Relevant bibliographies by topics / Gas networks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Gas networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "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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Dissertations / Theses on the topic "Gas networks"

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Olanrewaju, Oluwabamise. "Impact of the European gas network on the operation of Great Britain's gas and electricity networks." Thesis, Cardiff University, 2017. http://orca.cf.ac.uk/100254/.

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Recent events of natural gas supply disruption in Europe have led to severe consequences of supply shortages to some European Member States. As the United Kingdom increasingly depend on imported gas supply from different sources including Continental Europe, the effect of gas supply disruption in Europe on UK’s gas consumers is in question. This thesis investigated the effect of gas supply disruptions in Europe on the operation of the Great Britain’s gas and electricity network using a set of modelling tools. An optimisation model of the European gas network was developed to assess the resilience of the European gas network to the loss of gas supply through the Ukraine transit pipelines to Europe. The results showed that unserved gas demand occurred in South East Europe. It was shown that additional interconnector capacities of selected pipelines and higher storage withdrawal rate in South East Europe minimised unserved gas demand in South East Europe. A soft-link coupling of the European Gas Network model (EGN) and the Combined Gas and Electricity Network model (CGEN) was developed and used to examine the effect of a 90-day loss of Ukraine transit capacity in Europe on the operation of GB gas and electricity network at a period of limited LNG supply to Europe. The result showed that in a high gas demand situation, industrial customers would experience some amount of unserved gas demand. The effectiveness of the mitigation options to prevent or mitigate unserved gas in GB was analysed using the EGN-CGEN model. Then a cost-benefit assessment tool was used to rank the mitigation options according to the net benefit of reducing the cost of unserved gas demand in GB. It was shown that diversification of gas supply sources and routes in Europe would deliver significant security of supply benefit to GB gas and electricity network.
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Morgan, Christopher. "Gas sensing with carbon nanotube networks." Thesis, Queen Mary, University of London, 2009. http://qmro.qmul.ac.uk/xmlui/handle/123456789/480.

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Carbon nanotubes are an exciting new material with exemplary mechanical and electronic properties. Carbon nanotubes can be either metallic or semiconducting; either type has properties which rival conventional materials. The one-dimensional electronic nature of these materials leads to extreme sensitivity to the local energy landscape, a desirable property for a sensing element. Production of carbon nanotubes currently has no method of growing nanotubes with a speci c electronic property, any di erentiation occurs through processing a heterogenous ensemble. Recently, networks of carbon nanotubes have shown attractive properties for electronic applications. The self-selecting current path has properties averaged from the ensemble of nanotubes providing repeatability in addition to exibility and transparency. This thesis is a study of the transport properties of thin and thick networks of single-walled carbon nanotubes and their electrical response to oxygen adsorption in both a simple resistive geometry and as the gate layer in a nanotube-metal-oxide-silicon capacitor. The thickness of network was found to determine the electrical characteristics of the network ensemble, thin networks displaying semiconducting transport characteristics, thick networks becoming more metallic. The response of the nanotube networks to oxygen exposure was found to be dependent on UV treatment. UV-desorbed networks exhibited an increased conductance upon oxygen-exposure, adsorbed networks exhibited a decrease in conductance upon further oxygen-exposure. Thinner, more semiconducting nanotube networks exhibited a greater change in conductance upon oxygen exposure. The nanotube-metal-oxide-semiconductor capacitor also showed a greater change in at-band capacitance for thin nanotube networks. The capacitance of the nanotube device at the nanotube network at-band voltage is shown to be in uenced by both oxygen and nitrogen gases. The origin of the behaviour of the at-band voltage is attempted to be understood and future work is suggested. 4.
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André, Jean. "Optimization of investments in gas networks." Littoral, 2010. http://www.theses.fr/2010DUNK0286.

