Journal articles: 'Control systems/gas networks'

1

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

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

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Osiadacz, Andrzej J., and Maciej Chaczykowski. "Dynamic Control for Gas Pipeline Systems." Archives of Mining Sciences 61, no. 1 (April 1, 2016): 69–82. http://dx.doi.org/10.1515/amsc-2016-0006.

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Abstract An algorithm for optimal control of a gas network with any configuration based upon hierarchical control and decomposition of the network is described. Local problems are solved using a gradient technique. The subsystems are coordinated using „good coordination” method to find the overall optimum. Discrete state equation for the case in which output pressures are treated as elements of the control vector has been formulated. Results of investigations are included.

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Kambhampati, C., C. Perkgoz, R. J. Patton, and W. Ahamed. "An interaction predictive approach to fault-tolerant control in network control systems." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 221, no. 6 (September 1, 2007): 885–94. http://dx.doi.org/10.1243/09596518jsce377.

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This paper illustrates some of the capabilities of previously proposed network control system (NCS) architectures to carry on functioning in the event of faults, without recourse to system reconfiguration. The principle of interaction prediction is used to set up a coordination strategy that encapsulates an ability to withstand or tolerate certain faults, thereby allowing the system to continue functioning. It is also shown that the coordination strategy can be made more effective if a learning agent is allowed to learn the coordination functions. This facilitates the use of different types of agent at the local level, together with recurrent networks and genetic algorithms (GAs) at the coordination level. The experimental test-bed system is a benchmark three-tank system that has some of the main features of an industrial process control system.

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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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Marqués, Dardo, and Manfred Morari. "On-line optimization of gas pipeline networks." Automatica 24, no. 4 (July 1988): 455–69. http://dx.doi.org/10.1016/0005-1098(88)90091-x.

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Ferrigno, L., R. Morello, V. Paciello, and A. Pietrosanto. "Remote Metering in Public Networks." Metrology and Measurement Systems 20, no. 4 (December 1, 2013): 705–14. http://dx.doi.org/10.2478/mms-2013-0061.

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Abstract New research trends in energy grids and water networks push toward ICT solutions for allowing remote metering of consumption. In the paper, after an introduction to the European Standards on smart metering, two visual sensors thought to solve typical metering problems in water public networks are described. Particular detail is given hardware and software solutions and the perspective of integration with analog gas and electric energy metering devices.

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Chun, Yangha. "Analysis of Cyber Threats in the Connection Section of the Control System and Countermeasures Required." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 10, 2021): 412–17. http://dx.doi.org/10.17762/turcomat.v12i6.1831.

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In the past, the general practice for the control system network that manages and controls industrial facilities such as electric power, gas, oil, water, chemicals, automobiles, etc. was to install and operate this as an independent system, but over time the practice has gradually shifted toward the use of an open and standardized system. Until recently, most industrial control systems consisted of an independent network, and the possibility of cyber threat infringement was very low. As information storage media such as laptops or USB are connected to the control system for maintenance or management purposes, the possibility of cyber infringement is increasing. When the use of the control system's operational information increases due to beingVinked with the internal business system network or the Internet, countermeasures against external cyber threats must be provided.This paper analyzes and organizes the cyber threat factors that exist in the linking section connected to the industrial control system and other networks, examining domestic and foreign incidents of hacking of control systems to identify the vulnerabilities and security measures for each scenario in the control system network linkage section. Through this analysis, a method is suggested for establishing a control network that secures both availability and security, which are important in the control system, as well as the safe relay system in the configuration of the linkage between the control network and the business network, while addressing the vulnerabilities in the structure due to long-term use of the control system.This study analyzes cyber threat factors and real-life examples of infringements with the aim of providing approaches that will ensure industrial control systems can be operated safely and the risk of cyber hacking threats that occur in connection with other networks can be managed, and suggesting cyber security measures for the control system connection sections.

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Machón-González, Iván, and Hilario López-García. "Feedforward Nonlinear Control Using Neural Gas Network." Complexity 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3125073.

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Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a set of local linear models obtained by a supervised neural gas network. The proposed approach takes advantage of the neural gas feature by which the algorithm yields a very robust clustering procedure. The direct model of the plant constitutes a piece-wise linear approximation of the nonlinear system and each neuron represents a local linear model for which a linear controller is designed. The neural gas model works as an observer and a controller at the same time. A state feedback control is implemented by estimation of the state variables based on the local transfer function that was provided by the local linear model. The gradient vectors obtained by the supervised neural gas algorithm provide a robust procedure for feedforward nonlinear control, that is, supposing the inexistence of disturbances.

