Relevant bibliographies by topics / Control systems/gas networks

Contents

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

Academic literature on the topic 'Control systems/gas networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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

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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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Dissertations / Theses on the topic "Control systems/gas networks"

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

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

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Pearson, D. W. "Robust observer design and application to gas networks." Thesis, Coventry University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.380696.

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Mu, Junxia. "Design and application of advanced control methods to gas turbines and networked systems." Thesis, University of South Wales, 2005. https://pure.southwales.ac.uk/en/studentthesis/design-and-application-of-advanced-control-methods-to-gas-turbines-and-networked-systems(c6311aed-db75-415a-8b2a-f1594479f685).html.

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This thesis deals with the design and application of modern control techniques to a Rolls Royce aircraft gas turbine engine and networked systems. It is motivated by the need to fully exploit recent advances in control engineering and investigate the suitability of various control methods to gas turbine engines and networked systems. The main contributions of the first part of the thesis relate to the gas turbine engine control. Due to the nonlinearities of the gas turbine engines, the rate limiter and saturation constraints on the fuel feed, the aim is to illustrate the potential of a global nonlinear controller to cover the engine operating range. Several nonlinear control methods, gain-scheduling PID controller, approximate model predictive control (AMPC) and nonlinear model predictive control (NMPC), are presented along with the corresponding control algorithms. Since the parameters in a gain-scheduling PID controller change with the operating range, the need is apparent for a global nonlinear controller to cover its operating range. AMPC and NMPC are then demonstrated to be capable of providing a global nonlinear controller for the engine and can be used in the place of the gain scheduling PID controller. It is shown that AMPC is more preferable than NMPC if computational time is at a premium. The main theme of the second part of the thesis is the design and application of the networked predictive control (NPC) to compensate for the network delay and data packet dropout in both forward and backward channels for networked systems. NPC using both modified model predictive control and generic polynomial method is presented along with the corresponding control algorithms. For both approaches, the system stability for a fixed network delay is presented and an analytical stability criterion is obtained. This provides some guidelines on how to choose the NPC parameters in the case of random network delay. The performance of NPC can be further improved by using a robust NPC (RNPC). To validate the performance using the proposed control methods, a servo motor system is then used for both Intranet and Internet based simulations and practical experiments. A networked control test rig along with the network delay measurement method is used for real-time implementation. It is shown that both NPC and RNPC can efficiently compensate for the network delay and data packet dropout in both channels. This thesis provides basis for the real-time implementation of advanced control methods in gas turbine engines. While this work was applied to a gas turbine engine, these techniques can be applied to a range of nonlinear control systems. The work on the networked predictive control presented in this thesis can provide basis for further research relating to this area.
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DORASKEVICIUS, JUNIOR WALDEMAR. "Identificacao de padrões em sistemas supervisórios de instalações de reatores nucleares e em sistemas de gasodutos utilizando mapas auto-organizáveis." reponame:Repositório Institucional do IPEN, 2005. http://repositorio.ipen.br:8080/xmlui/handle/123456789/11273.

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Dissertacao (Mestrado)
IPEN/D
Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP
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Kleman, Björn, and Henrik Lindgren. "Evaluation of model-based fault diagnosis combining physical insights and neural networks applied to an exhaust gas treatment system case study." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176650.

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Fault diagnosis can be used to early detect faults in a technical system, which means that workshop service can be planned before a component is fully degraded. Fault diagnosis helps with avoiding downtime, accidents and can be used to reduce emissions for certain applications. Traditionally, however, diagnosis systems have been designed using ad hoc methods and a lot of system knowledge. Model-based diagnosis is a systematic way of designing diagnosis systems that is modular and offers high performance. A model-based diagnosis system can be designed by making use of mathematical models that are otherwise used for simulation and control applications. A downside of model-based diagnosis is the modeling effort needed when no accurate models are available, which can take a large amount of time. This has motivated the use of data-driven diagnosis. Data-driven methods do not require as much system knowledge and modeling effort though they require large amounts of data and data from faults that can be hard to gather. Hybrid fault diagnosis methods combining models and training data can take advantage of both approaches decreasing the amount of time needed for modeling and does not require data from faults. In this thesis work a combined data-driven and model-based fault diagnosis system has been developed and evaluated for the exhaust treatment system in a heavy-duty diesel engine truck. The diagnosis system combines physical insights and neural networks to detect and isolate faults for the exhaust treatment system. This diagnosis system is compared with another system developed during this thesis using only model-based methods. Experiments have been done by using data from a heavy-duty truck from Scania. The results show the effectiveness of both methods in an industrial setting. It is shown how model-based approaches can be used to improve diagnostic performance. The hybrid method is showed to be an efficient way of developing a diagnosis system. Some downsides are highlighted such as the performance of the system developed using data-driven and model-based methods depending on the quality of the training data. Future work regarding the modularity and transferability of the hybrid method can be done for further evaluation.
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Choudhary, P. A. "State estimation applied to gas distribution networks." Thesis, University of Manchester, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378007.

