Статті в журналах: "Gas network modelling"

1

Ekhtiari, Ali, Damian Flynn, and Eoin Syron. "Green Hydrogen Blends with Natural Gas and Its Impact on the Gas Network." Hydrogen 3, no. 4 (October 27, 2022): 402–17. http://dx.doi.org/10.3390/hydrogen3040025.

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With increasing shares of variable and uncertain renewable generation in many power systems, there is an associated increase in the importance of energy storage to help balance supply and demand. Gas networks currently store and transport energy, and they have the potential to play a vital role in longer-term renewable energy storage. Gas and electricity networks are becoming more integrated with quick-responding gas-fired power plants, providing a significant backup source for renewable electricity in many systems. This study investigates Ireland’s gas network and operation when a variable green hydrogen input from excess wind power is blended with natural gas. How blended hydrogen impacts a gas network’s operational variables is also assessed by modelling a quasi-transient gas flow. The modelling approach incorporates gas density and a compressibility factor, in addition to the gas network’s main pressure and flow rate characteristics. With an increasing concentration of green hydrogen, up to 20%, in the gas network, the pipeline flow rate must be increased to compensate for reduced energy quality due to the lower energy density of the blended gas. Pressure drops across the gas pipeline have been investigated using different capacities of P2H from 18 MW to 124 MW. The results show significant potential for the gas network to store and transport renewable energy as hydrogen and improve renewable energy utilisation without upgrading the gas network infrastructure.

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2

Lin, Jiwei, and Tso-Chien Pan. "Modelling of multi-sectoral critical infrastructure interdependencies for vulnerability analysis." Disaster Prevention and Resilience 1, no. 1 (2022): 2. http://dx.doi.org/10.20517/dpr.2021.05.

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Critical infrastructure such as the transportation, power generation, water supply, telecommunications, security and health services/systems, etc. are essential for providing a reliable flow of goods and services, crucial to the functioning of the economy and society. These infrastructures are closely linked and dependent on one another, and these interdependencies need to be modelled in order to analyse the disruptions and vulnerabilities of critical infrastructure networks as a whole. With increased, investment and complexity in the coupling of gas and electricity network, limitations and vulnerabilities of the coupled networks are becoming increasingly relevant to the operational planning of the critical infrastructures. Current modelling of a coupled gas and electricity network will be used in conjunction with nation input-output interdependency model to model physical critical infrastructures and critical infrastructure interdependencies, respectively. This research work will tackle two possible scenarios that might happen in the gas network while evaluating the cascading impact both in the physical model perspective and input-output interdependency model perspective. The results will provide insights on how disruption in the gas network affects the electricity grid and its corresponding economic impact on all economic sectors in a nation.

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3

Anderson, Taylor, and Suzana Dragićević. "Representing Complex Evolving Spatial Networks: Geographic Network Automata." ISPRS International Journal of Geo-Information 9, no. 4 (April 20, 2020): 270. http://dx.doi.org/10.3390/ijgi9040270.

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Many real-world spatial systems can be conceptualized as networks. In these conceptualizations, nodes and links represent system components and their interactions, respectively. Traditional network analysis applies graph theory measures to static network datasets. However, recent interest lies in the representation and analysis of evolving networks. Existing network automata approaches simulate evolving network structures, but do not consider the representation of evolving networks embedded in geographic space nor integrating actual geospatial data. Therefore, the objective of this study is to integrate network automata with geographic information systems (GIS) to develop a novel modelling framework, Geographic Network Automata (GNA), for representing and analyzing complex dynamic spatial systems as evolving geospatial networks. The GNA framework is implemented and presented for two case studies including a spatial network representation of (1) Conway’s Game of Life model and (2) Schelling’s model of segregation. The simulated evolving spatial network structures are measured using graph theory. Obtained results demonstrate that the integration of concepts from geographic information science, complex systems, and network theory offers new means to represent and analyze complex spatial systems. The presented GNA modelling framework is both general and flexible, useful for modelling a variety of real geospatial phenomena and characterizing and exploring network structure, dynamics, and evolution of real spatial systems. The proposed GNA modelling framework fits within the larger framework of geographic automata systems (GAS) alongside cellular automata and agent-based modelling.

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4

Song, Wenhui, Jun Yao, Kai Zhang, Yongfei Yang, and Hai Sun. "Understanding gas transport mechanisms in shale gas reservoir: Pore network modelling approach." Advances in Geo-Energy Research 6, no. 4 (July 25, 2022): 359–60. http://dx.doi.org/10.46690/ager.2022.04.11.

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5

Hagey, L., and H. de Lasa. "C1–C4 Hydrocarbons from synthesis gas Reaction network modelling." Chemical Engineering Science 54, no. 15-16 (July 1999): 3391–97. http://dx.doi.org/10.1016/s0009-2509(98)00476-x.

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6

Parkinson, J. S., and R. J. Wynne. "Systems Modelling and Control Applied to a Low-Pressure Gas Distribution Network." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 206, no. 1 (February 1992): 35–44. http://dx.doi.org/10.1243/pime_proc_1992_206_196_02.

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A control system has been designed and implemented to provide more effective energy management of low-pressure gas distribution networks. The key to this is the provision of a control scheme that maintains low pressures across a network. The work was approached from first principles and a modelling technique has been developed which provides reduced order models that adequately describe the characteristics of multi-feed gas networks. The models were then used for the control system design, which in this case also included the selection of the optimal measurement points for most effective control. Following extensive design studies a relatively straightforward control scheme resulted which has been implemented and proved to be effective.

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7

Sprengel, Jurgen, Pedro Milano, Ryan Sfand, and Doug Kolak. "Modelling of High Point Vents in water gathering systems – a new approach using Simcenter Flomaster." APPEA Journal 62, no. 1 (May 13, 2022): 106–15. http://dx.doi.org/10.1071/aj21135.