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Rao, Nandan, Shubhra Srivastava, and Sreekanth K.S. "PKI Deployment Challenges and Recommendations for ICS Networks." International Journal of Information Security and Privacy 11, no. 2 (April 2017): 38–48. http://dx.doi.org/10.4018/ijisp.2017040104.

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Different types of Control Systems used for Industrial production are broadly called Industrial Control Systems (ICS) (n.d.). These include Supervisory Control and Data Acquisition (SCADA) Systems and Distributed Control Systems (DCS). ICS typically perform the “Command and Control” operations required for smooth functioning of machines in industrial production. They are widely used in Critical infrastructures such as power generation and distribution systems, Oil and Gas plants, Chemical factories and various other manufacturing facilities. Traditionally, ICS used proprietary protocols and operated in isolation. Perimeter security was considered enough as the only means to compromise these systems was via physical access to the systems. However, increased standardization, open architecture adoption and connectivity to enterprise networks as well as internet opened up these systems for a wide range of audience and enabled remote attacks. In this new operation landscape, it is imperative that newer means of securing the networks are adopted continuously. One of the security considerations for the ICS networks is the communication paths used by these networks. Public Key Infrastructure (PKI) (n.d.) plays a key role in securing the communication of ICS networks. Using Digital Certificates, PKI provides a mechanism to verify the identity of all the entities on a network and also ensures that the information is shared securely between communicating entities. PKI is a proven mechanism for secure communication and is widely used in many organizations. However, PKI as a solution to ICS security is challenging due to factors such as resource constrained environments, bandwidth considerations and hard real-time communication requirements. This paper intends to focus on key challenges in Digital Certificate management and correct deployment of PKI in ICS networks and will also present recommendations to overcome these challenges without compromising the basic functionalities of ICS.

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11

Osiadacz, A. J., and D. J. Bell. "A simplified algorithm for optimization of large-scale gas networks." Optimal Control Applications and Methods 7, no. 1 (January 1986): 95–104. http://dx.doi.org/10.1002/oca.4660070107.

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12

Nazir, Sajid, Shushma Patel, and Dilip Patel. "Autoencoder Based Anomaly Detection for SCADA Networks." International Journal of Artificial Intelligence and Machine Learning 11, no. 2 (July 2021): 83–99. http://dx.doi.org/10.4018/ijaiml.20210701.oa6.

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Supervisory control and data acquisition (SCADA) systems are industrial control systems that are used to monitor critical infrastructures such as airports, transport, health, and public services of national importance. These are cyber physical systems, which are increasingly integrated with networks and internet of things devices. However, this results in a larger attack surface for cyber threats, making it important to identify and thwart cyber-attacks by detecting anomalous network traffic patterns. Compared to other techniques, as well as detecting known attack patterns, machine learning can also detect new and evolving threats. Autoencoders are a type of neural network that generates a compressed representation of its input data and through reconstruction loss of inputs can help identify anomalous data. This paper proposes the use of autoencoders for unsupervised anomaly-based intrusion detection using an appropriate differentiating threshold from the loss distribution and demonstrate improvements in results compared to other techniques for SCADA gas pipeline dataset.

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Wang, Chao, Bailing Wang, Yunxiao Sun, Yuliang Wei, Kai Wang, Hui Zhang, and Hongri Liu. "Intrusion Detection for Industrial Control Systems Based on Open Set Artificial Neural Network." Security and Communication Networks 2021 (August 18, 2021): 1–14. http://dx.doi.org/10.1155/2021/4027900.

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The security of industrial control systems (ICSs) has received a lot of attention in recent years. ICSs were once closed networks. But with the development of IT technologies, ICSs have become connected to the Internet, increasing the potential of cyberattacks. Because ICSs are so tightly linked to human lives, any harm to them could have disastrous implications. As a technique of providing protection, many intrusion detection system (IDS) studies have been conducted. However, because of the complicated network environment and rising means of attack, it is difficult to cover all attack classes, most of the existing classification techniques are hard to deploy in a real environment since they cannot deal with the open set problem. We propose a novel artificial neural network based-methodology to solve this problem. Our suggested method can classify known classes while also detecting unknown classes. We conduct research from two points of view. On the one hand, we use the openmax layer instead of the traditional softmax layer. Openmax overcomes the limitations of softmax, allowing neural networks to detect unknown attack classes. During training, on the other hand, a new loss function termed center loss is implemented to improve detection ability. The neural network model learns better feature representations with the combined supervision of center loss and softmax loss. We evaluate the neural network on NF-BoT-IoT-v2 and Gas Pipeline datasets. The experiments show our proposed method is comparable with the state-of-the-art algorithm in terms of detecting unknown classes. But our method has a better overall classification performance.

