Optimal Operation Of Pipeline Based On Data-driven

With the continuous development of China’s industry,the demand for crude oil and its products is increasing.As the main way of oil transportation,the energy consumption of oil pipelines is also rising.During the actual dispatching process,the dispatchers are unable to give the optimal scheduling plan under the current situation in the face of the combination of different oil pumps and the corresponding pipeline hydraulic constraints.Therefore,to improve the operation status of oil pipelines,it is urgent to build up the efficiency of oil transportation.Production optimization model of the oil pipeline to reduce oil transportation cost.With the gradual establishment of the monitoring and data acquisition system of the oil pipeline network,a large amount of historical data has been accumulated.How to make full use of the historical data to realize the optimal operation of the oil pipeline is of great significance.The main work of this paper is as follows:1.Analyze the historical flow and pressure distribution of each station of the Ri-Yi line,find the abnormal point in the pipeline,assume that there is a buffer tank in the pipeline according to the abnormal point,and establish a data-driven hydraulic model.2.Because the data-driven hydraulic model is based on assumptions,and its results can only be used as a reference value,a new hydraulic prediction model is established by using BP neural network and the hybrid optimization algorithm combining the fireworks algorithm and gray wolf algorithm.3.According to the actual needs of pipe network dispatchers and operators,the objective function with the lowest total energy consumption under the current oil pump operation is established.For the nonlinear mixed integer programming problem,the branch and bound algorithm and sequential quadratic programming algorithm are used to solve it.4.Sort out all historical data and divide them into operation data and operation data.According to different oil products and different operations,divide the historical data into independent oil transportation conditions,extract data characteristics and operation information,and use a hybrid optimization algorithm for clustering operation to obtain the historical optimal operation under similar conditions.Through the historical data test and actual operation of the Ri-Yi line,the optimized operation scheme can save 5%-12% energy consumption for the oil pipeline,and the optimization effect is very obvious.