催化裂化过程稳态优化控制研究

催化裂化装置是一个高度非线性、时变、长时延、强耦合、分布参数和不确定性的复杂 系统.在研究其过程机理的基础上,定义了一种模糊神经网络用以建模,用自相关函数检验法检 验模型的正确性,再用改进的Frank-Wolfe算法进行稳态优化计算,并以一炼油厂催化裂化装 置为对象进行试验,研究其辨识、建模和稳态优化控制.这种模糊神经网络具有隐层数多、隐层 结点数多、泛化能力和逼近能力强、收敛速度快的优点,更突出的特点还在于可由输出端对输入 求导,为稳态优化计算提供了极大方便,它与改进的Frank-Wolfe算法相结合用于解决非线性 复杂生产过程的建模和稳态优化控制问题是可行的.

The Study of Stable State Optimal Control of FCC Process

FCCU(fluid catalysis and cracking unit) is a highly non-linear, time variable, long time delay, intensive coupling, parameter distributed, indefinite and complex system. A fuzzy neural network based on the process mechanism for the modeling has been established. The autocorrelation function checking method to test the correctness of the model, and the advanced Frank-Wolfe algorithm are used to compute stable state optimization. An oil refinery works FCCU is also used to test and study the system identification, modeling and stable state optimal control by the network. The fuzzy neural network (FNN) has such advantages as multiple hidden layers, multiple neurons in each hidden layer, strong generalization and approximation ability, quick convergence rate, etc. Moreover we can make differential calculation to the input variables by output variables, which makes optimization calculation convenient. The fuzzy neural network, working with the advanced Frank-Wolfe algorithm, can be used in system modeling and stable state optimal control of non-linear complex production process.