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14

Khaytbaev, A. E., and A. M. Eshmuradov. "APPLICATIONS OF NEURAL NETWORK TECHNOLOGIES IN WIRELESS SENSOR NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 195 (September 2020): 46–51. http://dx.doi.org/10.14489/vkit.2020.09.pp.046-051.

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The purpose of the article is to study the possibilities of improving the efficiency of the sensory network management technique, using the neural network method. The presented model of the wireless sensor network takes into account the charging of the environment. The article also tests the hypothesis of the possibility of organizing distributed computing in wireless sensor networks. To achieve this goal, a number of tasks are allocated: review and analysis of existing methods for managing BSS nodes; definition of simulation model components and their properties of neural networks and their features; testing the results of using the developed method. The article explores the major historical insights of the application of the neural network technologies in wireless sensor networks in the following practical fields: engineering, farming, utility communication networks, manufacturing, emergency notification services, oil and gas wells, forest fires prevention equipment systems, etc. The relevant applications for the continuous monitoring of security and safety measures are critically analyzed in the context of the relevancy of specific decisions to be implemented within the system architecture. The study is focused on the modernization of methods of control and management for the wireless sensor networks considering the environmental factors to be allocated using senor systems for data maintenance, including the information on temperature, humidity, motion, radiation, etc. The article contains the relevant and adequate comparative analysis of the updated versions of node control protocols, the components of the simulation model, and the control method based on neural networks to be identified and tested within the practical organizational settings.

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Khaytbaev, A. E., and A. M. Eshmuradov. "APPLICATIONS OF NEURAL NETWORK TECHNOLOGIES IN WIRELESS SENSOR NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 195 (September 2020): 46–51. http://dx.doi.org/10.14489/vkit.2020.09.pp.046-051.

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The purpose of the article is to study the possibilities of improving the efficiency of the sensory network management technique, using the neural network method. The presented model of the wireless sensor network takes into account the charging of the environment. The article also tests the hypothesis of the possibility of organizing distributed computing in wireless sensor networks. To achieve this goal, a number of tasks are allocated: review and analysis of existing methods for managing BSS nodes; definition of simulation model components and their properties of neural networks and their features; testing the results of using the developed method. The article explores the major historical insights of the application of the neural network technologies in wireless sensor networks in the following practical fields: engineering, farming, utility communication networks, manufacturing, emergency notification services, oil and gas wells, forest fires prevention equipment systems, etc. The relevant applications for the continuous monitoring of security and safety measures are critically analyzed in the context of the relevancy of specific decisions to be implemented within the system architecture. The study is focused on the modernization of methods of control and management for the wireless sensor networks considering the environmental factors to be allocated using senor systems for data maintenance, including the information on temperature, humidity, motion, radiation, etc. The article contains the relevant and adequate comparative analysis of the updated versions of node control protocols, the components of the simulation model, and the control method based on neural networks to be identified and tested within the practical organizational settings.

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16

Palau, A., E. Velo, and L. Puigjaner. "Use of neural networks and expert systems to control a gas/solid sorption chilling machine." International Journal of Refrigeration 22, no. 1 (January 1999): 59–66. http://dx.doi.org/10.1016/s0140-7007(97)00046-7.

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Gnezdilova, Olga, Galina Shedrina, and Vasily Amelin. "Modeling of Natural Gas Transportation and Distribution in Low Pressure Gas Pipelines." Applied Mechanics and Materials 725-726 (January 2015): 1307–12. http://dx.doi.org/10.4028/www.scientific.net/amm.725-726.1307.

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It is known that the area of the implementation of automated systems for remote control of the technological processes of gas transportation and distribution, as well as its commercial metering and consumption is set for the settlements with population exceeding 100 thousand people. It has a centralized structure, including the lower level − controlled stations, and the upper level − central dispatcher board, which is implemented in the form of automation-equipped workstations connected using computer networks.