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Les réseaux de transport de gaz naturel nécessitent des investissements très importants pour faire face à une demande toujours croissante en énergie et pour satisfaire des contraintes réglementaires de plus en plus importantes. En effet, la libéralisation des marchés gaziers a imposé aux opérateurs de transport de gaz, d’une part, des règles de transparence d’un monopole naturel pour justifier leurs dépenses et, in fine, leurs tarifs, et , d’autre part, des objectifs de fluidification du marché afin de faciliter l’accès a la concurrence des clients finaux. Ces investissements majeurs justifient l’utilisation de techniques d’optimisation permettant de réduire leurs coûts. Vu la présence de choix discrets (choix de la localisation des investissements, choix limité de capacités supplémentaires, planification temporelle) en combinaison avec des contraintes physiques non linéaires (représentant la relation entre l’´ecoulement et les pressions dans les canalisations ou la plage de fonctionnement des compresseurs), les programmes à résoudre sont des programmes d’optimisation non linéaires en nombres entiers (PNLNE) de grandes tailles. Ce type de programmes étant connu pour être particulièrement difficile à résoudre en temps polynomial (NP-difficile), des méthodes avancées d’optimisation doivent être mises en oeuvre pour obtenir des réponses réalistes. Les objectifs de cette thèse sont au nombre de trois. Il s’agit d’abord de proposer une modélisation des problèmes d’investissement dans les réseaux de transport de gaz à partir des motivations du monde industriel. Il s’agit ensuite d’identifier les méthodes et algorithmes les plus adéquats pour résoudre les problèmes ainsi formulées. Il s’agit enfin d’évaluer les avantages et les inconvénients de ces méthodes à l’aide d’applications numériques sur des cas réels
The natural gas networks require very important investments to cope with a still growing demand and to satisfy the new regulatory constraints. The gas market deregulation imposed to the gas network operators, first, transparency rules of a natural monopoly to justify their costs and ultimately their tariffs, and, second, market fluidity objectives in order to facilitate access for competition to the end-users. These major investments are the main reasons for the use of optimization techniques aiming at reducing the costs. Due to the discrete choice (investment location, limited choice of additional capacities, timing) crossed with physical non linear constraints (flow/pressure relations in the pipe or operating ranges of compressors), the programs to solve are Large Mixed Non Linear Programs (MINLP). As these types of programs are known to be hard to solve exactly in polynomial times (NP-hard), advanced optimization methods have to be implemented to obtain realistic results. The objectives of this thesis are threefold. First, one states several investment problems modeling of natural gas networks from industrial world motivations. Second, one identifies the most suitable methods and algorithms to the formulated problems. Third, one exposes the main advantages and drawbacks of these methods with the help of numerical applications on real causes
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Chaudry, Modassar. "Interactions beween gas and electricity networks." Thesis, University of Manchester, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.529224.

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Choudhary, P. A. "State estimation applied to gas distribution networks." Thesis, University of Manchester, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378007.

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The need to make more efficient use of energy resources by improved methods of control, the deferment of expensive pipework reinforcement or replacement and the requirement to accommodate different loading patterns on sections of the gas network have made the requirement for effective feedback control essential. Gas distribution systems are large and complex and a major problem in applying conventional feedback techniques is the cost and reliability of transmitting the data necessary for automatic control. In order to overcome these difficulties, state estimation techniques have been investigated as a means of providing information about a distribution system from a minimum number of measurement points. The theoretical background to the study is reviewed including the modelling technique and the results of experimental work which has been performed on a distribution network in order to verify the techniques developed are presented. The results show that state estimation techniques have considerable potential for this type of application.
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Pearson, D. W. "Robust observer design and application to gas networks." Thesis, Coventry University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.380696.

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Wong, Man Lam. "The application of constrained optimization gas transmission networks." Thesis, University of Oxford, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235972.

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Parkinson, J. S. "Control system design for low pressure gas distribution networks." Thesis, University of Manchester, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378367.

