非独立模式算法下粒径分布反演及分类的研究

在光全散射法颗粒粒径测量中，提出一种非独立模式算法下粒径分布反演及分类的方法。对被测颗粒系分别按照不同的粒径分布函数同时进行反演，并依据反演误差大小判断被测颗粒系符合哪种分布函数。仿真实验结果表明，在非独立模式下，完全可以利用已知的不同分布函数的反演误差作为分类依据，从而更准确地确定被测颗粒系的粒径分布。采用的遗传反演算法能够在3个可见光波长下得到较准确的粒径分布，反演结果稳定可靠，最大限度地减少了多个波长的使用，从而对光源有更大的选择余地。对透射消光测量结果加入5%随机噪声时，单峰分布颗粒系的反演误差小于5%，多峰分布颗粒系的反演误差小于10%。整个算法运行时间小于2 s。该方法具有原理简单，计算速度快等优点，能够满足颗粒粒径在线测量的要求。

Study of Inversion and Classification of Particle Size Distribution under Dependent Model Algorithm

For the total light scattering particle sizing technique,an inversion and classification method was proposed with the dependent model algorithm.The measured particle system was inversed simultaneously by different particle distribution functions whose mathematic model was known in advance,and then classified according to the inversion errors.The simulation experiments illustrated that it is feasible to use the inversion errors to determine the particle size distribution.The particle size distribution function was obtained accurately at only three wavelengths in the visible light range with the genetic algorithm,and the inversion results were steady and reliable,which decreased the number of multi wavelengths to the greatest extent and increased the selectivity of light source.The single peak distribution inversion error was less than 5% and the bimodal distribution inversion error was less than 10% when 5% stochastic noise was put in the transmission extinction measurement values at two wavelengths.The running time of this method was less than 2 s.The method has advantages of simplicity,rapidity,and suitability for on-line particle size measurement.