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Wiid, A. J., J. D. le Roux, and I. K. Craig. "Modelling of methane-rich gas pipeline networks for simulation and control." Journal of Process Control 92 (August 2020): 234–45. http://dx.doi.org/10.1016/j.jprocont.2020.06.010.

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OSIADACZ, ANDRZEJ J., and KRZYSZTOF PIENKOSZ. "Methods of steady-state simulation for gas networks." International Journal of Systems Science 19, no. 7 (January 1988): 1311–21. http://dx.doi.org/10.1080/00207728808547163.

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ZUO, Ran, and XiuXiu SUN. "Study on gas reaction path in GaN HVPE." SCIENTIA SINICA Technologica 49, no. 9 (April 25, 2019): 1100–1106. http://dx.doi.org/10.1360/sst-2019-0008.

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Rüdiger, Jens. "Enhancements of the numerical simulation algorithm for natural gas networks based on node potential analysis." IFAC-PapersOnLine 53, no. 2 (2020): 13119–24. http://dx.doi.org/10.1016/j.ifacol.2020.12.2282.

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SUN, QiuYe, XiaoTing YU, Rui WANG, and DaZhong MA. "Research on stability control strategy of electricity-gas coupling network based on dynamic model of natural gas network." SCIENTIA SINICA Technologica 49, no. 4 (October 18, 2018): 479–90. http://dx.doi.org/10.1360/n092018-00122.

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Gnezhdilova, Olga, Anna Malysheva, Anna Gagulina, and Aleksandr Ovsienko. "The concept of reliability-oriented accounting of leaks in gas supply systems." E3S Web of Conferences 97 (2019): 03021. http://dx.doi.org/10.1051/e3sconf/20199703021.

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The concept of reliability-oriented maintenance of gas distribution networks is substantiated. Performed an analytical review of works of leading scientists in the field of the theory of gas supply of individual household. It has been established that the question of control and to prevent gas leaks remains relevant now. It is proved, that for engineering systems use gas for domestic purposes, also the greatest risk are low-intensity gas leaks, that hardly controlled by hardware due to accounting errors. An algorithm is presented for vulnerability assessment elements engineering gas supply systems for leaks. The mathematical model for the evaluation of measuring error of parametric parameter of gas flow in the system. The technique for monitoring the tightness of gas systems engineering with the rationale for selecting the method of evaluation of error results.

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Banda, Mapundi K., and Michael Herty. "Towards a space mapping approach to dynamic compressor optimization of gas networks." Optimal Control Applications and Methods 32, no. 3 (March 30, 2010): 253–69. http://dx.doi.org/10.1002/oca.929.

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WU, KeLiu, XiangFang LI, and ZhangXin CHEN. "Real gas transport through nanopores of shale gas reservoirs." SCIENTIA SINICA Technologica 46, no. 1 (January 1, 2016): 68–78. http://dx.doi.org/10.1360/n092015-00076.

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Gugat, Martin, and Michaël Herty. "Existence of classical solutions and feedback stabilization for the flow in gas networks." ESAIM: Control, Optimisation and Calculus of Variations 17, no. 1 (August 11, 2009): 28–51. http://dx.doi.org/10.1051/cocv/2009035.

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Babchuk, S. M. "CLASSIFICATION OF WIRELESS SPECIALIZED COMPUTER NETWORKS FOR THE OBJECTS LOCATION MONITORING." METHODS AND DEVICES OF QUALITY CONTROL, no. 1(42) (June 27, 2019): 70–76. http://dx.doi.org/10.31471/1993-9981-2019-1(42)-70-76.

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At present, it is possible to achieve the desired level of management of any enterprise only if there is complete and up-to-date source information about the current state of affairs in this enterprise. Growing size of an enterprise complicates the collection of necessary information and its processing. The speed and completeness of data collection is also influenced by many other factors associated with the specifics of specific enterprises. In particular, oil and gas companies have a large number of objects located at large distances from each other (including in remote and hard-toreach areas). In addition, these enterprises operate a variety of equipment and uses a large range of different matetechnical values. Quite often, for certain managerial needs, equipment and material and technical values can be moved within a single unit located in a large area or between units of one oil and gas undertaking. In connection with the aforementioned present, the enterprises of the oil and gas complex require the introduction of modern effective systems for collecting information on available material and technical values and their location at a certain point in time. In order to solve this problem, an analysis of modern wireless specialized digital networks was carried out and identified from them, which can be used at enterprises of the oil and gas complex for the monitoring of the location of the objects. Also, the basic characteristics of wireless specialized digital networks (which can be used for systems for monitoring the location of objects), by which they can be classified, are also established. A classification of wireless specialty digital networks (which can be used for objects monitoring systems) is created, which allows the specialists of control and measurement equipment and automation services to select the most appropriate wireless digital network for monitoring the location objects of oil and gas enterprises.