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SILVA, MARILIA PAULA E. "ARTIFICIAL NEURAL NETWORKS APPLIED TO GAS TURBINE FAULT DIAGNOSTICS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16580@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
A deterioração do desempenho da turbina a gás é resultado de vários tipos de falhas, como acúmulo de sujeira, erosão e corrosão, que afetam os componentes no caminho do gás, sendo os principais o compressor, o combustor e a turbina. No presente trabalho é avaliado o desempenho de Redes Neurais Artificiais (RNA) no emprego de diagnóstico de falha de turbinas a gás. Todas as redes projetadas são do tipo MLP (multi-layer perceptron) com algoritmo de retropropagação (backpropagation). Para cada função de diagnóstico, várias arquiteturas foram testadas, modificando parâmetros de rede como o número de camadas escondidas e o número de neurônios em cada uma destas camadas. As RNAs para diagnóstico de falhas foram aplicadas ao modelo termodinâmico de uma turbina a gás industrial. Este modelo foi responsável pela criação de dados da usina saudável e também degradada, utilizados para o treinamento e validação das redes. Com os resultados obtidos do treinamento das redes é possível mostrar que as mesmas são capazes de detectar, isolar e quantificar falhas de componentes de turbinas a gás de forma satisfatória.
The gas turbine performance deterioration is a result of several types of faults such as fouling, erosion and corrosion, which affects the components throughout the gas path. As the most significant of these components we can enumerate the compressor, the combustion chamber and the turbine itself. In this work the performance of different types of Artificial Neural Networks (ANN) are evaluated in the diagnosis of this kind of fault. Every neural network designed in this work is MLP (multi-layer perceptron) with back propagation algorithm. For each diagnosis function several architectures were tested, varying network parameters as the numbers of hidden layers and the number of neurons in each layer. The ANNs for fault diagnosis were applied in an industrial gas turbine thermodynamic model. This model was also used for healthy and degraded turbine data generation, which were used for ANNs training and validation. With the ANNs training results we can conclude that these networks are capable of detecting, isolating and quantifying gas turbine components faults in a satisfactory way.
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Hosseini, Seyed. "State estimation of integrated power and gas distribution networks." Thesis, Cardiff University, 2017. http://orca.cf.ac.uk/109819/.

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Future energy networks are likely to be highly integrated with several energy conversion utilities operating between them, which make the control and management of the whole system more complicated. Therefore, analysis of operation and management of the whole system needs to be performed in an integrated approach. In order to perform an effective control and management of the whole system, an accurate and reliable estimation of the state parameters and the energy flows within the integrated network is essential. In this research simulation and state estimation of integrated power and gas distribution networks with decentralised injection and generation in both networks was investigated. For this purpose, state estimation of individual networks was first reviewed. Afterwards, state estimation of integrated power and gas distribution networks was studied. Firstly, an algorithm was developed for state estimation of power distribution networks, which was validated through a case study power distribution network. Afterwards, an algorithm for placement of additional measurements within power distribution networks for improvement of state estimation results was developed. The performance of the algorithm showed satisfactory results for placement of a given number of additional individual measurements and a given number of additional measurement units. Secondly, an algorithm was developed for simulation of operation of gas distribution networks with decentralised injection, which was validated with the results of the commercial software Synergi Gas. Then, an algorithm was developed for the WLS state estimation of gas distribution networks with decentralised injection, which was validated on a case study gas distribution network. Afterwards, an algorithm was developed for placement of additional measurements within gas distribution networks with decentralised injection for improvement of estimation of the gas mixtures within the network, which showed satisfactory results on a case study gas distribution network. Finally, an algorithm was developed for performing state estimation of power and gas distribution networks with decentralised injection and generation in both networks, which was validated on a case study power and gas distribution network. Afterwards, impact of deployment of smart meters on improvement of estimation of the state parameters of the other coupled network was investigated. It was observed that information from one of the energy networks has no significant impact on improvement of state estimation results of the other coupled network.
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Books on the topic "Gas networks"

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Qadrdan, Meysam, Muditha Abeysekera, Jianzhong Wu, Nick Jenkins, and Bethan Winter. The Future of Gas Networks. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-66784-3.

