The Control System Design And Optimization Research Of28000Nm3/h Producing Oxygen Equipments In Tianjin Iron And Steel Company

Oxygen, nitrogen and argon play an important role in iron and steel production. Oxygen, nitrogen and argon are usually produced by air separated unit(ASU).With the steps of Tiansteel’s construction and development, the ASU’s are becoming gradually one big system, which brings forth to Tiansteel Oxygen-Plant more and more critical system requirements on ASUs control.In this paper, for the days of steel28000Nm3/h oxygen plant, designed based on Japan’s Yokogawa CS3000DCS routine control of air separation equipment, control systems; air separation plant under both the technological characteristics and control requirements of the key equipment for air separation equipment, the logical control. On this basis, the paper also from the perspective of energy saving equipment, air separation equipment to achieve high efficiency, energy conservation as the goal, advanced technology, stable and reliable, convenient operation and maintenance cost for the principle of the oxygen equipment of the automatic variable load problem.Model predictive control (MPC) is a model based computer control. Since its presentation, it has been widely used in process industry especially in refinery and petrochemical processes. One of the major reasons for this success is that the strategy can easily be used to solve multivariable input and output constrains. Recently, along with the scale of commercial enterprises expanding continuously, the plant become more and more complex which leads to many constrains. However, process limitation and constraints many perturb the plant form their desired working point, and the disturbance entering the plant or new input information from the operator may change the location of the optimal steady state. So how to deal with the optimization and control of the complex system in order to make system running in a more economic state is the main problem of this research. In this thesis, we studied the problem from three aspects which are as follows.MPC calculation is separated into steady-state and dynamic optimization. Considering the effects of measured disturbance, the steady-state objective is recalculated at each sampling time. The difference between the model prediction and the measured output at the current time is introduced into the model to incorporate feedback and the velocity constraints in steady-state target calculation ensure the steady-state objective is compatible with the velocity constraints in dynamic MPC optimization. Finally, this control strategy has been successfully applied to the control of the Shell heavy oil fraction ators benchmark problem.