基于GRBF神经网络的多级煤气压缩系统建模  

Modeling for Multi-stage Gas Compression System Based on GRBF Neural Network

作　　者：褚菲[1] 董世建[1] 王福利[2] 王小刚[1] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110819 [2]东北大学流程工业综合自动化国家重点实验室,辽宁沈阳110819

出　　处：《东北大学学报（自然科学版）》2012年第7期913-916,共4页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(61074074);国家重点基础研究发展计划项目(2009CB320601)

摘　　要：以某钢厂燃气、蒸汽联合循环发电机组煤气压缩系统为背景,建立以煤水分离器、离心式压缩机和冷却器为核心的多级煤气压缩系统机理模型.采用自适应遗传算法辨识机理模型中某些难以确定的重要参数.由于多级煤气压缩系统的影响因素较多,机理模型预测结果不精确.利用基于广义径向基函数的神经网络补偿机理模型的误差,建立GRBF神经网络和机理模型并联的多级煤气系统的混合模型.试验结果表明相比于机理模型,混合模型有更高的预测精度.Based on the gas system of gas-steam combined cycle power plant from a steelworks, a mechanistic model was established for the multi-stage gas compression system, which essentially consisted of scrubbers, centrifugal compressors and coolers. Adaptive genetic algorithm was applied to estimating the important parameters of the mechanistic model, which cannot be confirmed using the mechanistic model. Since there are many factors having effect on the performance of the multi-stage gas compression system, the mechanistic model may yield inaccurate results. Thus GRBF neural network was employed to correct the error of the mechanistic model. The hybrid model was established by connecting the mechanistic model and GRBF neural network in parallel. Models were applied to the practical gas system, and the results demonstrated that compared with the mechanistic model, the hybrid model has higher accuracy.

关 键 词：煤气系统 机理建模 混合建模 自适应遗传算法 神经网络 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]