The conversion rate of the crude methanol is the primary indicator of methanol production

but also the key factor to influence the economic target.Based on the previous work

two random learning factor particle swarm optimizations with chaos are proposed.In the algorithms

the ergodicity of chaos is introduced respectively at early and late stage of evolution.The simulation of test functions evaluates the effectiveness of RLFPSOC.Finally

the proposed RLFPSOC

which is employed to optimize the parameter of neural network

is integrated with neural network to measure the methanol conversion rate.The results indicate that RLFPSOC-based neural network model can predict the methanol conversion rate well

which further verifies the global convergence of RLFPSOC.