Abstract:

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.