Modeling And Control Of Vacuum Tower Based On Neural Network

Crude distillation is most important process in refineries and large petrifaction enterprises. The atmospheric and vacuum towers whose production level directly affect the oil using-rate and economic benefit of a enterprise are the important equipments to implement the distillation. The vacuum tower who plays an important role in crude distillation process is studied.On the basis of comprehension of process flow and principle of crude oil distillation, various factors which affect viscosity and flash point about No.3 side line of vacuum tower are analyzed and then the software instruments of viscosity and flash point about No.3 side line of vacuum tower are established by using DRNN neural network based on genetic algorithm. From simulation results, the software instruments obtain good accuracy. They can replace quality analysis instruments.The control method of No.3 side line temperature system of vacuum tower is only introduced and the model of No.3 side line temperature system of vacuum tower is established by using DRNN neural network based on genetic algorithm because No.3 side line is most important in all side lines of vacuum tower. Control temperature indirectly equals to control quality because of the corresponding relation between temperature and quality. In distillation system, control of side line's temperature is attained by controlling the extraction's flux from side line. After analyzing the technics-characteristic and real production's scheme of the vacuum tower, neural network adaptive control is applied in No.3 side line temperature control system of vacuum tower. From simulation result, it receives good effect and reaches the requirement of control.