精馏塔的机理-神经网络混合建模  

Modeling of Distillation Column Based on Hybrid Neural Network and Prior Knowledge

作　　者：何小阳[1] 李健[1,2] 闵力[1] 韩宇星[1] 段军霞[1] 

机构地区：[1]广西大学电气工程学院,广西南宁530004 [2]南宁铁路局科学技术研究所,广西柳州545007

出　　处：《控制工程》2009年第2期211-213,共3页Control Engineering of China

基　　金：广西区研究生教育创新计划研究生科研创新基金资助项目(2006105930811M305)

摘　　要：酒精精馏过程是一个复杂的化工过程,动态响应缓慢,内在机理复杂,参数间相互关联。为了解决精馏塔机理模型精度低和神经网络模型外推能力差的缺点,同时也为了精馏塔的先进控制提供一种可靠的先进模型,针对试验室酒精精馏塔,充分发挥机理模型和神经网络模型的特点,建立一种基于机理模型和神经网络补偿模型的酒精精馏塔的混合模型。最后对混合模型进行了仿真试验,仿真结果显示有很好的性能,精馏塔的精馏精度和精馏效率都得到了很大的提高。而且下一步正准备以此模型为基础,设计精馏塔的先进控制算法。The process of ethanol distillation is a complex chemical process with slowness of dynamic process, complexity of inner mechanism and coupling parameters. For distillation column of ethanol, a hybrid model based on prior knowledge model and neural network model is built. The accuracy of prior knowledge model and range of neural network model, as well as the dependable advanced model for advanced control is improved. The simulation results show that this model has enhanced the performance. The advanced control algorithm of distillation column based on the hybrid model is the future work.

关 键 词：精馏 机理 神经网络 混合模型 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]