/201825103031.

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The article considers the issues of obtaining a network configuration by the criterion of maximizing the reliability index. The rationally designed configuration of the gas distribution network for the selected gas supply scheme ensures reliable operation throughout the life of gas pipeline. The results are recommended in designing of gas distribution networks, as well as when determining the reserve for improving the reliability of the network for the adopted gas supply scheme for subscribers.

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Estakhr, Javad, Mohsen Simab, and Taher Niknam. "Security Analysis of Hybrid Multi-Carrier Energy Systems." Sustainability 13, no. 6 (March 12, 2021): 3102. http://dx.doi.org/10.3390/su13063102.

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Multi-carrier energy systems (MCESs) provide collaboration between various kinds of energy carriers to supply the electricity, heating, and cooling demands. With the widespread use of MCESs in recent years, the security assessment of energy systems has attracted the attention of many contemporary researchers. However, the complexity of an MCES, including electrical, natural gas, and district heating networks, and different uncertainties imposes vast challenges to keep a safe operation energy supply. In this paper, a systematic methodology for the security analysis of MCESs is presented. For this purpose, considering electrical, natural gas, and district heating networks, an integrated model of energy systems is introduced. The security analysis of this framework is evaluated using some indices. In this approach, two well-known performance indices, including power performance index (PIP) and voltage performance index (PIV), are used to analyze the electrical networks’ security. Besides, the concept of Energy not supplied (ENS) is used for natural gas and district heating networks. In this regard, security analysis of a typical MCES including the IEEE 14-bus electrical network, the IEEE 30-bus electrical network, 20-node Belgian natural gas network, and 14-node district heating network is examined. The applicability of the proposed technique will be proven using comprehensive simulation analysis.

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Klochko, Aleksey, and Viktor Zhila. "Determination of high-pressure pipeline cyclization degree with exploratory technique." MATEC Web of Conferences 251 (2018): 03030. http://dx.doi.org/10.1051/matecconf/201825103030.

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The article examines the issues of determining the network configuration, which ensures the maximum reliability of the gas distribution system. Rationally designed configuration of the gas distribution network with the adopted gas supply scheme can provide reliable operation for a long time. Results are recommended to be used in the design of gas distribution networks, as well as in determining the reserve for improving the reliability of the network for the adopted gas supply scheme for users.

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Pylypiv, L. D., and І. І. Maslanych. "Research of pressure influence in the gas supply system on the energy consuption level of gas devices." JOURNAL OF HYDROCARBON POWER ENGINEERING 6, no. 2 (December 27, 2019): 56–63. http://dx.doi.org/10.31471/2311-1399-2019-2(12)-56-63.

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There are investigated the influence of operating pressures in the gas supply system on the level of such energy indicators as efficiency, gas flow and gas overrun by gas equipment in residential buildings. There is established a relationship between the values of operating pressures in the gas supply system and the gas consumption level of household appliances. The causes of insufficient pressure in the gas networks of settlements are analyzed in the article. There is also developed an algorithm for calculating the change in the efficiency of gas appliances depending on the operational parameters of the gas network. It has been found that the most efficient operation of gas appliances is observed at an overpressure at the inlet of gas appliances of about 1200 Pa.To ensure the required quality of natural gas combustion among consumers and minimize gas consumption there are justified the following measures in the article: coordinating a domestic regulatory framework for assessing the quality of natural gas with international norms and standards; improving the preparation of gas coming from local wells before supplying it to gas distribution networks; auditing low pressure gas pipelines and reconstructing areas affected by corrosion; ensuring standard gas pressure in the network for the normal operation of domestic gas appliances; stating quality indicators of natural gas combustion by gas sales organizations.

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de Wolf, Daniel, and Yves Smeers. "Optimal Dimensioning of Pipe Networks with Application to Gas Transmission Networks." Operations Research 44, no. 4 (August 1996): 596–608. http://dx.doi.org/10.1287/opre.44.4.596.

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Zhang, Shuang Yan, Jun Yu, Guang Fen Wei, Zhen An Tang, Yi Chen, and Yuan Hui Cui. "Gas Mixture Recognition Method with New Hybrid Architecture." Applied Mechanics and Materials 40-41 (November 2010): 604–9. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.604.

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The quantification accuracy of the gas mixture recognizing is greatly dependent on the gas sensor array signal processing method. The paper reports the new hybrid architecture with two main stages for gas mixture recognition. The first stage combine the principal component analysis (PCA) and back propagation neural network (BPNN) to qualitative identify the gas mixture, and the second stage composed of the independent component analysis (ICA) and BP sub networks to quantify the gas concentrations. The hybrid architecture and three other commonly used methods of PCA+BPNN, ICA+BPNN, and ICA+BP sub networks were respectively applied in binary gas mixture quantification based on the same gas sensor array, and results show that the hybrid architecture has the lowest quantitative recognition errors and fast converge speed comparing with the other methods.