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The cost-efficient extraction of coal seam gas (CSG) is the ultimate objective of every CSG project. One challenge is that gas holdups in water gathering lines can significantly obstruct water removal by causing higher back pressure on the wells, thus resulting in lower dewatering rates with subsequent delays in expected gas production. Gas holdups originate from entrained gas entering the wellhead pumps and from dissolved gas bubbling off in water gathering pipelines. Gas holdups are mechanically removed by High Point Vents (HPVs) installed at pipeline high points, with the efficiency of gas removal directly impacting project economics. A new hydraulic modelling approach enables the realistic simulation of two-phase flow regimes in a single-phase solver by utilising hydraulic components designed to separate gas and water. This modelling process offers major advantages over the traditional gas water ratio approach, as it enables the addition of individual HPVs to be analysed for their impact on the pressure reduction in water gathering pipelines. Modelling can be performed for any network complexity in either steady-state or transient simulation, enabling the analysis of the entire water gathering network over its life span of several decades. An operating envelope of well and pipeline pressure can be generated for an idealistic gas-free network, showing the lowest pressure and a network with fully developed gas holdups and resulting in the maximum possible back pressure. The paper includes a benchmarking study of an existing complex water gathering system confirming the practicality and accuracy of this game changing modelling approach.

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8

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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9

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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10

Jin, Tianying, Luis F. Ayala H., and M. Thaddeus Ityokumbul. "Network modelling and prediction of retrograde gas behaviour in natural gas pipeline systems." International Journal of Engineering Systems Modelling and Simulation 8, no. 3 (2016): 169. http://dx.doi.org/10.1504/ijesms.2016.077646.

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11

Chan, B. "Modelling gas metal arc weld geometry usingartificial neural network technology." Canadian Metallurgical Quarterly 38, no. 1 (January 1, 1999): 43–51. http://dx.doi.org/10.1016/s0008-4433(98)00037-8.

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12

Zhang, Zihang, Isam Saedi, Sleiman Mhanna, Kai Wu, and Pierluigi Mancarella. "Modelling of gas network transient flows with multiple hydrogen injections and gas composition tracking." International Journal of Hydrogen Energy 47, no. 4 (January 2022): 2220–33. http://dx.doi.org/10.1016/j.ijhydene.2021.10.165.

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13

Raos, Miomir, Ljiljana Zivkovic, Amelija Djordjevic, and Branislav Todorovic. "Modelling of the filter-adsorber type air cleaner by using neural network." Facta universitatis - series: Physics, Chemistry and Technology 7, no. 1 (2009): 23–31. http://dx.doi.org/10.2298/fupct0901023r.

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It is well known that most air purifying methods imply the passing of air flow, as a pollutant carrier, through a control unit which retains impurities. Properties of the air control unit and the purifying process itself therefore differ depending on the nature of present impurities, as well as on flow-thermal properties of air as the carrier of those impurities. For the assumed conditions, in terms of production of a pollution source and presence of different polluting substances in the form of dust, aerosols, gas, vapor in the exhaust gas, etc., an integrated gas purifier has been designed and tested, comprising a module for purification of mechanical impurities and a module for purification of gaseous impurities. The purifier is compact and has a universal application while simultaneously retaining several different pollutants. These requirements were met through application of the filtration and adsorption methods. On the formed experimental line with an adequate system of acquisition, filter-adsorber type gas cleaners in the function of flow-thermal parameters of gas mixture were tested simultaneously. Experimental data were used for training the radial basis function neural network, which was then used to model properties of the process and gas cleaner.

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14

Moreno, Ricardo, Diego Larrahondo, and Oscar Florez. "Power system operation considering detailed modelling of the natural gas supply network." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (December 1, 2021): 4740. http://dx.doi.org/10.11591/ijece.v11i6.pp4740-4750.

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The energy transition from fossil-fuel generators to renewable energies represents a paramount challenge. This is mainly due to the uncertainty and unpredictability associated with renewable resources. A greater flexibility is requested for power system operation to fulfill demand requirements considering security and economic restrictions. In particular, the use of gas-fired generators has increased to enhance system flexibility in response to the integration of renewable energy sources. This paper provides a comprehensive formulation for modeling a natural gas supply network to provide gas for thermal generators, considering the use of wind power sources for the operation of the electrical system over a 24-hour period. The results indicate the requirements of gas with different wind power level of integration. The model is evaluated on a network of 20 NG nodes and on a 24-bus IEEE RTS system with various operative settings during a 24-hour period.

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15

Грановський, E. А., and В. В. Смалій. "Model of stationary gas network." ВІСНИК СХІДНОУКРАЇНСЬКОГО НАЦІОНАЛЬНОГО УНІВЕРСИТЕТУ імені Володимира Даля, no. 2 (266) (March 13, 2021): 40–48. http://dx.doi.org/10.33216/1998-7927-2021-266-2-40-48.

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Modern problems of quantitative risk assessment require a development of more sophisticated types of so-called formation models. The formation models give all the information about an accidental leakage due the depressurization needed for quantitative estimation of the dangerous substances accumulating in the environment and further calculation of impact factors on humans, buildings etc. Common type of depressurization is a release of gaseous substances throughout the accidental hole on the surface of apparatus or pipeline. The pipeline connecting two vessels and a hole occurred on the pipeline as well as streams of vapour phase moving inside the pipelines and throughout the hole is a classical example of graph theory transport network problem. Thereby the model of stationary gas network based on equations of subsonic and choked adiabatic flow (for ideal gas) with accounting of mixing processes has been proposed. The solution with applying of graph theory, linear algebra and numerical analysis has been found.The gas net is represented as an oriented graph with nodes as a pressure-points and lines as pipelines. Case of incorrect estimation of flows directions has been studied and the problem of solving algorithm’s self-correction has been arisen. The method of incidence matrix correction during solving process has been developed and applied. The Newton’s method of non-linear equations system solving has been applied and specific method of Jacoby matrix correction has been developed.The behaviour of gas network model has been studied on example of a hydrocarbons’ mixture leakage from an accidental hole on the pipeline connecting two vessels. Results of numerical simulation experiments showed good agreement of model with basic laws of ideal gas adiabatic flow movement and gas network system in general. The directions of flows were in agreement with pressures’ differences on the lines as well as material and energy conservation laws have been observed. Model can be applied in numerical risks analysis for modelling of accidental processes of gaseous substances leaks as well as for the transport problems of chemical technology or educational purposes.

