We developed a predictive model for the pipeline friction in the 520-730 m^3/h transmission range using the multi-layerperceptron-back-propagation(MLP-BP)method and analyzing the unit friction data after the pigging o...We developed a predictive model for the pipeline friction in the 520-730 m^3/h transmission range using the multi-layerperceptron-back-propagation(MLP-BP)method and analyzing the unit friction data after the pigging of a hot oil pipeline.In view of the shortcomings of the MLP-BP model,two optimization methods,the genetic algorithm(GA)and mind evolutionary algorithm(MEA),were used to optimize the MLP-BP model.The research results were applied to the standard friction prediction of three sections of a hot oil pipeline.After the GA and MEA optimizations,the average errors of the three sections were 0.0041 MPa for the GA and 0.0012 MPa for the MEA,and the mean-square errors were 0.083 and 0.067,respectively.The MEA-BP model prediction results were characterized by high precision and small dispersion.The MEABP prediction model was applied to the analysis of the wax formation 60 and 90 days after pigging.The analysis results showed that the model can effectively guide pipe pigging and optimization.There was little sample data for the individual transmission and oil temperature steps because the model was based on actual production data modeling and analysis,which may have affected the accuracy and adaptability of the model.展开更多
Artificial neural network is a new approach to pattern recognition and classification. The model of multilayer perceptron （MLP） and back-propagation （BP） is used to train the algorithm in the artificial neural net...Artificial neural network is a new approach to pattern recognition and classification. The model of multilayer perceptron （MLP） and back-propagation （BP） is used to train the algorithm in the artificial neural network. An improved fast algorithm of the BP network was presented, which adopts a singular value decomposition （SVD） and a generalized inverse matrix. It not only increases the speed of network learning but also achieves a satisfying precision. The simulation and experiment results show the effect of improvement of BP algorithm on the classification of the surface defects of steel plate.展开更多
基金supported by National Natural Science Foundation of China(51904327,51774311)Natural Science Foundation of Shandong Province of China(ZR2017MEE022)+1 种基金China Postdoctoral Science Foundation(2019TQ0354,2019M662468)Qingdao postdoctoral researchers applied research project.
文摘We developed a predictive model for the pipeline friction in the 520-730 m^3/h transmission range using the multi-layerperceptron-back-propagation(MLP-BP)method and analyzing the unit friction data after the pigging of a hot oil pipeline.In view of the shortcomings of the MLP-BP model,two optimization methods,the genetic algorithm(GA)and mind evolutionary algorithm(MEA),were used to optimize the MLP-BP model.The research results were applied to the standard friction prediction of three sections of a hot oil pipeline.After the GA and MEA optimizations,the average errors of the three sections were 0.0041 MPa for the GA and 0.0012 MPa for the MEA,and the mean-square errors were 0.083 and 0.067,respectively.The MEA-BP model prediction results were characterized by high precision and small dispersion.The MEABP prediction model was applied to the analysis of the wax formation 60 and 90 days after pigging.The analysis results showed that the model can effectively guide pipe pigging and optimization.There was little sample data for the individual transmission and oil temperature steps because the model was based on actual production data modeling and analysis,which may have affected the accuracy and adaptability of the model.
基金Item Sponsored by National Natural Science Foundation of China (60277029)
文摘Artificial neural network is a new approach to pattern recognition and classification. The model of multilayer perceptron （MLP） and back-propagation （BP） is used to train the algorithm in the artificial neural network. An improved fast algorithm of the BP network was presented, which adopts a singular value decomposition （SVD） and a generalized inverse matrix. It not only increases the speed of network learning but also achieves a satisfying precision. The simulation and experiment results show the effect of improvement of BP algorithm on the classification of the surface defects of steel plate.