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Grudz, Ya V., R. B. Stasyuk, and M. Ya Krill. "Research on energy efficiency of operation of city gas networks." Oil and Gas Power Engineering, no. 2(32) (December 27, 2019): 29–34. http://dx.doi.org/10.31471/1993-9868-2019-2(32)-29-34.

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The task of choosing the criterion of energy efficiency of operation of city gas networks of high and medium pressure, under the condition of transition to the system of individual energy supply is set and solved. It is shown that in conditions of substantial increase of prices for natural gas and deepening of its deficit, the city gas network is often not able to provide consumers with energy in a given volume, so the approach to forecasting its operating modes should fundamentally change. First of all it concerns the field of permissible modes, which in such conditions considerably expands. Given the reloading of the gas distribution network as a result of changing the nature of gas consumption, a large role is given to the criteria for energy efficiency of gas transportation and to ensure maximum operational reliability while being within the scope of optimal maintenance. In order to assess the energy efficiency of gas transportation by gas pipelines of a distribution network, it is suggested to use the notion of the net efficiency coefficient, which is determined by the ratio of useful work performed when providing a given volume of gas transportation by pipelines to total energy consumption. It is shown that the efficiency of the distribution network or its individual elements differs fundamentally from the coefficient of its hydraulic efficiency, which is a characteristic of the technical state of the system. On the basis of analytical studies, the correlation between them is established. Calculated dependencies are proposed for determining the coefficient of hydraulic efficiency of a branched gas pipeline as an element of an urban gas distribution network. The influence of various factors on the value of the coefficient of hydraulic efficiency of the urban gas distribution network has been analyzed. The examples of calculating the efficiency of the network are given, conclusions on improving the efficiency of operation are made.

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Lucas, D. D., C. Yver Kwok, P. Cameron-Smith, H. Graven, D. Bergmann, T. P. Guilderson, R. Weiss, and R. Keeling. "Designing optimal greenhouse gas observing networks that consider performance and cost." Geoscientific Instrumentation, Methods and Data Systems 4, no. 1 (June 16, 2015): 121–37. http://dx.doi.org/10.5194/gi-4-121-2015.

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Abstract. Emission rates of greenhouse gases (GHGs) entering into the atmosphere can be inferred using mathematical inverse approaches that combine observations from a network of stations with forward atmospheric transport models. Some locations for collecting observations are better than others for constraining GHG emissions through the inversion, but the best locations for the inversion may be inaccessible or limited by economic and other non-scientific factors. We present a method to design an optimal GHG observing network in the presence of multiple objectives that may be in conflict with each other. As a demonstration, we use our method to design a prototype network of six stations to monitor summertime emissions in California of the potent GHG 1,1,1,2-tetrafluoroethane (CH2FCF3, HFC-134a). We use a multiobjective genetic algorithm to evolve network configurations that seek to jointly maximize the scientific accuracy of the inferred HFC-134a emissions and minimize the associated costs of making the measurements. The genetic algorithm effectively determines a set of "optimal" observing networks for HFC-134a that satisfy both objectives (i.e., the Pareto frontier). The Pareto frontier is convex, and clearly shows the tradeoffs between performance and cost, and the diminishing returns in trading one for the other. Without difficulty, our method can be extended to design optimal networks to monitor two or more GHGs with different emissions patterns, or to incorporate other objectives and constraints that are important in the practical design of atmospheric monitoring networks.

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Lucas, D. D., C. Yver Kwok, P. Cameron-Smith, H. Graven, D. Bergmann, T. P. Guilderson, R. Weiss, and R. Keeling. "Designing optimal greenhouse gas observing networks that consider performance and cost." Geoscientific Instrumentation, Methods and Data Systems Discussions 4, no. 2 (December 23, 2014): 705–49. http://dx.doi.org/10.5194/gid-4-705-2014.

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Abstract. Emission rates of greenhouse gases (GHGs) entering into the atmosphere can be inferred using mathematical inverse approaches that combine observations from a network of stations with forward atmospheric transport models. Some locations for collecting observations are better than others for constraining GHG emissions through the inversion, but the best locations for the inversion may be inaccessible or limited by economic and other non-scientific factors. We present a method to design an optimal GHG observing network in the presence of multiple objectives that may be in conflict with each other. As a demonstration, we use our method to design a prototype network of six stations to monitor summertime emissions in California of the potent GHG 1,1,1,2-tetrafluoroethane (CH2FCF3, HFC-134a). We use a multiobjective genetic algorithm to evolve network configurations that seek to jointly maximize the scientific accuracy of the inferred HFC-134a emissions and minimize the associated costs of making the measurements. The genetic algorithm effectively determines a set of "optimal" observing networks for HFC-134a that satisfy both objectives (i.e., the Pareto frontier). The Pareto frontier is convex, and clearly shows the tradeoffs between performance and cost, and the diminishing returns in trading one for the other. Without difficulty, our method can be extended to design optimal networks to monitor two or more GHGs with different emissions patterns, or to incorporate other objectives and constraints that are important in the practical design of atmospheric monitoring networks.