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Schoendienst, Thilo, and Vinod M. Vokkarane. "Reducing Greenhouse Gas Emissions With Power Source-Aware Multidomain Multilayer Networks." IEEE Systems Journal 11, no. 2 (June 2017): 673–83. http://dx.doi.org/10.1109/jsyst.2015.2448599.

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Furey, B. P. "A sequential quadratic programming-based algorithm for optimization of gas networks." Automatica 29, no. 6 (November 1993): 1439–50. http://dx.doi.org/10.1016/0005-1098(93)90008-h.

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Dodd, N., and J. Martin. "Using neural networks to optimise gas turbine aero engines." Computing & Control Engineering Journal 8, no. 3 (June 1, 1997): 129–35. http://dx.doi.org/10.1049/cce:19970305.

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Sagdatullin, A. M. "Improving the energy efficiency of oil production using identification and prediction of operating modes of production wells based on data analysis methods, machine learning and neural networks." E3S Web of Conferences 124 (2019): 05031. http://dx.doi.org/10.1051/e3sconf/201912405031.

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Currently, there is a need to improve the systems and control of pumping equipment in the oil and gas production and oil and gas transport industries. Therefore, an adaptive neural network control system for an electric drive of a production well was developed. The task of expanding the functional capabilities of asynchronous electric motors control of the oil and gas production system using the methods of neural networks is solved. We have developed software modules of the well drive control system based on the neural network, an identification system, and a scheme to adapt the control processes to changing load parameters, that is, to dynamic load, to implement the entire system for real-time control of the highspeed process. In this paper, based on a model of an identification block that includes a multilayered neural network of direct propagation, the control of the well system was implemented. The neural network of the proposed system was trained on the basis of the error back-propagation algorithm, and the identification unit works as a forecaster of system operation modes based on the error prediction. In the initial stage of the model adaptation, some fluctuations of the torque are observed at the output of the neural network, which is associated with new operating conditions and underestimated level of learning. However, the identification object and control system is able to maintain an error at minimum values and adapt the control system to a new conditions, which confirms the reliability of the proposed scheme.

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PARK, BYOUNG-JUN, WITOLD PEDRYCZ, and SUNG-KWUN OH. "SIMPLIFIED FUZZY INFERENCE RULE-BASED GENETICALLY OPTIMIZED HYBRID FUZZY NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 02 (April 2008): 245–74. http://dx.doi.org/10.1142/s0218488508005169.

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In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the condition part of the rule-based structure of the gHFNN. The conclusion part of the gHFNN is designed using PNNs. We distinguish between two types of the simplified fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the conclusion part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, we experimented with three representative numerical examples. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when compared with other neurofuzzy models.

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ZHANG, Hong, and Ran ZUO. "Effect of radicals on gas phase reactions in GaN MOVPE process." SCIENTIA SINICA Technologica 49, no. 8 (June 18, 2019): 939–46. http://dx.doi.org/10.1360/n092018-00394.

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van der Heijde, Annelies Vandermeulen, Salenbien, and Helsen. "Integrated Optimal Design and Control of Fourth Generation District Heating Networks with Thermal Energy Storage." Energies 12, no. 14 (July 18, 2019): 2766. http://dx.doi.org/10.3390/en12142766.

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In the quest to increase the share of renewable and residual energy sources in our energy system, and to reduce its greenhouse gas emissions, district heating networks and seasonal thermal energy storage have the potential to play a key role. Different studies prove the techno-economic potential of these technologies but, due to the added complexity, it is challenging to design and control such systems. This paper describes an integrated optimal design and control algorithm, which is applied to the design of a district heating network with solar thermal collectors, seasonal thermal energy storage and excess heat injection. The focus is mostly on the choice of the size and location of these technologies and less on the network layout optimisation. The algorithm uses a two-layer program, namely with a design optimisation layer implemented as a genetic algorithm and an optimal control evaluation layer implemented using the Python optimal control problem toolbox called modesto. This optimisation strategy is applied to the fictional district energy system case of the city of Genk in Belgium. We show that this algorithm can find optimal designs with respect to multiple objective functions and that even in the cheaper, less renewable solutions, seasonal thermal energy storage systems are installed in large quantities.