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16

Ekhtiari, Ali, Damian Flynn, and Eoin Syron. "Investigation of the Multi-Point Injection of Green Hydrogen from Curtailed Renewable Power into a Gas Network." Energies 13, no. 22 (November 19, 2020): 6047. http://dx.doi.org/10.3390/en13226047.

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Renewable electricity can be converted into hydrogen via electrolysis also known as power-to-H2 (P2H), which, when injected in the gas network pipelines provides a potential solution for the storage and transport of this green energy. Because of the variable renewable electricity production, the electricity end-user’s demand for “power when required”, distribution, and transmission power grid constrains the availability of renewable energy for P2H can be difficult to predict. The evaluation of any potential P2H investment while taking into account this consideration, should also examine the effects of incorporating the produced green hydrogen in the gas network. Parameters, including pipeline pressure drop, flowrate, velocity, and, most importantly, composition and calorific content, are crucial for gas network management. A simplified representation of the Irish gas transmission network is created and used as a case study to investigate the impact on gas network operation, of hydrogen generated from curtailed wind power. The variability in wind speed and gas network demands that occur over a 24 h period and with network location are all incorporated into a case study to determine how the inclusion of green hydrogen will affect gas network parameters. This work demonstrates that when using only curtailed renewable electricity during a period with excess renewable power generation, despite using multiple injection points, significant variation in gas quality can occur in the gas network. Hydrogen concentrations of up to 15.8% occur, which exceed the recommended permitted limits for the blending of hydrogen in a natural gas network. These results highlight the importance of modelling both the gas and electricity systems when investigating any potential P2H installation. It is concluded that, for gas networks that decarbonise through the inclusion of blended hydrogen, active management of gas quality is required for all but the smallest of installations.

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17

Azevedo-Perdicoúlis, Teresa Paula, and Gerhard Jank. "MODELLING ASPECTS OF DESCRIBING A GAS NETWORK THROUGH A DAE SYSTEM." IFAC Proceedings Volumes 40, no. 20 (2007): 40–45. http://dx.doi.org/10.3182/20071017-3-br-2923.00007.

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18

Korb, R., H. P. J�rgl, and B. Lutz. "Nonlinear Dynamic Modelling of a Gas Engine Using an RBF-Network." Mathematical and Computer Modelling of Dynamical Systems 5, no. 2 (June 1, 1999): 133–51. http://dx.doi.org/10.1076/mcmd.5.2.133.6171.

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19

Chan, Billy, Jack Pacey, and Malcolm Bibby. "Modelling Gas Metal Arc Weld Geometry Using Artificial Neural Network Technology." Canadian Metallurgical Quarterly 38, no. 1 (January 1999): 43–51. http://dx.doi.org/10.1179/cmq.1999.38.1.43.

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20

Holubar, P., L. Zani, M. Hagar, W. Fröschl, Z. Radak, and R. Braun. "Modelling of anaerobic digestion using self-organizing maps and artificial neural networks." Water Science and Technology 41, no. 12 (June 1, 2000): 149–56. http://dx.doi.org/10.2166/wst.2000.0259.

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In this work the training of a self-organizing map and a feed-forward back-propagation neural network was made. The aim was to model the anaerobic digestion process. To produce data for the training of the neural nets an anaerobic digester was operated at steady state and disturbed by pulsing the organic loading rate. Measured parameters were: gas composition, gas production rate, volatile fatty acid concentration, pH, redox potential, volatile suspended solids and chemical oxygen demand of feed and effluent. It could be shown that both types of self-learning networks in principle could be used to model the process of anaerobic digestion. Using the unsupervised Kohonen self-organizing map, the model's predictions could not follow the measurements in all details. This resulted in an unsatisfactory regression coefficient of R2= 0.69 for the gas composition and R2= 0.76 for the gas production rate. When the supervised FFBP neural net was used the training resulted in more precise predictions. The regression coefficient was found to be R2= 0.74 for the gas composition and R2== 0.92 for the gas production rate.

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21

Das, Arghya, Sumit Basu, and Ankit Kumar. "Modelling of shale rock pore structure based on gas adsorption." E3S Web of Conferences 92 (2019): 15006. http://dx.doi.org/10.1051/e3sconf/20199215006.

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Shale rock consists of a complex matrix structure due to presence of nano-scale pores. Owing to such complexity determination and/or prediction of the mineralogical, mechanical, and petrophysical properties (e.g., permeability, porosity, pore size distribution, etc.) of shale is a challenging task. A preliminary estimation of these properties is essential before shale gas exploration. In this study, experimental and numerical analyses are conducted to estimate the permeability, porosity, and pore size distribution of a typical shale sample. Gas adsorption experiments were conducted to characterize the pore spaces of the shale via analysing the isotherms. Using conventional theories, such as BET and BJH methods, surface area, pore volume, and pore size distributions were estimated. On the other hand, gross porosity of the shale samples was measured by conducting gas pycnometry experiment. Finally based on the obtained results an equivalent pore network model is constructed which accounts for the pore size distributions and low pore connectivity in the shale matrix. We have simulated gas flow through the network to estimate permeability of the shale. This model considers Knudsen diffusion and the effects of gas slippage on permeability. Further parametric study shows that the apparent permeability primarily depends on the reservoir pressure, pore coordination number and porosity.

