催化裂化过程稳态优化控制研究  

The Study of Stable State Optimal Control of FCC Process

作　　者：冯明琴[1] 张靖[2] 孙政顺[3] 

机构地区：[1]攀枝花大学电气工程系,攀枝花617000 [2]攀枝花大学信息中心,攀枝花617000 [3]清华大学自动化系,北京100084

出　　处：《自动化学报》2003年第6期1015-1022,共8页Acta Automatica Sinica

基　　金：国家自然科学基金 (6 0 174 0 2 2 )资助~~

摘　　要：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FrankFCCU(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.

关 键 词：石油炼制过程 催化裂化过程 稳态优化控制 模糊神经网络 炼油厂 

分 类 号：TE624[石油与天然气工程—油气加工工程]