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Hu, Xiao-Bing, and Mark S. Leeson. "Evolutionary Computation with Spatial Receding Horizon Control to Minimize Network Coding Resources." Scientific World Journal 2014 (2014): 1–23. http://dx.doi.org/10.1155/2014/268152.

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The minimization of network coding resources, such as coding nodes and links, is a challenging task, not only because it is a NP-hard problem, but also because the problem scale is huge; for example, networks in real world may have thousands or even millions of nodes and links. Genetic algorithms (GAs) have a good potential of resolving NP-hard problems like the network coding problem (NCP), but as a population-based algorithm, serious scalability and applicability problems are often confronted when GAs are applied to large- or huge-scale systems. Inspired by the temporal receding horizon control in control engineering, this paper proposes a novel spatial receding horizon control (SRHC) strategy as a network partitioning technology, and then designs an efficient GA to tackle the NCP. Traditional network partitioning methods can be viewed as a special case of the proposed SRHC, that is, one-step-wide SRHC, whilst the method in this paper is a generalizedN-step-wide SRHC, which can make a better use of global information of network topologies. Besides the SRHC strategy, some useful designs are also reported in this paper. The advantages of the proposed SRHC and GA for the NCP are illustrated by extensive experiments, and they have a good potential of being extended to other large-scale complex problems.

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Barbasova, T. A. "A MULTILEVEL RESOURCE-SAVING BLAST FURNACE PROCESS CONTROL." Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 21, no. 1 (February 2021): 136–46. http://dx.doi.org/10.14529/ctcr210112.

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A multilevel resource-saving blast furnace process control is considered. The resource-saving control is provided for operating, adaptation, technical and economic control in the automated systems of blast-furnace processes. It is proposed to form optimal operation modes of blast furnace heating, metal charge structures, natural gas and oxygen consumption. Decisions are made using Kohonen neural networks taking into account current and planned parameters of coke quality, iron ore, raw materials and blast. At the level of operating control, the work suggests a model predictive control to improve the resource conservation indicators. The method is based on decomposition of the general problem of the process dynamics identification on particular problems: dynamic synchronization and identification of process transfer functions. At the level of adaptive control, optimal operating modes of blast furnaces are expedient to be developed with respect to blast furnace heating, structure of metal charge, natural gas and oxygen rate considering the current and planned parameters of coke, blasting. The blast furnace operating modes are suggested to be determined based on Kohonen neural networks. In evaluating the efficiency of introducing the model predictive control, the existing actual statistics of scatter of BF mode parameters should be based upon. The fact is that the introduction of model predictive control assumes no radical change of the BF melt technology. Like in all the control systems, the BF process is considered as the set control object with all its characteristics. Changing process settings, raw material content does not introduce any cardinal variation in the scatter of process characteristics. However, in this case a transient process occurs which is necessary for the control system to identify the changing conditions. The transient process is inherent to all the control systems and the blast furnace process is not an exclusion. As a result of transient process, the control system is set to the optimal mode.

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Zhou, Hang, Jason McGinty, and Ajith K. Parlikad. "Failure Prediction of Auxiliary Lube Oil Pump in Gas Turbines: Applying Monte Carlo Neural Networks to an Unbalanced Dataset." IFAC-PapersOnLine 53, no. 3 (2020): 131–36. http://dx.doi.org/10.1016/j.ifacol.2020.11.021.

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OSIADACZ, ANDRZEJ J. "Numerical analysis of a method of transient simulation for gas networks." International Journal of Systems Science 21, no. 5 (May 1990): 961–75. http://dx.doi.org/10.1080/00207729008910424.

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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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Chen, Qian, Robert K. Abercrombie, and Frederick T. Sheldon. "Risk Assessment For Industrial Control Systems Quantifying Availability Using Mean Failure Cost (MFC)." Journal of Artificial Intelligence and Soft Computing Research 5, no. 3 (July 1, 2015): 205–20. http://dx.doi.org/10.1515/jaiscr-2015-0029.

