Study on the Economic Insulation Thickness of the Buried Hot Oil Pipelines Based on Environment Factors

It is important to determine the insulation thickness in the design of the buried hot oil pipelines.The economic thickness of the insulation layer not only meets the needs of the project but also maximizes the investment and environmental benefits.However,as a significant evaluation,the environmental factors haven’t been considered in the previous study.Considering this factor,the mathematical model of economic insulation thickness of the buried hot oil pipelines is built in this paper,which is solved by the golden section method while considering the costs of investment,operation,environment,the time value of money.The environmental cost is determined according to the pollutant discharge calculated through relating heat loss of the pipelines to the air emission while building the model.The results primarily showed that the most saving fuel is natural gas,followed by LPG,fuel oil,and coal.The fuel consumption for identical insulation thickness is in the order:coal,fuel oil,LPG,and natural gas.When taking the environmental costs into account,the thicker the economic insulation layer is,the higher cost it will be.Meanwhile,the more pollutant discharge,the thicker the economic insulation layer will be.

Standard friction prediction model of long-distance hot oil pipelines

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.