关键词: 气液两相流, 干度, 粒子群算法, 最小二乘支持向量机

Abstract: To improve the standard orifice plate design a better outflow characteristics of tapered orifice. Using tapered orifice differential pressure fluctuation characteristics of the empirical formula and Least Square Support Vector Machine （mixed kernel） which is improved by particle swarm optimization establish the soft sensing model of two-phase flow dryness. The mixed kernel of this model is constituted of radial basis function and polynomial kernel，it can be both the local fitting ability and global fitting ability of Least Square Support Vector Machine. What’s more，by optimizing parameters of the Least Square Support Vector Machine，this model can reduce the dependence on the initial point and the number of samples. The experimental results show that such a prediction method is feasible than not optimized least squares support vector machine model and the optimized mononuclear least squares vector machine model generalization ability. So，it can be widely used in engineering practice.

Key words: gas-liquid two phase flow, dryness, particle swarm option, least squares support vector machine