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Friedl, Herwig, Radoslava Mirkov, and Ansgar Steinkamp. "Modelling and Forecasting Gas Flow on Exits of Gas Transmission Networks." International Statistical Review 80, no. 1 (April 2012): 24–39. http://dx.doi.org/10.1111/j.1751-5823.2011.00171.x.

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Askari, S., N. Montazerin, and M. H. Fazel Zarandi. "Gas networks simulation from disaggregation of low frequency nodal gas consumption." Energy 112 (October 2016): 1286–98. http://dx.doi.org/10.1016/j.energy.2016.06.122.

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Merkel, Gregory, Richard Povinelli, and Ronald Brown. "Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †." Energies 11, no. 8 (August 2, 2018): 2008. http://dx.doi.org/10.3390/en11082008.

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Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE).

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Xiang, Yue, Peng Wang, Bo Yu, and Dongliang Sun. "GPU-accelerated hydraulic simulations of large-scale natural gas pipeline networks based on a two-level parallel process." Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles 75 (2020): 86. http://dx.doi.org/10.2516/ogst/2020076.

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The numerical simulation efficiency of large-scale natural gas pipeline network is usually unsatisfactory. In this paper, Graphics Processing Unit (GPU)-accelerated hydraulic simulations for large-scale natural gas pipeline networks are presented. First, based on the Decoupled Implicit Method for Efficient Network Simulation (DIMENS) method, presented in our previous study, a novel two-level parallel simulation process and the corresponding parallel numerical method for hydraulic simulations of natural gas pipeline networks are proposed. Then, the implementation of the two-level parallel simulation in GPU is introduced in detail. Finally, some numerical experiments are provided to test the performance of the proposed method. The results show that the proposed method has notable speedup. For five large-scale pipe networks, compared with the well-known commercial simulation software SPS, the speedup ratio of the proposed method is up to 57.57 with comparable calculation accuracy. It is more inspiring that the proposed method has strong adaptability to the large pipeline networks, the larger the pipeline network is, the larger speedup ratio of the proposed method is. The speedup ratio of the GPU method approximately linearly depends on the total discrete points of the network.

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Osiadacz, Andrzej J., and Niccolo Isoli. "Multi-Objective Optimization of Gas Pipeline Networks." Energies 13, no. 19 (October 2, 2020): 5141. http://dx.doi.org/10.3390/en13195141.

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The main goal of this paper is to prove that bi-objective optimization of high-pressure gas networks ensures grater system efficiency than scalar optimization. The proposed algorithm searches for a trade-off between minimization of the running costs of compressors and maximization of gas networks capacity (security of gas supply to customers). The bi-criteria algorithm was developed using a gradient projection method to solve the nonlinear constrained optimization problem, and a hierarchical vector optimization method. To prove the correctness of the algorithm, three existing networks have been solved. A comparison between the scalar optimization and bi-criteria optimization results confirmed the advantages of the bi-criteria optimization approach.

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Zhou, Fang, Yanchao Du, Yongbo Yuan, and Mingyuan Zhang. "The cross-networks impact analysis and assessment in multilayer interdependent networks: A case study of critical infrastructures." International Journal of Modern Physics C 30, no. 07 (July 2019): 1940007. http://dx.doi.org/10.1142/s0129183119400072.

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Critical infrastructures are tightly connected and extremely fragile multilayer coupled networks. This paper discusses the cross-networks impact of subnetworks and global network of networks on robustness by taking a critical infrastructures with three-layer interdependent networks as an example. The percolation theory is applied to capture the flow characteristics of cascading failures and evaluate the robustness of multilayer networks. And further discuss and compare the situation of each subnetwork affecting or being affected. The quantitative evaluation model of the interaction of multilayer networks is proposed based on cascading failures, where the influence expansion matrix and the dependency matrix are obtained. The results show that the power network has a high influence on other networks, and it is difficult to be affected. Meanwhile the influence ability of water network and gas network is limited.

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MIYAJIMA, Hiromi, Michiharu MAEDA, Fumihisa SAKAGUCHI, and Kazuya KISHIDA. "Construction of Self-Organizing Neural Gas Networks." Journal of Japan Society for Fuzzy Theory and Systems 14, no. 1 (2002): 88–95. http://dx.doi.org/10.3156/jfuzzy.14.1_88.

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Peng, Pai, Xiaojin Zhao, Xiaofang Pan, and Wenbin Ye. "Gas Classification Using Deep Convolutional Neural Networks." Sensors 18, no. 2 (January 8, 2018): 157. http://dx.doi.org/10.3390/s18010157.

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Solovyeva, E. B. "Analysis of pressure changes in gas networks." Agrarian Scientific Journal, no. 3 (March 18, 2019): 94–97. http://dx.doi.org/10.28983/asj.y2019i3pp94-97.

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Herty, Michael, and Veronika Sachers. "Adjoint calculus for optimization of gas networks." Networks & Heterogeneous Media 2, no. 4 (2007): 733–50. http://dx.doi.org/10.3934/nhm.2007.2.733.

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Journal articles on the topic 'Gas networks'

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