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22

Vigants, Edgars, Toms Prodanuks, Girts Vigants, Ivars Veidenbergs, and Dagnija Blumberga. "Modelling of Technological Solutions to 4th Generation DH Systems." Environmental and Climate Technologies 20, no. 1 (November 27, 2017): 5–23. http://dx.doi.org/10.1515/rtuect-2017-0007.

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Abstract Flue gas evaporation and condensing processes are investigated in a direct contact heat exchanger - condensing unit, which is installed after a furnace. By using equations describing processes of heat and mass transfer, as well as correlation coherences for determining wet gas parameters, a model is formed to create a no-filling, direct contact heat exchanger. Results of heating equipment modelling and experimental research on the gas condensing unit show, that the capacity of the heat exchanger increases, when return temperature of the district heating network decreases. In order to explain these alterations in capacity, the character of the changes in water vapour partial pressure, in the propelling force of mass transfer, in gas and water temperatures and in the determining parameters of heat transfer are used in this article. The positive impact on the direct contact heat exchanger by the decreased district heating (DH) network return temperature shows that introduction of the 4th generation DH system increases the energy efficiency of the heat exchanger. In order to make an assessment, the methodology suggested in the paper can be used in each particular situation.

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23

Mehdizadeh, N. S., and P. Sinaei. "Modelling methane-air turbulent diffusion flame in a gas turbine combustor with artifical neural network." Aeronautical Journal 113, no. 1146 (August 2009): 541–47. http://dx.doi.org/10.1017/s0001924000003195.

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Abstract The present paper reports a way of using an artificial neural network (ANN) for modelling methane-air jet diffusion turbulent flame characteristics, such as temperature and chemical species mass fractions in a gas turbine combustion chamber. Since the neural network needs sets of examples to adapt its synaptic weights in the training phase, we used pre-assumed probability density function (PDF) method and considered chemical equilibrium chemistry model to compute the flame characteristics for generating the examples of input-output data sets. In this approach, flow and mixing field results are presented with a non-linear first order k-ε model. The turbulence model is applied in combination with preassumed β-PDF modelling for turbulence-chemistry interaction. The training algorithm for the neural network is based on a back-propagation supervised learning procedure, and the feed-forward multilayer network is incorporated as neural network architecture. The ability of ANN model to represent a highly non-linear system, such as a turbulent non-premixed flame is illustrated, and it can be summarized that the results of modelling of the combustion characteristics using ANN model are satisfactory, and the CPU-time and memory savings encouraging.

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24

Casadei, Riccardo, Marco Lorenzini, Daniele Fattini, and Paolo Valdiserri. "Dynamic Modelling of a Heat Exchanger Network for a Dairy Plant." Journal of Physics: Conference Series 2385, no. 1 (December 1, 2022): 012005. http://dx.doi.org/10.1088/1742-6596/2385/1/012005.

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Abstract In co-and trigeneration plants with internal combustion engines, waste heat from their cooling system can be fed to a heat exchanger network for office heating, hot water production, or used in industrial processes. At the same time, care must be exerted not to cool the return fluid below a threshold, in order to avoid piston seizure. In this work, the heat exchanger network is that of a gas-engine trigeneration system for a large dairy plant in Rome. Since its daily and weekly production schedule is subject to several changes, temperature control must be both precise and efficient. A thermal-hydraulic, dynamic (i.e. time-dependent) model of the heat exchanger network was developed in Matlab/Simulink, to obtain the instantaneous pressure, mass flowrate and temperature of the fluids along the network. The approach is mixed, a lumped-parameter description of the hydraulic and thermal networ, and finite volumes for the heat exchangers. The model has been verified and validated with experimental, steady-state data from the dairy plant and the results found satisfactory.

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25

Guan, Raymond, and Aimy Bazylak. "Determining Local Transport Properties in Gas Diffusion Layers Using Pore Network Modelling." ECS Meeting Abstracts MA2021-02, no. 36 (October 19, 2021): 1009. http://dx.doi.org/10.1149/ma2021-02361009mtgabs.

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26

Song, Wenhui, Jun Yao, Dongying Wang, Yang Li, Hai Sun, and Yongfei Yang. "Dynamic pore network modelling of real gas transport in shale nanopore structure." Journal of Petroleum Science and Engineering 184 (January 2020): 106506. http://dx.doi.org/10.1016/j.petrol.2019.106506.

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27

Gorikhovskii, Viacheslav I., and Elena V. Kustova. "Neural network approach in modelling vibrational kinetics of carbon dioxide." Vestnik of Saint Petersburg University. Mathematics. Mechanics. Astronomy 9, no. 4 (2022): 665–78. http://dx.doi.org/10.21638/spbu01.2022.409.

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The study is devoted to modeling nonequilibrium vibrational kinetics of carbon dioxide taking into account complex mechanisms of relaxation and intermode energy exchanges. The possibilities of using machine learning methods to improve the performance of numerical simulation of non-equilibrium carbon dioxide flows are studied. Various strategies for increasing the efficiency of the hybrid four-temperature model of CO2 kinetics are considered. The neural network approach proposed by the authors to calculate the rate of vibrational relaxation in each mode turned out to be the most promising. For the problem of spatially homogeneous relaxation, estimates of the error and computational costs of the developed algorithm are carried out, and its high accuracy and efficiency are demonstrated. For the first time, the carbon dioxide flow behind a plane shock wave was simulated in a full state-to-state approximation. A comparison with the results obtained in the framework of the hybrid four-temperature approach is carried out, and the equivalence of the approaches is shown. This makes it possible to recommend developed multitemperature approxima tions as the main tool for solving problems of nonequilibrium kinetics and gas dynamics. The hybrid four-temperature approach using the neural network method for calculating relaxation terms showed the acceleration of numerical simulation in time by more than an order of magnitude, while maintaining accuracy. This technique can be recommended for solving complex multidimensional problems of nonequilibrium gas dynamics, including state-to-state chemical reactions.