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The need to make more efficient use of energy resources by improved methods of control, the deferment of expensive pipework reinforcement or replacement and the requirement to accommodate different loading patterns on sections of the gas network have made the requirement for effective feedback control essential. Gas distribution systems are large and complex and a major problem in applying conventional feedback techniques is the cost and reliability of transmitting the data necessary for automatic control. In order to overcome these difficulties, state estimation techniques have been investigated as a means of providing information about a distribution system from a minimum number of measurement points. The theoretical background to the study is reviewed including the modelling technique and the results of experimental work which has been performed on a distribution network in order to verify the techniques developed are presented. The results show that state estimation techniques have considerable potential for this type of application.
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Ho, Chi-Jui. "Call admission control in cellular networks." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/15730.

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Ruano, Antonio Eduardo de Barras. "Applications of neural networks to control systems." Thesis, Bangor University, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.304917.

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Somanath, Amith. "Control of stochastically interacting systems on networks." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112459.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 109-117).
The goal of this thesis is to develop control theoretic analysis and algorithms for characterizing and controlling stochastically interacting systems on networks. Such systems have three essential features - (i) they are stochastic processes, (ii) they are made up several individual components connected through a network, and (iii) the connected components influence one another through local interactions. This thesis presents analysis and control of three representative examples of such systems from the fields of spreading processes, smart manufacturing, and transport phenomena. In the first part of the thesis, control of spreading processes on lattices is considered. Analysis and control of spreading processes is difficult because the dimensionality of state space is often large. A common approach to this issue is to use mean field approximations which completely average out the stochasticity inherent to these systems. Instead this thesis, using recently developed tools from nonequilibrium statistical physics, accurately characterizes open loop behavior of spreading processes in its stable, neutral and unstable regimes. Such a characterization is not possible using approximate models. Furthermore, for an unstable spreading process, a randomized control policy is proposed that is optimal in both resource allocation and control effort. In the second part of the thesis, control of smart manufacturing processes is considered. Due to increased product customization and rapidly changing demands, the recent trend in manufacturing is to shift towards modular architectures. Such a shift presents scheduling challenges in a rapidly and dynamically changing environment. This thesis presents a queuing theory framework for modeling job flow, and a stochastic scheduling algorithm. Such an approach is amenable for fast implementation while achieving balanced load among operating agents. In the last part of the thesis, control of transport phenomena is considered. Transport phenomena are systems that are in nonequilibrium. Even though study and analysis of systems exhibiting nonequilibrium phenomena have been considered in the past, there is no effective way to control or modify the behavior of these systems. This thesis presents control theoretic formulations for systems in nonequilibrium. Starting from a paradigmatic model for traffic flow known as totally asymmetric simple exclusion process (TASEP), thesis presents routing policies to achieve maximum flow rate of traffic for all set of input traffic conditions. Extensions are also made to TASEP models on intersections and generic road networks.
by Amith Somanath.
Ph. D.
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Books on the topic "Control systems/gas networks"

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Wu lian wang gan zhi yu kong zhi ji shu. Beijing: Qing hua da xue chu ban she, 2012.

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1973-, Ben-Ari Guy, ed. Transforming European militaries: Coalition operations and the technology gap. New York, NY: Routledge, 2006.

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Chiang, Mung. Power control in wireless cellular networks. Hanover, MA: Now Publishers, 2007.

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Maurice, Heemels, and Johansson Mikael, eds. Networked control systems. Berlin: Springer, 2010.

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D, Metz Stephen. Survey of gas tubine control for application to marine gas turbine propulsion system control. Monterey, Calif: Naval Postgraduate School, 1989.

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Gordon, Patrick C. Network management and control: Systems planning. Boston, Ma. (200 Portland St., Boston 02114): Yankee Group, 1986.

