Study On The Characteristics Of Batch Transportation Of Multi-product Pipeline

To guarantee the pipeline operation optimally, safely and efficiently, it is meaningful to understand the characteristics and rules of batch transportation of multi-product pipeline completely, deeply and exactly. The fundamental theory, numerical computation and dynamic simulation of batch transportation of multi-product pipeline have been studied.The adaptive grid method was adopted in the hydraulic transients, the one-dimensional contamination and two-dimensional contamination.The formula have been presented in the paper, taking into account the varying of propagation velocity of water hammer with the moving of the contamination interface and the other factors. It is much easier for the adaptive grid method to control the size and the density of the mesh and the generation of the grid can be finished quickly and automatically according to the characteristics of solutions. More grid points are redistributed in the large gratitude solution regions in response to numerical solutions and thus the larger the weight function on the grids,the smaller the corresponding grid distance is. The movement of the grids can be written into the governing equations so that the new positions can be worked out at every step allowing the grids to vary continuously and smoothly with the field changing, and thus the forwards position of gratitude solution region of the pressure field, velocity field and concentration field can be captured easily. The result shows that the technique has excellent qualities in improving accuracy of numerical solutions and reducing CPU time. The thermal transient equation, in which the temperature increment due to friction is taken into consideration, is derived on the bases of the continuity equation, momentum conservation equation and energy conservation equation. By combining the thermal transient equation, the hydraulic transient equation and the contamination equation, the coupling thermal-hydraulic-contamination model is offered, in which he corresponding adaptive grid method is also developed and the advantages of characteristics method and adaptive grid technique are combined.The prediction model of contamination viscosity has been established by using the formidable non-linearity mapping ability of artificial neural network. This model can predict contamination viscosity in each given point of the non-linearity influence factor. Its error is smaller than other way and it can completely meet the actual project need.The new contamination mathematical model of batch transportation is established based on the Navier-Stokes equation, the mass transportation equation and the k ?εturbulent flow theory, and the numerical simulation of contamination in pipeline under special operating mode has been investigated with the help of PHOENICS. In the interface of two different oils, its result can reflect clearly the phenomenon of convection and mass diffusion. The influences of transportation order in the erect pipeline and shutdown and blind pipeline and right angle pipe to contamination are analyzed prominently. The results provide a foundation for further study of the influence of tubular specimens and valves to the contamination as the interface flow through the intermediate stations.Acceptable concentration level of mixtures prediction models have been established in the paper originally by using BP neural networks. These models can be used to control doping percentage as well as determine cutting point. Firstly, the performance indicators of pure oil quality are determined according to the experimental data, and quality index forecast models are established with the help of BP neural networks. Secondly, Acceptable concentration level of mixtures prediction models are established on the basis of quality prediction models by changing the input and output of the quality forecast neural network models. Based on the experimental data, three prediction models have been established in this paper, which are flash point forecasting model respect to the incorporation of diesel and gasoline, dry point forecasting model respect to the incorporation of diesel and gasoline, solidifying point prediction model respect to the corporation of low-grade diesel oil and high-grade diesel. Finally, neural model prediction values have been compared with measured values and values predicted by the other method. The result shows that the neural network prediction model is more accurate than other method.The models are simple and convenient to be used to determine cutting time and dissolving ratio.In accordance with the above-mentioned studies, the calculation programs have been developed in the paper.