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Shaharun, Azwan. "Integrated modelling of entire production network and topsides facilities for production optimisation of major oil and gas fields." APPEA Journal 53, no. 2 (2013): 474. http://dx.doi.org/10.1071/aj12085.

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An oil company sought to identify bottlenecks in three of their main oil and gas production networks. It was desired to, therefore, develop the entire production network from wells, flowlines, intra-field and inter-field pipelines, and export pipelines up to the onshore terminal first stage separator/slug catcher, all in the transient multi-phase-flow oil and gas (OLGA) simulator. Furthermore, the detailed topsides facilities were separately modelled in a process simulator. The OLGA and process simulator models were subsequently integrated, where the flow simulator model received boundary pressures from the topsides model and pushed through the mass flows of the individual phases into the process simulator. After field-matching and tuning the integrated models to the given field data, optimising the overall fields’ production and performance was carried out, powered by a market-leading optimisation engine. The main optimisation parameters were: wellhead choke openings; gas lift rates and allocations; and topsides operating conditions, facility constraints and control tuning parameters. The network models were used to investigate the dynamic behaviour of wells and pipelines as well as surface process facilities equipment and control systems, with the aim to improve productivity of the entire field networks. The development of the integrated and dynamic well, pipeline and process models is part of company initiatives to facilitate the design and operational support tools for the company’s engineers.

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Gilbert, Thomas, Stuart Barr, Philip James, Jeremy Morley, and Qingyuan Ji. "Software Systems Approach to Multi-Scale GIS-BIM Utility Infrastructure Network Integration and Resource Flow Simulation." ISPRS International Journal of Geo-Information 7, no. 8 (August 1, 2018): 310. http://dx.doi.org/10.3390/ijgi7080310.

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There is an increasing impetus for the use of digital city models and sensor network data to understand the current demand for utility resources and inform future infrastructure service planning across a range of spatial scales. Achieving this requires the ability to represent a city as a complex system of connected and interdependent components in which the topology of the electricity, water, gas, and heat demand-supply networks are modelled in an integrated manner. However, integrated modelling of these networks is hampered by the disparity between the predominant data formats and modelling processes used in the Geospatial Information Science (GIS) and Building Information Modelling (BIM) domains. This paper presents a software systems approach to scale-free, multi-format, integrated modelling of evolving cross-domain utility infrastructure network topologies, and the analysis of the spatiotemporal dynamics of their resource flows. The system uses a graph database to integrate the topology of utility network components represented in the CityGML UtilityNetwork Application Domain Extension (ADE), Industry Foundation Classes (IFC) and JavaScript Object Notation (JSON) real-time streaming messages. A message broker is used to disseminate the changing state of the integrated topology and the dynamic resource flows derived from the streaming data. The capability of the developed system is demonstrated via a case study in which internal building and local electricity distribution feeder networks are integrated, and a real-time building management sensor data stream is used to simulate and visualise the spatiotemporal dynamics of electricity flows using a dynamic web-based visualisation.

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Xia, Tong-qiang, Ke Gao, Hong-yun Ren, Jiao-fei He, and Zi-long Li. "Network Design Mode of In-Seam Gas Extraction Parameters Using Mathematical Modelling—Take Tangan Colliery as an Example." Geofluids 2020 (November 10, 2020): 1–12. http://dx.doi.org/10.1155/2020/8886068.

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Gas extraction is a practical and effective way to guarantee mining-process safety and deliver greater environmental benefits through reducing greenhouse gas emissions and increase the supply of a valuable clean gas resource. It has been effective in recent years, however it still has a series of problems that need to be solved. Gas extraction design mainly relies on engineering experience rather than quantitative design, resulting in low input-output ratio of gas extraction because of unreasonable design. How to build a bridge of communication between engineers and scientists is the key to realize scientific gas extraction. In this work, taking our previous gas-coal and gas-coal-heat coupling models of gas extraction as the theoretical basis, a new communication and design concept—an engineering design platform for gas extraction—is proposed using the network mode. Through the platform, on- and off-line interactions between service centre (scientific workers) and design objects (enterprises or individuals), such as data transmission, material review, scheme design and reviews, and so on. It greatly improves the efficiency and standardization of gas extraction design. Appling the networked platform, the gas extraction engineering parameters were quantitatively designed in the working face of 3307, Tangan colliery. According to the extraction time, the working face was divided into 6 extraction units. The number of boreholes were 763, the drilling capacity of coal was 0.03 m/t, and the extraction rate of each unit was more than 25%. The networked mode of in-seam gas extraction design would transform the traditional experience to the quantitative mode.

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Lin, Yan Cherng, Han Ming Chow, Hsin Min Lee, and Jia Feng Liu. "Modelling of the Parameters of EDM in Gas Based on Back Propagation Neural Network." Materials Science Forum 926 (July 2018): 11–16. http://dx.doi.org/10.4028/www.scientific.net/msf.926.11.