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Osiadacz, Andrzej J. Simulation and analysis of gas networks. London: Spon, 1987.

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O'Siadhail, Micheal. Simulation and analysis of gas networks. London: E. & F.N. Spon, 1987.

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Moreno, V. A. Leak location in gas pipeline networks. Manchester: UMIST, 1994.

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Johnson, Debra. Trans-European energy networks: Europe's gas and electricity into the 21st century. London: Financial Times Energy Publishing, 1996.

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Gao, Zhong-Ke, Ning-De Jin, and Wen-Xu Wang. Nonlinear Analysis of Gas-Water/Oil-Water Two-Phase Flow in Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-38373-1.

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Ghosh, Diptesh. On the blowout preventer testing problem: An approach to cheking for leakage in BOP networks. Ahmedabad: Indian Institute of Management, 2012.

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Koch, Thorsten. Evaluating gas network capacities. Philadelphia: Society for Industrial and Applied Mathematics, 2015.

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Koch, Thorsten, Benjamin Hiller, Marc E. Pfetsch, and Lars Schewe, eds. Evaluating Gas Network Capacities. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611973693.

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Pańko, Adam. Wykorzystanie możliwości sieci neuronowych w prognozowaniu i sterowaniu praca̜ podziemnego magazynu gazu (PMG). Kraków: INIG, 2008.

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Book chapters on the topic "Gas networks"

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Shimomura, Satoru, Sareeya Bureekaew, and Susumu Kitagawa. "Porous Coordination Polymers Towards Gas Technology." In Molecular Networks, 51–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01367-6_6.

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Shimomura, Satoru, Sareeya Bureekaew, and Susumu Kitagawa. "Porous Coordination Polymers Towards Gas Technology." In Molecular Networks, 96–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01367-6_8.

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Shimomura, Satoru, Sareeya Bureekaew, and Susumu Kitagawa. "Porous Coordination Polymers Towards Gas Technology." In Molecular Networks, 51–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/430_2008_6.

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Qadrdan, Meysam, Muditha Abeysekera, Jianzhong Wu, Nick Jenkins, and Bethan Winter. "Fundamentals of Natural Gas Networks." In The Future of Gas Networks, 5–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-66784-3_2.

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Qadrdan, Meysam, Muditha Abeysekera, Jianzhong Wu, Nick Jenkins, and Bethan Winter. "The Future of Gas Networks." In The Future of Gas Networks, 49–68. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-66784-3_5.

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SoltaniNejad, Alireza, Ramin Bahmani, and Heidarali Shayanfar. "Integrated Gas and Power Networks." In Electricity Markets, 37–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36979-8_3.

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Hammer, Barbara, Alexander Hasenfuss, Frank-Michael Schleif, and Thomas Villmann. "Supervised Batch Neural Gas." In Artificial Neural Networks in Pattern Recognition, 33–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11829898_4.

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Talebi, Amir, and Ahmad Sadeghi-Yazdankhah. "Optimal Scheduling of Electricity-Gas Networks Considering Gas Storage and Power-to-Gas Technology." In Planning and Operation of Multi-Carrier Energy Networks, 179–94. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60086-0_8.

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Holdsworth, A. K., and D. J. Hourston. "Gas Permeability of Latex Interpenetrating Polymer Network Films." In Interpenetrating Polymer Networks, 449–61. Washington, DC: American Chemical Society, 1994. http://dx.doi.org/10.1021/ba-1994-0239.ch022.

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Qadrdan, Meysam, Muditha Abeysekera, Jianzhong Wu, Nick Jenkins, and Bethan Winter. "Overview of the Transition to a Low Carbon Energy System." In The Future of Gas Networks, 1–4. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-66784-3_1.

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Conference papers on the topic "Gas networks"

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Luongo, C. A., W. C. Yeung, and B. J. Gilmour. "Optimizing the Operation of Gas Transmission Networks." In ASME 1991 International Computers in Engineering Conference and Exposition. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/cie1991-0083.

