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Intelligent nonlinear model predictive control of gas pipeline networks
Transactions of the Institute of Measurement and Control, 2019In 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 ...
Hamid Reza Moetamedzadeh +3 more
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International Journal of Advanced Robotic Systems, 2013 Since the control system of the welding gun pose in whole-position welding is complicated and nonlinear, an intelligent control system of welding gun pose for a pipeline welding robot based on an improved radial basis function neural network (IRBFNN) and
Jingwen Tian, Meijuan Gao
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Intelligent Monitoring And Control System of A Pipeline Network
Technical Meeting / Petroleum Conference of The South Saskatchewan Section, 1995Abstract A knowledge-based or expert system has been developed for intelligent monitoring and control of industrial pipeline network operations. The expert system would perform the supervisory and decision-support tasks based on the expertise and operating procedures that are documented in the maintainable knowledge
W. Kritpiphat +5 more
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Wavelet Neural Network Based Intelligent System for Oil Pipeline Defect Characterization
2010 3rd International Conference on Emerging Trends in Engineering and Technology, 2010Wavelet neural network is a new kind of network which fuses advantages of wavelet transform and neural computing. It utilizes the good localize character of the wavelet transformation and combines the self learning function of the neural network. It has the ability of strong adaptive learning and function approach. Wavelet neural network has the simple
Mamta Tikaria, Shikha Nema
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Intelligent Pigging of the Ekofisk Subsea Pipeline Network
Offshore Technology Conference, 1991ABSTRACT In 1988, Phillips Petroleum Company Norway (PPCON) as the operator of the greater Ekofisk field, embarked upon a four year plan to complete an inspection of the entire Ekofisk pipeline system utilizing a 2nd generation intelligent pig.
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Neural Computing and Applications, 2021
Inefficient scheduling of a pipeline system may lead to severe degradation and substantial economic losses. Earlier studies mostly focussed on corrosion and statistical analysis. This study presents a novel approach for the prediction of life conditions and the classification of metal loss (ML) faults for a group of five pipeline sections of a pipeline
Nagoor Basha Shaik +4 more
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Inefficient scheduling of a pipeline system may lead to severe degradation and substantial economic losses. Earlier studies mostly focussed on corrosion and statistical analysis. This study presents a novel approach for the prediction of life conditions and the classification of metal loss (ML) faults for a group of five pipeline sections of a pipeline
Nagoor Basha Shaik +4 more
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An intelligent optimization method for oil-gas gathering and transportation pipeline network layout
2016 Chinese Control and Decision Conference (CCDC), 2016Gathering and transferring pipeline network of oil-gas system exerts remarkable impact on construction cost for whole oilfield engineering. In this paper, hierarchical optimization strategy is used to optimize the Multilevel Star-Tree Style of oil-gas gathering and transportation network.
Qiang Liu, Li Mao, Fangfang Li
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Data Analytics Using Two-Stage Intelligent Model Pipelining for Virtual Network Functions
2021 IEEE 10th International Conference on Cloud Networking (CloudNet), 2021Takaya Miyazawa +2 more
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INTELLIGENT X-RAY IMAGE ANALYSIS SYSTEM BASED ON A CASCADED NEURAL NETWORK PIPELINE
SOFT MEASUREMENTS AND COMPUTINGThis paper presents the development of an intelligent X-ray image analysis system based on a cascaded pipeline of three convolutional neural networks. At the first stage, a ResNet50 model classifies the image type (chest or musculoskeletal) with 99.97% accuracy and AUC-ROC of 0.9999.
Vyacheslav M. Sludnikov +2 more
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