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The aim of this study is to develop a predicted model of the machining parameters with relation to material removal rate (MRR) and surface roughness (SR) of electrical discharge machining (EDM) in gas. The experimental tasks were implemented by a specific design of experimental method named central composite design (CCD) method. The mathematical prediction models between operating parameters and machining characteristics based on artificial neural network (ANN) were established. The back propagation neural network (BPNN) was employed to construct the architecture of the input layer, the hidden layer and the output layer to build the ANN model. Moreover, the weight and the bias values were examined by the steepest descent method (SDM) with the training data. Thus, the suitable ANN models were established with the acquired weight and bias values. The essential parameters of the EDM in gas such as peak current (Ip), pulse duration (tp), gas pressure (GP), servo reference voltage (Sv) were chosen to investigate the effects on MRR and SR. The developed ANN model with 4 input variables on the input layer, one hidden layer with 5 neurons, and 2 response variables on the output layer was obtained by the training with 30 experimental data. Moreover, as the prediction values obtained from the ANN compared with the 5 testing data, the error falls in the rage of 5% indicating the developed ANN is appropriate and predictable. Moreover, the developed ANN model can be used to predict the machining characteristics such as MRR and SR for the EDM in gas with various parameter settings.

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Kainourgiakis, M. E., E. S. Kikkinides, A. K. Stubos, and N. K. Kanellopoulos. "Adsorption–desorption gas relative permeability through mesoporous media—network modelling and percolation theory." Chemical Engineering Science 53, no. 13 (July 1998): 2353–64. http://dx.doi.org/10.1016/s0009-2509(98)00084-0.

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Ouyang, Yicun, and Hujun Yin. "A neural gas mixture autoregressive network for modelling and forecasting FX time series." Neurocomputing 135 (July 2014): 171–79. http://dx.doi.org/10.1016/j.neucom.2013.12.037.

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Ekhtiari, Ali, Ioannis Dassios, Muyang Liu, and Eoin Syron. "A Novel Approach to Model a Gas Network." Applied Sciences 9, no. 6 (March 13, 2019): 1047. http://dx.doi.org/10.3390/app9061047.

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The continuous uninterrupted supply of Natural Gas (NG) is crucial to today’s economy, with issues in key infrastructure, e.g., Baumgarten hub in Austria in 2017, highlighting the importance of the NG infrastructure for the supply of primary energy. The balancing of gas supply from a wide range of sources with various end users can be challenging due to the unique and different behaviours of the end users, which in some cases span across a continent. Further complicating the management of the NG network is its role in supporting the electrical network. The fast response times of NG power plants and the potential to store energy in the network play a key role in adding flexibility across other energy systems. Traditionally, modelling the NG network relies on nonlinear pipe flow equations that incorporate the demand (load), flow rate, and physical network parameters including topography and NG properties. It is crucial that the simulations produce accurate results quickly. This paper seeks to provide a novel method to solve gas flow equations through a network under steady-state conditions. Firstly, the model is reformulated into non-linear matrix equations, then the equations separated into their linear and nonlinear components, and thirdly, the non-linear system is solved approximately by providing a linear system with similar solutions to the non-linear one. The non-linear equations of the NG transport system include the main variables and characteristics of a gas network, focusing on pressure drop in the gas network. Two simplified models, both of the Irish gas network (1. A gas network with 13 nodes, 2. A gas network with 109 nodes) are used as a case study for comparison of the solutions. Results are generated by using the novel method, and they are compared to the outputs of two numerical methods, the Newton–Raphson solution using MATLAB and SAINT, a commercial software that is used for the simulation of the gas network and electrical grids.

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35

YUAN, ZHAOHUI, DEWEN HU, LIHONG HUANG, and GUOHUA DONG. "ON THE GLOBAL ASYMPTOTIC STABILITY ANALYSIS OF DELAYED NEURAL NETWORKS." International Journal of Bifurcation and Chaos 15, no. 12 (December 2005): 4019–25. http://dx.doi.org/10.1142/s0218127405014453.

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In this paper, the problem of the global asymptotic stability (GAS) of a class of delayed neural network is investigated. Under the generalization of dropping the boundedness and differentiability hypotheses for activation functions, using some existing results for the existence and uniqueness of the equilibrium point, we obtain a couple of general results concerning GAS by means of Lyapunov functional method without the assumption of symmetry of interconnection matrix. Our results improve and extend some previous works of other researchers. Moreover, our conditions are presented in terms of system parameters, which have leading significance in designs and applications of GAS for Hopfield neural network (HNNs) and delayed cellular neural network (DCNNs).

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36

Asgari, Hamid, Mohsen Fathi Jegarkandi, XiaoQi Chen, and Raazesh Sainudiin. "Design of conventional and neural network based controllers for a single-shaft gas turbine." Aircraft Engineering and Aerospace Technology 89, no. 1 (January 3, 2017): 52–65. http://dx.doi.org/10.1108/aeat-11-2014-0187.

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Purpose The purpose of this paper is to develop and compare conventional and neural network-based controllers for gas turbines. Design/methodology/approach Design of two different controllers is considered. These controllers consist of a NARMA-L2 which is an artificial neural network-based nonlinear autoregressive moving average (NARMA) controller with feedback linearization, and a conventional proportional-integrator-derivative (PID) controller for a low-power aero gas turbine. They are briefly described and their parameters are adjusted and tuned in Simulink-MATLAB environment according to the requirement of the gas turbine system and the control objectives. For this purpose, Simulink and neural network-based modelling is used. Performances of the controllers are explored and compared on the base of design criteria and performance indices. Findings It is shown that NARMA-L2, as a neural network-based controller, has a superior performance to PID controller. Practical implications This study aims at using artificial intelligence in gas turbine control systems. Originality/value This paper provides a novel methodology for control of gas turbines.

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37

Dawson, James M., Timothy A. Davis, Edward L. Gomez, and Justus Schock. "A self-supervised, physics-aware, Bayesian neural network architecture for modelling galaxy emission-line kinematics." Monthly Notices of the Royal Astronomical Society 503, no. 1 (February 13, 2021): 574–85. http://dx.doi.org/10.1093/mnras/stab427.