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Abstract This paper presents a generalized procedure for optimizing steady-state operation of natural gas networks based on different objectives: minimum fuel consumption, minimum fuel cost, maximum throughput, and maximum operating margin (profit). The generalized procedure is a hierarchical approach that combines linear and non-linear optimizers in order to achieve good performance without compromising accuracy in modeling physical network components (compressors, pipes, etc.). The non-linear optimizer is used to optimize the physical flows in the transmission networks. The non-linear optimizer calls the linear optimizer to find the optimum allocation of flows among the contracts for the best objective function. The solution technique used by the linear optimizer can handle thousands of contracts with and without two-tier priced structure. Separating the physical flow optimization and economic optimization makes the global optimization feasible for large problems usually encountered in the gas transmission industry. In that sense, the present optimization method bridges the gap between engineering modeling and economic dispatch optimization of natural gas networks.
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Mohammadi, Rasul, Esmaeil Naderi, Khashayar Khorasani, and Shahin Hashtrudi-Zad. "Fault Diagnosis of Gas Turbine Engines by Using Dynamic Neural Networks." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-23586.

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This paper presents a novel methodology for fault detection in gas turbine engines based on the concept of dynamic neural networks. The neural network structure belongs to the class of locally recurrent globally feed-forward networks. The architecture of the network is similar to the feed-forward multi-layer perceptron with the difference that the processing units include dynamic characteristics. The dynamics present in these networks make them a powerful tool useful for identification of nonlinear systems. The dynamic neural network architecture that is described in this paper is used for fault detection in a dual-spool turbo fan engine. A number of simulation studies are conducted to demonstrate and verify the advantages of our proposed neural network diagnosis methodology.
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Xiradakis, N., and Y. G. Li. "Gas Turbine and Sensor Fault Diagnosis With Nested Artificial Neural Networks." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53570.

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Accurate gas turbine diagnosis relies on accurate measurements from sensors. Unfortunately, sensors are prone to degradation or failure during gas turbine operations. In this paper a stack of decentralised artificial neural networks are introduced and investigated as an approach to approximate the measurement of a failed sensor once it is detected. Such a system is embedded into a nested neural network system for gas turbine diagnosis. The whole neural network diagnostic system consists of a number of feedforward neural networks for engine component diagnosis, sensor fault detection and isolation; and a stack of decentralised neural networks for sensor fault recovery. The application of the decentralised neural networks for the recovery of any failed sensor has the advantage that the configuration of the nested neural network system for engine component diagnosis is relatively simple as the system does not take into account sensor failure. When a sensor fails, the biased measurement of the failed sensor is replaced with a recovered measurement approximated with the measurements of other healthy sensors. The developed approach has been applied to an engine similar to the industrial 2-shaft engine, GE LM2500+, whose performance and training samples are simulated with an aero-thermodynamic modelling tool — Cranfield University’s TURBOMATCH computer program. Analysis shows that the use of the stack of decentralised neural networks for sensor fault recovery can effectively recover the measurement of a failed sensor. Comparison between the performance of the diagnostic system with and without the decentralised neural networks shows that the sensor recovery can improve the performance of the neural network engine diagnostic system significantly when a sensor fault is present.
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Loboda, Igor, Yakov Feldshteyn, and Volodymyr Ponomaryov. "Neural Networks for Gas Turbine Fault Identification: Multilayer Perceptron or Radial Basis Network?" In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-46752.