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Abstract 1 Industrial Control Systems (ICS) are commonly used in industries such as oil and natural gas, transportation, electric, water and wastewater, chemical, pharmaceutical, pulp and paper, food and beverage, as well as discrete manufacturing (e.g., automotive, aerospace, and durable goods.) SCADA systems are generally used to control dispersed assets using centralized data acquisition and supervisory control. Originally, ICS implementations were susceptible primarily to local threats because most of their components were located in physically secure areas (i.e., ICS components were not connected to IT networks or systems). The trend toward integrating ICS systems with IT networks (e.g., efficiency and the Internet of Things) provides significantly less isolation for ICS from the outside world thus creating greater risk due to external threats. Albeit, the availability of ICS/SCADA systems is critical to assuring safety, security and profitability. Such systems form the backbone of our national cyber-physical infrastructure. Herein, we extend the concept of mean failure cost (MFC) to address quantifying availability to harmonize well with ICS security risk assessment. This new measure is based on the classic formulation of Availability combined with Mean Failure Cost (MFC). The metric offers a computational basis to estimate the availability of a system in terms of the loss that each stakeholder stands to sustain as a result of security violations or breakdowns (e.g., deliberate malicious failures).

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Fernø, M. A., J. Gauteplass, M. Pancharoen, A. Haugen, A. Graue, A. R. Kovscek, and G. Hirasaki. "Experimental Study of Foam Generation, Sweep Efficiency, and Flow in a Fracture Network." SPE Journal 21, no. 04 (August 15, 2016): 1140–50. http://dx.doi.org/10.2118/170840-pa.

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Summary Foam generation for gas mobility reduction in porous media is a well-known method and frequently used in field applications. Application of foam in fractured reservoirs has hitherto not been widely implemented, mainly because foam generation and transport in fractured systems are not clearly understood. In this laboratory work, we experimentally evaluate foam generation in a network of fractures within fractured carbonate slabs. Foam is consistently generated by snap-off in the rough-walled, calcite fracture network during surfactant-alternating-gas (SAG) injection and coinjection of gas and surfactant solution over a range of gas fractional flows. Boundary conditions are systematically changed including gas fractional flow, total flow rate, and liquid rates. Local sweep efficiency is evaluated through visualization of the propagation front and compared for pure gas injection, SAG injection, and coinjection. Foam as a mobility-control agent resulted in significantly improved areal sweep and delayed gas breakthrough. Gas-mobility reduction factors varied from approximately 200 to more than 1,000, consistent with observations of improved areal sweep. A shear-thinning foam flow behavior was observed in the fracture networks over a range of gas fractional flows.

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Bezrodny, Alexey А., Renat R. Yunushev, and Anatoly М. Korolyonok. "System analysis and efficient structure formation for gas station nets." SCIENCE & TECHNOLOGIES OIL AND OIL PRODUCTS PIPELINE TRANSPORTATION 10, no. 1 (February 29, 2020): 96–105. http://dx.doi.org/10.28999/2541-9595-2020-10-1-96-105.

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Gas station nets as a type of petroleum supply systems are necessary to provide working and development of the stations themselves since alone objects of the type are often commercially inefficient. For their improvement, it is purposeful by means of the set-theoretic approach known to create and to apply the models of information, organization-technical, decision-making, control and non-active (infra-) systems. The models said would describe the networks operation and development at conditions of friendly, competitive and indifferent surroundings. In the article, structure models of the systems mentioned are presented for gas station nets, whereas synthesis methods (on processes, objects and time periods) of the structure variants optimal on given criteria and their features are discussed and some results obtained are shown.

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Cięszczyk, Sławomir. "A Multi-Band Integrated Virtual Calibration-Inversion Method for Open Path FTIR Spectrometry." Metrology and Measurement Systems 20, no. 2 (June 1, 2013): 287–98. http://dx.doi.org/10.2478/mms-2013-0025.

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Abstract This paper addresses problems arising from in situ measurement of gas content and temperature. Such measurements can be considered indirect. Transmittance or natural radiation of a gas is measured directly. The latter method (spectral radiation measurement) is often called spectral remote sensing. Its primary uses are in astronomy and in the measurement of atmospheric composition. In industrial processes, in situ spectroscopic measurements in the plant are often made with an open path Fourier Transform Infrared (FTIR) spectrometer. The main difficulty in this approach is related to the calibration process, which often cannot be carried out in the manner used in the laboratory. Spectral information can be obtained from open path spectroscopic measurements using mathematical modeling, and by solving the inverse problem. Determination of gas content based on spectral measurements requires comparison of the measured and modeled spectra. This paper proposes a method for the simultaneous use of multiple lines to determine the gas content. The integrated absorptions of many spectral lines permits calculation of the average band absorption. An inverse model based on neural networks is used to determine gas content based on mid-infrared spectra at variable temperatures.