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ABSTRACT In the upcoming decades, large facilities, such as the SKA, will provide resolved observations of the kinematics of millions of galaxies. In order to assist in the timely exploitation of these vast data sets, we explore the use of a self-supervised, physics-aware neural network capable of Bayesian kinematic modelling of galaxies. We demonstrate the network’s ability to model the kinematics of cold gas in galaxies with an emphasis on recovering physical parameters and accompanying modelling errors. The model is able to recover rotation curves, inclinations and disc scale lengths for both CO and H i data which match well with those found in the literature. The model is also able to provide modelling errors over learned parameters, thanks to the application of quasi-Bayesian Monte Carlo dropout. This work shows the promising use of machine learning, and in particular, self-supervised neural networks, in the context of kinematically modelling galaxies. This work represents the first steps in applying such models for kinematic fitting and we propose that variants of our model would seem especially suitable for enabling emission-line science from upcoming surveys with e.g. the SKA, allowing fast exploitation of these large data sets.

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Gao, Zhong-Ke, Dong-Mei Lv, Wei-Dong Dang, Ming-Xu Liu, and Xiao-Lin Hong. "Multilayer Network from Multiple Entropies for Characterizing Gas-Liquid Nonlinear Flow Behavior." International Journal of Bifurcation and Chaos 30, no. 01 (January 2020): 2050014. http://dx.doi.org/10.1142/s0218127420500145.

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Characterizing nonlinear dynamic behaviors underlying multiphase flow has attracted considerable attention from the nonlinear research field. In this paper, the authors develop a novel multiple entropy-based multilayer network (MEMN) for exploring the complex gas-liquid two-phase flow. At first, we carry out the gas-liquid flow experiments to get the multichannel measurements. Then, MEMN is constructed based on the fusion of three nonlinear entropies, namely weighted permutation entropy (WPE), wavelet packet energy entropy (WPEE), and amplitude entropy (AE). For each derived projection network of MEMN, spectral radius and global clustering coefficient are both calculated and they allow effectively uncovering the nonlinear flow behaviors in the transition of different gas-liquid flow patterns. In addition, we perform wavelet time-frequency representation for the two typical flow patterns and the results support our findings well. All these demonstrate that our MEMN framework can effectively characterize the nonlinear evolution of gas-liquid flow from the perspective of complex network theory. And this also provides a novel idea for studying nonlinear complex systems from the observed multivariate time series.

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Senyel Kurkcuoglu, Muzeyyen Anil, and Beyda Nur Zengin. "Spatio-Temporal Modelling of the Change of Residential-Induced PM10 Pollution through Substitution of Coal with Natural Gas in Domestic Heating." Sustainability 13, no. 19 (September 30, 2021): 10870. http://dx.doi.org/10.3390/su131910870.

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Air pollution has been one of the most critical urban problems. Urban energy networks are among the major sources of air pollution, particularly in highly populated urban areas. Residential heating, which is the primary cause of particulate matter (PM) emissions, contributes to the problem through the use of low-quality fuels, such as coal. Natural gas, although a fossil fuel, is a modern, relatively clean, and more efficient alternative in residential energy use, which helps to reduce particulate matter emissions. Coal was widely used in residential heating in İzmir, Turkey, whereas natural gas is a relatively new alternative which started to be used domestically in 2006. Switching from coal and other highly polluting fossil fuels to natural gas in urban energy distribution network has contributed to the alleviation of air pollution in the city in the past decade. Spatiotemporal analyses of the PM10 concentrations, and their relation to the natural gas investments, have been conducted in geographical information systems (GIS). The spatial distribution of the change in PM10 levels has been modeled with ordinary kriging for the 2010–2011 and 2018–2019 winter seasons. Interpolated PM10 surfaces show that there is a significant decrease in the emissions throughout the city in the overall, while the highest levels of decrease are observed in the southern part of the city. Overlaying the interpolated PM10 surfaces and the natural gas pipeline investments enables the demonstration of the mutual relationship between the change in emission levels and the energy distribution network. Indeed, the spatial distribution of the pollution concentrations appears to be parallel to the natural gas investments. The pipeline investments were intensive during the 2010–2018 period in the southern districts when compared the rest of the city. The use of natural gas in residential heating contributed to the decrease in PM10 emissions.

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40

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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41

Adi Putra, Zulfan, Zalina Harun, and Shahrul Azman Zainal Abidin. "Development of Dynamic Simulation of Gas and Condensate Pipeline Network." E3S Web of Conferences 287 (2021): 03016. http://dx.doi.org/10.1051/e3sconf/202128703016.

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Developing a steady operation of gas and condensate value chain is an important task to maintain stable productions of oil & gas industries. In this regard, PETRONAS continues to improve its production facilities by utilizing process modelling and simulation via Symmetry iCON® as one of its main engineering tools. In this work, Symmetry iCON® pipe network solver was used to build a dynamic simulation model for gas and condensate pipeline network in Malaysian Peninsular region. One-month data of December 2018 has been used to validate the model. Then it was utilized to predict the data in January 2019 to further evaluate the applicability of the model. Some valuable observations included the significance of properties estimation of a pseudo component of C6+ in terms of thermodynamic and transport properties. Due to lack of data monitoring of the condensate in some terminals, this property estimation became very crucial while at the same time difficult to validate. Nonetheless, the model can predict the data within the range of error of 4-6%. In the future, when more data is available, the properties can be easily tuned to better represent the reality.

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42

Shtykov, R. A. "Automation of the control process of a gas pipeline network with a complex topological structure." Journal of Physics: Conference Series 2182, no. 1 (March 1, 2022): 012011. http://dx.doi.org/10.1088/1742-6596/2182/1/012011.