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Efficiency of gas turbine condition monitoring systems depends on quality of diagnostic analysis at all its stages such as feature extraction (from raw input data), fault detection, fault identification, and prognosis. Fault identification algorithms based on the gas path analysis may be considered as an important and sophisticated component of these systems. These algorithms widely use pattern recognition techniques, mostly different artificial neural networks. In order to choose the best technique, the present paper compares two network types: a multilayer perceptron and a radial basis network. The first network is being commonly applied to recognize gas turbine faults. However, some studies note high recognition capabilities of the second network. For the purpose of the comparison, both networks were included into a special testing procedure that computes for each network the true positive rate that is the probability of a correct diagnosis. Networks were first tuned and then compared using this criterion. Same procedure input data were fed to both networks during the comparison. However, to draw firm conclusions on the networks’ applicability, comparative calculations were repeated with different variations of these data. In particular, two engines that differ in an application and gas path structure were chosen as a test case. By way of summing up comparison results, the conclusion is that the radial basis network is a little more accurate than the perceptron, however the former needs much more available computer memory and computation time.
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Gao, B., J. Darling, D. G. Tilley, R. A. Williams, A. Bean, and J. Donahue. "Modelling of a Novel Gas Strut Using Neural Networks." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59119.

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The strut is one of the most important components in a vehicle suspension system. Since it is highly non-linear it is difficult to predict its performance characteristics using a physical mathematical model. However, neural networks have been successfully used as universal ‘black-box’ models in the identification and control of non-linear systems. This approach has been used to model a novel gas strut and the neural network was trained with experimental data obtained in the laboratory from simulated road profiles. The results obtained from the neural network demonstrated good agreement with the experimental results over a wide range of operation conditions. In contrast a linearised mathematical model using least square estimates of system parameters was shown to perform badly due to the highly non-linear nature of the system. A quarter car mathematical model was developed to predict strut behavior. It was shown that the two models produced different predictions of ride performance and it was argued that the neural network was preferable as it included the effects of non-linearities. Although the neural network model does not provide a good understanding of the physical behavior of the strut it is a useful tool for assessing vehicle ride and NVH performance due to its good computational efficiency and accuracy.
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Szczurek, Andrzej, Monika Maciejewska, and Tomasz Pietrucha. "Occupancy Detection using Gas Sensors." In 6th International Conference on Sensor Networks. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006207100990107.

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Loboda, Igor, and Yakov Feldshteyn. "Polynomials and Neural Networks for Gas Turbine Monitoring: A Comparative Study." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-23749.

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Gas turbine health monitoring includes the common stages of problem detection, fault identification, and prognostics. To extract useful diagnostic information from raw recorded data, these stages require a preliminary operation of computing differences between measurements and an engine baseline, which is a function of engine operating conditions. These deviations of measured values from the baseline data can be good indicators of engine health. However, their quality and success of all diagnostic stages strongly depend on an adequacy of the baseline model employed and, in particular, on mathematical techniques applied to create it. To create the baseline model we have applied polynomials and the least square method for computing their coefficients over a long period of time. Some methods were proposed to enhance such a polynomial-based model. The resulting accuracy was sufficient for reliable monitoring gas turbine deterioration effects. The polynomials previously investigated enough are used in the present study as a standard for evaluating artificial neural networks, a very popular technique in gas turbine diagnostics. The focus of this comparative study is to verify whether the use of networks results in a better description of the engine baseline. Extensive field data of two different industrial gas turbines were used to compare these two techniques in various conditions. The deviations were computed for all available data and quality of the resulting deviations plots was compared visually. A mean error of the baseline model was an additional criterion for the comparing the techniques. To find the best network configurations many network variations were realized and compared with the polynomials. Although the neural networks were found to be close to the polynomials in accuracy, they could not exceed the polynomials in any variation. In this way, it seems that polynomials can be successfully used for engine monitoring, at least for the analyzed gas turbines.
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Bulaev, V. I. "Using neural networks for geophysical data compression." In Data Science in Oil & Gas. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202054011.

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Botros, K. K., G. Kibrya, and A. Glover. "A Demonstration of Artificial Neural Networks Based Data Mining for Gas Turbine Driven Compressor Stations." In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0351.

