在工业许多领域上,对于颗粒粒度的快速、准确的在线测量的需求越来越紧迫.这对反演算法的质量如鲁棒性、运行效率、重复性及精度等提出了更高的要求.本文提出将量子粒子群算法用于非独立模式下颗粒粒径的反演,将提出的算法与基本粒子群算法、模拟退火算法进行对比.在仿真方面,在不同随机噪声下,对均匀球形颗粒进行了模拟仿真.当随机噪声为5%时,反演误差在10%以内.在实验方面,通过搭建基于CCD为探测器的激光粒度检测系统,对国家标准颗粒进行了反演计算,仿真及实验结果表明量子粒子群算法具有全局性、收敛速度快、鲁棒性好等优点,且反演时间约为1s,适合快速、高精度的在线颗粒粒度测量.

At present,the need of the fast and accurate online measurement of particle size is more and more urgent in many industrial fields. It is need to put forward higher requirements about the quality of the inversion algorithm,such as robustness,efficiency,repeatability and accuracy,etc. Quantum-behaved Particle Swarm Optimization(QPSO) is proposed to retrieve particle size distribution(PSD)in the dependent mode. Comparison is made between the proposed algorithm and the conventional algorithm,Simulated Annealing algorithm. The Simulation is performed to verify the effectiveness of QPSO,in which the spheroidal particle of unimodal distribution is retrieved at different levels of random noise. Inversion errors are within 10% when 5% random noise is added. Experiment is also performed to verify the practicability of QPSO,in which the laser particle size detection system based on CCD is built. Standardized polystyrene microsphere is tested and calculated. Both simulation and experiment results indicate that QPSO has good global superiority,speed and good robustness. Moreover,the inversion time is about 1 second,which is suitable for fast,accurate online particle size measurement.