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Abstract The reliability of the gas pipeline network is determined by the perfection of the structures, the precise fullfilament of the conditions of its operation, ensuring the coordination of the flow parameters in the pipe with the parameters of the blowers. Deviation from the established mode of operation in gas pipelines can affect the stability of the system. With a significant deviation from the operating mode, various features arise, both kinematic and dynamic deviations in the flow, which leads to unstable operation of the system. Therefore, it is necessary to automate the control processes of the pipeline gas network during gas transportation, which will be based on the results of mathematical modelling of mass transfer processes in pipes to determine the main flow parameters. It is also known that the main losses and changes in flow parameters in pipelines occur along the entire length of the linear sections of the network. Therefore, to adapt these changes to specific objects, it is necessary to use more accurate mathematical models that allow to adequately control the processes in the network based on automated control systems.

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Rahmoune, Mohamed Ben, Ahmed Hafaifa, Abdellah Kouzou, XiaoQi Chen, and Ahmed Chaibet. "Gas turbine monitoring using neural network dynamic nonlinear autoregressive with external exogenous input modelling." Mathematics and Computers in Simulation 179 (January 2021): 23–47. http://dx.doi.org/10.1016/j.matcom.2020.07.017.

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44

Pellegrino, Sandro, Andrea Lanzini, and Pierluigi Leone. "Greening the gas network – The need for modelling the distributed injection of alternative fuels." Renewable and Sustainable Energy Reviews 70 (April 2017): 266–86. http://dx.doi.org/10.1016/j.rser.2016.11.243.

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45

Xiao, Cong, and Leng Tian. "Modelling of fractured horizontal wells with complex fracture network in natural gas hydrate reservoirs." International Journal of Hydrogen Energy 45, no. 28 (May 2020): 14266–80. http://dx.doi.org/10.1016/j.ijhydene.2020.03.161.

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46

Hoare, D., RL Jones, NRP Harris, V. Ferracci, D. Carruthers, A. Stidworthy, E. Forsyth, and M. Rigby. "Development of an urban greenhouse gas modelling system to support a London monitoring network." Weather 75, no. 11 (September 7, 2020): 353–59. http://dx.doi.org/10.1002/wea.3795.

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47

Asgari, Hamid, Xiao Qi Chen, Mohammad Bagher Menhaj, and Raazesh Sainudiin. "ANN-Based System Identification, Modelling and Control of Gas Turbines – A Review." Advanced Materials Research 622-623 (December 2012): 611–17. http://dx.doi.org/10.4028/www.scientific.net/amr.622-623.611.

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Gas Turbines (GTs) are the beating heart of nearly all industrial plants and specifically play a vital role in oil and power industries. Significant research activities have been carried out to discover accurate dynamics and to approach to the optimal operational point of these systems. A variety of analytical and experimental system identification methods, models and control systems has been investigated so far for gas turbines. Artificial neural network (ANN) has been recognized as one of the successful approaches that can disclose nonlinear behaviour of such complicated systems. This paper briefly reviews major ANN-based research activities in the field of system identification, modelling and control of gas turbines. It can be used as a reference for those who are interested to work and study in this area.

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48

Clegg, Stephen, and Pierluigi Mancarella. "Storing renewables in the gas network: modelling of power-to-gas seasonal storage flexibility in low-carbon power systems." IET Generation, Transmission & Distribution 10, no. 3 (February 18, 2016): 566–75. http://dx.doi.org/10.1049/iet-gtd.2015.0439.

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49

Afuape, Gbenga, Myles Regan, Ronald May, Vernon Roewer, Anton Chung, and Nuntawan Silpngarmlers. "Integrated production modelling of the Wheatstone-Iago fields: boon or bane?" APPEA Journal 52, no. 2 (2012): 639. http://dx.doi.org/10.1071/aj11053.

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Applying integrated production modelling (IPM) for decision making in the oil and gas industry has proliferated rapidly, evidenced by the amount of published information about successful applications of this approach. A reason for its popularity is to mitigate the risk of over(under)investment, which is driving asset teams toward jettisoning the practice of using fixed THP to account for backpressure effects or to use the limited surface network options available in most numerical reservoir simulators. This extended abstract describes the modelling of an offshore gas development by coupling multiple full-field, numerical reservoir simulation models with a shared surface network model. Such an approach enabled subsurface elements of the production system to be linked directly to surface elements (subsea and platform) yielding a fully coupled IPM. Key development decisions were tested and justified in a technically rigorous and economically robust manner. These decisions included the phasing of development wells, compression requirements and flow balancing in the pipeline system to maintain specified gas delivery rates. Experience from this approach has shown traditional reservoir engineering techniques can still yield the same outcome as an IPM with comparable accuracy—for some development decisions such as using a creaming curve and fixed THP to determine optimum well count; nevertheless, using simple methods to account for backpressure effects may not allow the same broad-based integration of design requirements needed at the design and engineering stage of large-scale projects. The PowerPoint presentation is not available to APPEA.

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MIAO, TONGJUN, AIMIN CHEN, YAN XU, SUJUN CHENG, and KEDONG WANG. "A PERMEABILITY MODEL FOR WATER–GAS PHASE FLOW IN FRACTAL FRACTURE NETWORKS." Fractals 26, no. 06 (December 2018): 1850087. http://dx.doi.org/10.1142/s0218348x18500871.

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Flow mechanism of water–gas phase is of significant importance to accurately understand and predict the fluid flow behavior in many fields, such as water and oil exploration, hot dry rock development and optimization of fuel cells. In this work, a novel relative permeability (RP) model for two-phase flow in fracture networks is proposed based on the fractal theory. The proposed model is expressed as a nonlinear function of saturation of water and gas with no empirical parameter. A stronger interference between water and gas phases than that of the X-model is found. The proposed model may provide a better understanding of the fundamental mechanism of water–gas phase flow in fracture network.

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