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This paper presents a successful demonstration of application of Neural networks to perform various data mining functions on an RB211 gas turbine driven compressor station. Radial Basis Function networks were optimized and were capable of performing the following functions: a) Backup of critical parameters, b) Detection of sensor faults, c) Prediction of complete engine operating health with few variables, and d) Estimation of parameters that cannot be measured. A Kohonen SOM technique has also been applied to recognize the correctness and validity of any data once the network is trained on a good set of data. This was achieved by examining the activation levels of the winning unit on the output layer of the network. Additionally, it would also be possible to determine the suspicious, faulty or corrupted parameter(s) in the cases which are not recognized by the network by simply examining the activation levels of the input neurons.
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LaViolette, Marc, and Michael Strawson. "On the Prediction of Nitrogen Oxides From Gas Turbine Combustion Chambers Using Neural Networks." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-50566.

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This paper describes a method of predicting the oxides of nitrogen emissions from gas turbine combustion chambers using neural networks. A short review of existing empirical models is undertaken and the reasoning behind the choice of correlation variables and mathematical formulations is presented. This review showed that the mathematical functions obtained from the underlying theory used to develop the semi-empirical model ultimately limit their general applicability. Under these conditions, obtaining a semi-empirical model with a large domain and good accuracy is difficult. An overview of the use of neural networks as a modelling tool is given. Using over 2000 data points, a neural network that can predict NOx emissions with greater accuracy than published correlations was developed. The coefficients of determination of the prediction for the previous published semi-empirical models are 0.8048 and 0.7885. However one tends to grossly overpredict and the other underpredict. The coefficient of determination is 0.8697 for the model using a neural network. Because of the nature of neural networks, this more accurate model does not allow better insight into the physical and chemical phenomena. It is however, a useful tool for the initial design of combustion chambers.
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Reports on the topic "Gas networks"

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Winters, William S. A New Approach to Modeling Fluid/Gas Flows in Networks. Office of Scientific and Technical Information (OSTI), July 2001. http://dx.doi.org/10.2172/787888.

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Reed, Aaaron T. Bayesian Belief Networks for Fault Identification in Aircraft Gas Turbines. Fort Belvoir, VA: Defense Technical Information Center, June 2000. http://dx.doi.org/10.21236/ada378859.

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Melaina, M. W., O. Antonia, and M. Penev. Blending Hydrogen into Natural Gas Pipeline Networks. A Review of Key Issues. Office of Scientific and Technical Information (OSTI), March 2013. http://dx.doi.org/10.2172/1219920.

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Melaina, M. W., O. Antonia, and M. Penev. Blending Hydrogen into Natural Gas Pipeline Networks: A Review of Key Issues. Office of Scientific and Technical Information (OSTI), March 2013. http://dx.doi.org/10.2172/1068610.

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Kroeger, P. G., R. J. Kennett, J. Colman, and T. Ginsberg. THATCH: A computer code for modelling thermal networks of high- temperature gas-cooled nuclear reactors. Office of Scientific and Technical Information (OSTI), October 1991. http://dx.doi.org/10.2172/6239042.

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Hagen Schempf. Gas Main Sensor and Communications Network System. Office of Scientific and Technical Information (OSTI), May 2006. http://dx.doi.org/10.2172/890711.

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Kobos, Peter H., Alexander V. Outkin, Walter E. Beyeler, LaTonya Nicole Walker, Leonard A. Malczynski, Melissa M. Myerly, Vanessa N. Vargas, Craig M. Tenney, and David J. Borns. Natural Gas Value-Chain and Network Assessments. Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1221180.

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Hagen Schempf. Gas Main Sensor and Communications Network System. Office of Scientific and Technical Information (OSTI), February 2003. http://dx.doi.org/10.2172/924030.

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Hickmott, Donald Degarmo. FIND GAS SOURCE (FIGS) NEURAL NETWORK SOFTWARE. Office of Scientific and Technical Information (OSTI), December 2019. http://dx.doi.org/10.2172/1579674.

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Hagen Schempf, Ph D. GAS MAIN SENSOR AND COMMUNICATIONS NETWORK SYSTEM. Office of Scientific and Technical Information (OSTI), February 2003. http://dx.doi.org/10.2172/816706.

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