基于随机学习因子混沌PSO算法的甲醇合成转化率测量

Measurement of methanol conversion rate based on random learning factor PSO with chaos

粗甲醇转化率不仅是粗甲醇的主要技术指标,也是直接影响粗甲醇经济指标的重要因素。在之前工作的基础上,提出了两类随机学习因子混沌粒子群优化算法(RLFPSOC)。两种新算法分别从种群进化初期和后期两个方面引入混沌遍历性的特点,有效提高了算法的全局寻优能力。典型测试函数的仿真实验验证RLFPSOC算法的有效性。最后,将提出的RLFPSOC算法用于神经网络参数的优化,并建立甲醇合成塔转化率预测模型。实验结果表明,基于RLFPSOC的神经网络模型能够较好地预测甲醇合成转化率,并进一步验证了RLFPSOC算法的全局收敛性能。

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.