Research On Energy Efficiency Evaluation Optimization And Monitoring System Of Atmospheric And Vacuum Distillation Unit

Petroleum refining is one of the most critical energy-consuming industries in China.With the increasing of demand,the scale of production is expanding and also consume more energy.The energy conservation and consumption reduction are therefore of great significant for petroleum refining.Atmospheric and vacuum distillation unit accounts for the largest proportion of energy consumption in oil refining process,and is responsible for the output of key products.The energy efficiency level of petroleum refining is directly related to energy utilization efficiency and economic benefits of enterprises.Therefore,it is of great significance to study energy efficiency evaluation and optimization for atmospheric and vacuum distillation unit.This paper proposes a soft-sensing modeling and optimization control strategy.On this basis,a mobile system for the energy efficiency monitoring and evaluation of the oil refining unit is developed.The effectiveness of presented method is verified by applying to the national 863 project "Energy Efficiency Monitoring,Evaluation and Optimization Control Technology and System for Petrochemical Industry".The main contributions are as follows:(1)There are many indexes used to evaluate the energy efficiency level.By analyzing the process flow of atmospheric and vacuum distillation,and considering the factors such as energy consumption and side-line product output,the comprehensiveness and rationality of different energy efficiency evaluation indexes are compared.The energy efficiency level of atmospheric and vacuum distillation unit is evaluated according to the definitive index--unit comprehensive energy consumption output.(2)The changes of production load and operating conditions during atmospheric and vacuum distillation process lead to the energy consumption and output fluctuation.The existing energy efficiency evaluation method based on single working condition is difficult to accurately reflect the true energy efficiency level of atmospheric and vacuum distillation unit.In addition,the statistics of side-line product output in the production process need a lot of manpower and material resources,which often cause large errors due to human factors.Therefore,this paper proposes an energy efficiency evaluation method of soft-sensing modeling based on unsupervised learning condition division.A prediction model for unit comprehensive energy consumption output is established by using PSO optimization LSSVM algorithm.The actual production data show that the soft-sensing modeling based on working condition division can effectively improve the accuracy of energy efficiency assessment.Given that the level of energy utilization directly determines the production efficiency of enterprises,an optimization model aiming at maximum output of unit comprehensive energy consumption is built for low energy efficiency conditions.The APSO algorithm is adopted to optimize the temperature and vacuum degree of the vacuum tower top.Results confirm that the energy utilization rate of atmospheric and vacuum distillation units is effectively improved,which achieves the purpose of saving energy and reducing consumption.(3)According to the requirement of a refinery for a mobile energy efficiency monitoring and evaluation system,this paper develops a mobile energy efficiency monitoring and evaluation system for refinery based on Android system.It expands the scope of supervision work and realizes seven functions such as parameter monitoring,energy efficiency evaluation and system management.Meanwhile,the decision support for the staff is also provided.The developed system provides the technical support and the convenience for the intelligent energy efficiency monitoring and evaluation of oil refining units,which contributes to improving the management efficiency.