Simulation Study On Process Optimization And Advanced Control Technology Of Atmospheric And Vacuum Distillation Unit

Atmospheric and vacuum distillation unit is the first program of the petro-chemical industry, the control of which has the characteristics of capacity lag and modeling complexity, as well as high-grade requirements of parameter adjustment. Vacuum tower and atmospheric tower, as the core equipments of CDU(crude oil distillation unit), are towers with multi-line, which have high quality requirements and strong correlations in the system. Many parameters can not be used as the operation parameters. Currently,petrochemical enterprises have mostly imported integrated control system with high reliability in early stage, but the application of control idea is relatively backward.Although the domestic of advanced control technique has been put into experiments,compared with the developed countries there is still a considerable gap, it is difficult to adapt to improve recommendation production requirements.This paper describes the application of ASPEN PLUS software in the process simulation of a common device. By means of sensitivity analysis, the relationship between process parameters is analyzed. The main parameters of the optimization scheme are proposed. The control parametersfluctuates in reasonable range near balance point of high product extraction rate and oil quality, the proposed control scheme to control more clear requirements. Through the optimization analysis of the steam quantity and the heating temperature to optimize, both the production efficiency and quality could be achieved at the same time as well as the purpose of energy conservation.The problem of the traditional method due to the internal influence of parameters is analyzed. By application of ASPEN PLUS Dynamics to establish the dynamic model of the atmospheric and vacuum device, the internal connections between the parameters are explored. The model is studied by taking advantage of calculation and control system simulation of Matlab andthe model parameters and physical property database of ASPEN software.By application of system identification, the RBF dynamic neural network is used to establish the mathematical model of the control object. The implementation method offuzzy neural network predictive control in the multi-input and multi-output system with large time delay is discussed. Using Matlab and Simulink as well as ASPEN PLUS, the control system simulation was established in this experiment. To discuss the advantages and disadvantages of the traditional PID control, fuzzy neural PID control and fuzzy neural predictive control in the application of the constant pressure reduction device. In this paper, the analysis and design of the control system of the constant pressure and pressure reduction process is of high practical value and theoretical significance.