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Kuzirian, Robert. Gas turbine systems supervisor. [Pensacola, Fla.]: The Activity, 1994.

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A course in fuzzy systems and control. Upper Saddle River, N.J: Prentice Hall PTR, 1997.

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Moore, Kevin L. Iterative learning control for deterministic systems. London: Springer-Verlag, 1993.

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Networking for building automation and control systems. Lilburn, GA: Fairmont Press, 1992.

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Book chapters on the topic "Control systems/gas networks"

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Klempous, Ryszard, Barbara Łysakowska, and Jan Nikodem. "Supervisory control and data acquisition system for the gas flow networks." In Computer Aided Systems Theory — EUROCAST '95, 530–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0034787.

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Hante, Falk M., Günter Leugering, Alexander Martin, Lars Schewe, and Martin Schmidt. "Challenges in Optimal Control Problems for Gas and Fluid Flow in Networks of Pipes and Canals: From Modeling to Industrial Applications." In Industrial Mathematics and Complex Systems, 77–122. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3758-0_5.

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Schmidt, G., R. Maier, and G. Lappus. "Parallel Simulation Techniques as Kernel of a Decision Support System for Control of Large Gas Transmission Networks." In System Fault Diagnostics, Reliability and Related Knowledge-Based Approaches, 199–213. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3931-8_16.

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Devitt, Timothy W. "Flue Gas Desulfurization Systems." In Air Pollution Control Equipment, 355–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85144-5_11.

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Werner, Eric. "Developmental Control Networks." In Encyclopedia of Systems Biology, 567–69. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_570.

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James, Matthew R. "Quantum Networks." In Encyclopedia of Systems and Control, 1–8. London: Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-5102-9_100162-1.

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James, Matthew R. "Quantum Networks." In Encyclopedia of Systems and Control, 1800–1807. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_100162.

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Das, Aveek, Rafael Fierro, and Vijay Kumar. "Control Graphs for Robot Networks." In Cooperative Systems, 55–73. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-3758-5_4.

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Dupuy, F. J. P. "Towards seamless control and management systems." In Intelligent Networks and Intelligence in Networks, 29–39. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-0-387-35323-4_3.

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Liu, Rongjian, Jianquan Lu, and Jie Zhong. "Pinning Control Design for Stabilization of Boolean Networks From Constructed Boolean Control Networks." In Positive Systems, 269–77. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04327-8_21.

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Conference papers on the topic "Control systems/gas networks"

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Lahroodi, Mahmood, and A. A. Mozafari. "Combination of Neural Networks and State Vector Feedback Adaptive Control (SVFAC) Technique to Control the Gas Turbine Combustor." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-15764.

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Neural networks have been applied very successfully in the identification and control of dynamic systems. When designing a control system to ensure the safe and automatic operation of the gas turbine combustor, it is necessary to be able to predict temperature and pressure levels and outlet flow rate throughout the gas turbine combustor to use them for selection of control parameters. This paper describes a nonlinear SVFAC controller scheme for gas turbine combustor. In order to achieve the satisfied control performance, we have to consider the affection of nonlinear factors contained in controller. The neural network controller learns to produce the input selected by the optimization process. The controller is adaptively trained to force the plant output to track a reference output. Proposed Adaptive control system configuration uses two neural networks: a controller network and a model network. The model network is used to predict the effect of controller changes on plant output, which allows the updating of controller parameters. This paper presents the new adaptive SFVC controller using neural networks with compensation for nonlinear plants. The control performance of designed controller is compared with inverse control method and results have shown that the proposed method has good performance for nonlinear plants such as gas turbine combustor.
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Abbatantuono, Gaetano, Silvia Lamonaca, Massimo La Scala, and Ugo Stecchi. "Monitoring and emergency control of natural gas distribution urban networks." In 2016 IEEE Workshop on Environmental, Energy and Structural Monitoring Systems (EESMS). IEEE, 2016. http://dx.doi.org/10.1109/eesms.2016.7504843.

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Lakehal, A., Z. Ghemari, and S. Saad. "Transformer fault diagnosis using dissolved gas analysis technology and Bayesian networks." In 2015 4th International Conference on Systems and Control (ICSC). IEEE, 2015. http://dx.doi.org/10.1109/icosc.2015.7152759.