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Ahmed, Ahmed, Mathias Kleiner, and Lionel Roucoules. "Model-Based Interoperability IoT Hub for the Supervision of Smart Gas Distribution Networks." IEEE Systems Journal 13, no. 2 (June 2019): 1526–33. http://dx.doi.org/10.1109/jsyst.2018.2851663.

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Sundar, Kaarthik, and Anatoly Zlotnik. "State and Parameter Estimation for Natural Gas Pipeline Networks Using Transient State Data." IEEE Transactions on Control Systems Technology 27, no. 5 (September 2019): 2110–24. http://dx.doi.org/10.1109/tcst.2018.2851507.

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Lalik, Krzysztof, Mateusz Kozek, Szymon Podlasek, Rafał Figaj, and Paweł Gut. "Q-Learning Neural Controller for Steam Generator Station in Micro Cogeneration Systems." Energies 14, no. 17 (August 27, 2021): 5334. http://dx.doi.org/10.3390/en14175334.

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This article presents the results of the optimization of steam generator control systems powered by mixtures of liquid fuels containing biofuels. The numerical model was based on the results of experimental research of steam generator operation in an open system. The numerical model is used to build control algorithms that improve performance, increase efficiency, reduce fuel consumption and increase safety in the full range of operation of the steam generator and the cogeneration system of which it is a component. In this research, the following parameters were monitored: temperature and pressure of the circulating medium, exhaust gas temperature, oxygen content in exhaust gas, percentage control of oil burner power. Two methods of controlling the steam generator were proposed: the classic one, using the PID regulator, and the advanced one, using artificial neural networks. The work shows how the model is adapted to the real system and the impact of the control algorithms on the efficiency of the combustion process. The example is considered for the implementation of advanced control systems in micro-, small- and medium-power cogeneration and trigeneration systems in order to improve their final efficiency and increase the profitability of implementation.

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Ma, Lei, Liang Cheng, and Manchun Li. "Quantitative risk analysis of urban natural gas pipeline networks using geographical information systems." Journal of Loss Prevention in the Process Industries 26, no. 6 (November 2013): 1183–92. http://dx.doi.org/10.1016/j.jlp.2013.05.001.

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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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Sundin, Heidi, David Brown, James Wakefield, and Janet Ranganathan. "Management Control Systems in a Non-Enterprise Network: The Greenhouse Gas Protocol Initiative." Australian Accounting Review 19, no. 2 (June 2009): 93–102. http://dx.doi.org/10.1111/j.1835-2561.2009.00048.x.

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Ilyushin, Pavel, Aleksandr Kulikov, Konstantin Suslov, and Sergey Filippov. "Consideration of Distinguishing Design Features of Gas-Turbine and Gas-Reciprocating Units in Design of Emergency Control Systems." Machines 9, no. 3 (February 24, 2021): 47. http://dx.doi.org/10.3390/machines9030047.

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Modern gas-turbine units (GTUs) and gas-reciprocating units (GRUs) have found a wide use at power plants, including distributed generation facilities, running on gaseous fuel. The design features of these generating units have a considerable effect on the nature and parameters of transient processes due to emergency disturbances in the adjacent network. The study shows that single-shaft gas-turbine and gas-reciprocating units do not allow even short-term considerable frequency drops. These schemes and operating conditions arise due to emergency active power shortages when the connection between the power plant and the power system weakens due to repair conditions or islanded operation. The paper presents the results of transient process calculations for operating power plants (distributed generation facilities), which make it possible to identify the unfavorable properties of GTUs and GRUs. The results show that two-shaft (three-shaft) GTUs and GRUs can switch to out-of-step conditions even when short-circuits in the adjacent network are cleared with high-speed relay protection devices. The features of out-of-step conditions and the admissibility of their short-term duration for the spontaneous restoration of generators’ synchronization are considered. The findings suggest that considering the fundamental design features of generating units provides informed technical decisions on equipping power plants (distributed generation (DG) facilities) and the adjacent network with efficient emergency control systems.

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Journal articles on the topic 'Control systems/gas networks'

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