摘要
提出一种基于K均值聚类的多群竞争粒子群优化算法(MSCPSO),该算法避免陷入局部最优,提高了算法的全局搜索能力。同时利用MSCPSO训练RBF神经网络并建立裂解产物的在线预测模型,研究一种集成MSCPSO-RBFNN过程建模的裂解深度智能优化控制方法。该方法以实现乙烯和丙烯收率之和最大化为目标函数,把满足优化目标的裂解深度作为深度控制器的输入,并与裂解炉出口温度先进控制系统集成,实现裂解深度的平稳控制。实际应用效果表明,提高了乙烯和丙烯的收率,裂解深度控制更加稳定,该方法具有良好的适应性、稳定性。
A new multi-swarm competitive particle swarm optimization（MSCPSO）algorithm is proposed,which avoids to immersing the local optimization and improves the global searching performance.Then radical basis functions neural network （RBFNN） is trained by proposed MSCPSO to model cracking productions of furnace for online predictions.At the same time,the intelligent optimal control method for cracking depth is studied integrated by MSCPSO-RBFNN.The optimal function is maximized the sum of ethylene and propylene yields.And then cracking depth which is satisfied to the optimal function is input the depth＇s controller,which is linked into the advanced process control system of coil out temperature （COT）,so the depth＇s controlling is realized optimally.The applications are showed that the yields of ethylene and propylene are increased,and the depth＇s control is more stable than before.The proposed optimal control method has good adaptability,stability and reliability.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2010年第8期1942-1948,共7页
CIESC Journal
基金
国家高技术研究发展计划项目(2007AA04Z170)
中央高校基本科研业务费项目(JD0906)~~
关键词
RBF神经网络
多群竞争粒子群
裂解炉
优化控制
radical basis function neural network
MSCPSO
cracking furnace
optimal control