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Lezzar, Omar C., M. Boutamine, A. Bellel, S. Sahli, Y. Segui, and P. Raynaud. "Quantitative gas identification using HMDSO coated QCM sensors and multilayer neural networks." In 2013 3rd International Conference on Systems and Control (ICSC). IEEE, 2013. http://dx.doi.org/10.1109/icosc.2013.6750857.

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Mantelli, L., V. Zaccaria, K. Kyprianidis, and M. L. Ferrari. "A Degradation Diagnosis Method for Gas Turbine – Fuel Cell Hybrid Systems Using Bayesian Networks." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-16203.

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Abstract During the last decades there has been a rise of awareness regarding the necessity to increase energy systems efficiency and reduce carbon emissions. These goals could be partially achieved through a greater use of gas turbine - solid oxide fuel cell hybrid systems to generate both electric power and heat. However, this kind of systems are known to be delicate, especially due to the fragility of the cell, which could be permanently damaged if its temperature and pressure levels exceed their operative limits. This could be caused by degradation of a component in the system (e.g. the turbomachinery), but also by some sensor fault which leads to a wrong control action. To be considered commercially competitive, these systems must guarantee high reliability and their maintenance costs must be minimized. Thus, it is necessary to integrate these plants with an automated diagnosis system capable to detect degradation levels of the many components (e.g. turbomachinery and fuel cell stack) in order to plan properly the maintenance operations, and also to recognize a sensor fault. This task can be very challenging due to the high complexity of the system and the interactions between its components. Another difficulty is related to the lack of sensors, which is common on commercial power plants, and makes harder the identification of faults in the system. This paper aims to develop and test Bayesian belief network based diagnosis methods, which can be used to predict the most likely degradation levels of turbine, compressor and fuel cell in a hybrid system on the basis of different sensors measurements. The capability of the diagnosis systems to understand if an abnormal measurement is caused by a component degradation or by a sensor fault is also investigated. The data used both to train and to test the networks is generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks to fuel cell - gas turbine hybrid systems is novel, thus the results obtained from this analysis could be a significant starting point to understand their potential. The diagnosis systems developed for this work provide essential information regarding levels of degradation and presence of faults in gas turbine, fuel cell and sensors in a fuel cell – gas turbine hybrid system. The Bayesian belief networks proved to have a good level of accuracy for all the scenarios considered, regarding both steady state and transient operations. This analysis also suggests that in the future a Bayesian belief network could be integrated with the control system to achieve safer and more efficient operations of these plants.
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Gao, B., J. Darling, D. G. Tilley, R. A. Williams, A. Bean, and J. Donahue. "Modelling of a Novel Gas Strut Using Neural Networks." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59119.

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The strut is one of the most important components in a vehicle suspension system. Since it is highly non-linear it is difficult to predict its performance characteristics using a physical mathematical model. However, neural networks have been successfully used as universal ‘black-box’ models in the identification and control of non-linear systems. This approach has been used to model a novel gas strut and the neural network was trained with experimental data obtained in the laboratory from simulated road profiles. The results obtained from the neural network demonstrated good agreement with the experimental results over a wide range of operation conditions. In contrast a linearised mathematical model using least square estimates of system parameters was shown to perform badly due to the highly non-linear nature of the system. A quarter car mathematical model was developed to predict strut behavior. It was shown that the two models produced different predictions of ride performance and it was argued that the neural network was preferable as it included the effects of non-linearities. Although the neural network model does not provide a good understanding of the physical behavior of the strut it is a useful tool for assessing vehicle ride and NVH performance due to its good computational efficiency and accuracy.
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Yu, Huaping, and Mei Guo. "An efficient oil and gas pipeline monitoring systems based on wireless sensor networks." In 2012 International Conference on Information Security and Intelligence Control (ISIC). IEEE, 2012. http://dx.doi.org/10.1109/isic.2012.6449735.

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Arnold, Michele, Rudy R. Negenborn, Goran Andersson, and Bart De Schutter. "Distributed control applied to combined electricity and natural gas infrastructures." In 2008 First International Conference on Infrastructure Systems and Services: Building Networks for a Brighter Future (INFRA 2008). IEEE, 2008. http://dx.doi.org/10.1109/infra.2008.5439653.

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Dymkou, S., and G. Jank. "2-D control theory in gas transport network modeling." In The Fourth International Workshop on Multidimensional Systems - NDS 2005. IEEE, 2005. http://dx.doi.org/10.1109/nds.2005.195361.

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Culley, Dennis. "Transition in Gas Turbine Control System Architecture: Modular, Distributed, and Embedded." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-23226.

