An integrated dynamic model of natural gas pipeline networks is developed in this paper.Components for gas supply,e.g.,pipelines,junctions,compressor stations,LNG terminals,regulation stations and gas storage faciliti...An integrated dynamic model of natural gas pipeline networks is developed in this paper.Components for gas supply,e.g.,pipelines,junctions,compressor stations,LNG terminals,regulation stations and gas storage facilities are included in the model.These components are firstly modeled with respect to their properties and functions and,then,integrated at the system level by Graph Theory.The model can be used for simulating the system response in different scenarios of operation,and evaluate the consequences from the perspectives of supply security and resilience.A case study is considered to evaluate the accuracy of the model by benchmarking its results against those from literature and the software Pipeline Studio.Finally,the model is applied on a relatively complex natural gas pipeline network and the results are analyzed in detail from the supply security and resilience points of view.The main contributions of the paper are:firstly,a novel model of a complex gas pipeline network is proposed as a dynamic state-space model at system level;a method,based on the dynamic model,is proposed to analyze the security and resilience of supply from a system perspective.展开更多
Water supply pipelines are the lifelines of a city. When pipelines burst, the burst site is difficult to locate by traditional methods such as manual tools or only by watching. In this paper, the burst site was iden...Water supply pipelines are the lifelines of a city. When pipelines burst, the burst site is difficult to locate by traditional methods such as manual tools or only by watching. In this paper, the burst site was identified using back-propagation (BP) artificial neural networks (ANN). The study is based on an indoor urban water supply model experiment. The key to appling BP ANN is to optimize the ANN's topological structure and learning parameters. This paper presents the optimizing method for a 3-layer BP neural network's topological structure and its learning parameters-learning ratio and the momentum factor. The indoor water supply pipeline model experimental results show that BP ANNs can be used to locate the burst point in urban water supply systems. The topological structure and learning parameters were optimized using the experimental results.展开更多
基金supported by National Natural Science Foundation of China[grant number 51904316]provided by China University of Petroleum,Beijing[grant number2462021YJRC013,2462020YXZZ045]
文摘An integrated dynamic model of natural gas pipeline networks is developed in this paper.Components for gas supply,e.g.,pipelines,junctions,compressor stations,LNG terminals,regulation stations and gas storage facilities are included in the model.These components are firstly modeled with respect to their properties and functions and,then,integrated at the system level by Graph Theory.The model can be used for simulating the system response in different scenarios of operation,and evaluate the consequences from the perspectives of supply security and resilience.A case study is considered to evaluate the accuracy of the model by benchmarking its results against those from literature and the software Pipeline Studio.Finally,the model is applied on a relatively complex natural gas pipeline network and the results are analyzed in detail from the supply security and resilience points of view.The main contributions of the paper are:firstly,a novel model of a complex gas pipeline network is proposed as a dynamic state-space model at system level;a method,based on the dynamic model,is proposed to analyze the security and resilience of supply from a system perspective.
文摘Water supply pipelines are the lifelines of a city. When pipelines burst, the burst site is difficult to locate by traditional methods such as manual tools or only by watching. In this paper, the burst site was identified using back-propagation (BP) artificial neural networks (ANN). The study is based on an indoor urban water supply model experiment. The key to appling BP ANN is to optimize the ANN's topological structure and learning parameters. This paper presents the optimizing method for a 3-layer BP neural network's topological structure and its learning parameters-learning ratio and the momentum factor. The indoor water supply pipeline model experimental results show that BP ANNs can be used to locate the burst point in urban water supply systems. The topological structure and learning parameters were optimized using the experimental results.