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Controls systems are an increasingly important component of turbine-engine system technology. However, as engines become more capable, the control system itself becomes ever more constrained by the inherent environmental conditions of the engine; a relationship forced by the continued reliance on commercial electronics technology. A revolutionary change in the architecture of turbine-engine control systems will change this paradigm and result in fully distributed engine control systems. Initially, the revolution will begin with the physical decoupling of the control law processor from the hostile engine environment using a digital communications network and engine-mounted high temperature electronics requiring little or no thermal control. The vision for the evolution of distributed control capability from this initial implementation to fully distributed and embedded control is described in a roadmap and implementation plan. The development of this plan is the result of discussions with government and industry stakeholders.
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Reports on the topic "Control systems/gas networks"

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Aman Behal, Sunil Kumar, and Goodarz Ahmadi. Intelligent Control via Wireless Sensor Networks for Advanced Coal Combustion Systems. Office of Scientific and Technical Information (OSTI), August 2007. http://dx.doi.org/10.2172/934579.

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Mu, Hong H., Y. P. Kakad, and B. G. Sherlock. Application of Artificial Neural Networks in the Design of Control Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada384438.

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Livengood, C. D., Hann S. Huang, M. H. Mendelsohn, and Jiann M. Wu. Enhancement of mercury control in flue-gas cleanup systems. Office of Scientific and Technical Information (OSTI), July 1996. http://dx.doi.org/10.2172/266762.

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Gölles, Markus, Daniel Muschick, Viktor Unterberger, Paolo Leoni, Ralf-Roman Schmidt, and Gunnar Lennermo. Control of DHC networks and Reduction of the operating temperatures in DH systems. IEA SHC Task 55, January 2021. http://dx.doi.org/10.18777/ieashc-task55-2021-0002.

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Overview on different approaches for the control of the heat distribution networks in case of the integration of large-scale solar thermal systems, and different possibilities for the reduction of the operating temperatures in DH systems.
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KRIPPS, L. J. Control Decisions for Flammable Gas Hazards in Waste Transfer Systems. Office of Scientific and Technical Information (OSTI), June 2000. http://dx.doi.org/10.2172/803940.

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Livengood, C. D., H. S. Huang, M. H. Mendelsohn, and Jiann M. Wu. Development of mercury control enhancements for flue-gas cleanup systems. Office of Scientific and Technical Information (OSTI), August 1995. http://dx.doi.org/10.2172/97170.

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G. Blythe, B. Marsh, S. Miller, C. Richardson, and M. Richardson. ENHANCED CONTROL OF MERCURY BY WET FLUE GAS DESULFURIZATION SYSTEMS. Office of Scientific and Technical Information (OSTI), June 2001. http://dx.doi.org/10.2172/828035.

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Unknown. ENHANCED CONTROL OF MERCURY BY WET FLUE GAS DESULFURIZATION SYSTEMS. Office of Scientific and Technical Information (OSTI), June 2001. http://dx.doi.org/10.2172/794238.

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Murray, William R. Control for Intelligent Tutoring Systems: A Comparison of Blackboard Architectures and Discourse Management Networks. Fort Belvoir, VA: Defense Technical Information Center, September 1988. http://dx.doi.org/10.21236/ada201534.

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Li, Howell, Enrique Saldivar-Carranza, Jijo K. Mathew, Woosung Kim, Jairaj Desai, Timothy Wells, and Darcy M. Bullock. Extraction of Vehicle CAN Bus Data for Roadway Condition Monitoring. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317212.

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Obtaining timely information across the state roadway network is important for monitoring the condition of the roads and operating characteristics of traffic. One of the most significant challenges in winter roadway maintenance is identifying emerging or deteriorating conditions before significant crashes occur. For instance, almost all modern vehicles have accelerometers, anti-lock brake (ABS) and traction control systems. This data can be read from the Controller Area Network (CAN) of the vehicle, and combined with GPS coordinates and cellular connectivity, can provide valuable on-the-ground sampling of vehicle dynamics at the onset of a storm. We are rapidly entering an era where this vehicle data can provide an agency with opportunities to more effectively manage their systems than traditional procedures that rely on fixed infrastructure sensors and telephone reports. This data could also reduce the density of roadway weather information systems (RWIS), similar to how probe vehicle data has reduced the need for micro loop or side fire sensors for collecting traffic speeds.
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