【作者】 彭佩星；

【导师】 王保良； 黄志尧；

【作者基本信息】 浙江大学 ， 检测技术与自动化装置， 2007， 硕士

【摘要】 两相流广泛存在于动力、化工、石油、冶金和核能等领域，两相流参数的准确测量对于生产过程的计量、控制以及环保等具有重要的意义。但是由于两相流流动特性复杂，使得参数检测的难度很大。所以，两相流参数的检测一直是两相流研究中的前沿课题。空隙率是气液两相流的一个非常重要的参数。本文基于12电极电容层析成像系统(ECT)，对油气两相流的空隙率测量进行了研究。本文的主要工作如下：1．将独立分量分析(ICA)和支持向量机(SVM)引入到油气两相流空隙率测量中。用ICA方法对从ECT电容传感器获得的66个电容值进行特征提取，既降低了数据的维数，减少了后续的计算量，又提高了模型的泛化能力。最小二乘支持向量机(LS-SVM)作为一种新型的支持向量机，具有运算简单的优点。为了克服标准LS-SVM鲁棒性差和稀疏性缺失的缺点，本文又引入两种改进型LS-SVM—加权LS-SVM和稀疏LS-SVM。2．引入进化策略(ES)选择LS-SVM参数。针对LS-SVM参数选择困难的问题，本文提出了基于ES的LS-SVM参数选择方法。将LS-SVM参数选择问题看作优化问题，建立优化目标函数，利用具有全局搜索能力的ES寻找最优LS-SVM参数。该方法对SVM的参数选择具有普遍的适用性。3．基于ECT系统，提出了一种油气两相流空隙率测量的新方法。实际测量时，以ECT电容传感器获得的66个电容测量值作为输入，利用空隙率测量模型计算空隙率。建模过程中，首先采用ICA方法，对66个电容测量值进行特征提取。然后以特征参数作为输入，空隙率作为输出，用LS-SVM建立回归函数，并运用ES寻找最优LS-SVM参数。静态实验结果验证了本文方法的有效性。更多还原

【Abstract】 Two-phase flow widely exists in industries such as power, chemical engineering, petroleum, metallurgy and nuclear energy. The parameters measurement of two-phase flow is very important for the metering, control and environment protection in production process. However, the complexity of two-phase flow results in many difficulties in the parameters measurement. Therefore, the parameters measurement of two-phase flow is always a frontier subject in research on two-phase flow. Voidage is one of the most important parameters of gas-liquid two-phase flow. Based on 12-electrode Electrical Capacitance Tomography (ECT), the voidage measurement of oil-gas two-phase flow is studied.The main works of the dissertation are listed as follows:1. Independent Component Analysis (ICA) and Support Vector Machine (SVM) are introduced to the voidage measurement of oil-gas two-phase flow. Features are extracted from the 66 measured capacitances obtained from ECT sensors by ICA. It not only reduces the data dimension and computation cost, but also improves the generalization performance of the model. Least Squares Support Vector Machine (LS-SVM) is a new kind of SVM with the advantage of simple computation. Two kinds of improved LS-SVMs—Weighted Least Squares Support Vector Machine and Sparse Least Squares Support Vector Machine are introduced, for the standard LS-SVM is bad in robustness and has no sparseness.2. Evolution Strategy (ES) is introduced to solve the difficult problem of parameters selection in LS-SVM, and a parameters selection method based on ES is proposed. The issue of parameters selection in LS-SVM is regarded as an optimization problem, objective function is established, and ES with global searching capability is employed to search the optimal parameters in LS-SVM. This method is universal for the parameters selection in SVM.3. Based on ECT, a new method is proposed for voidage measurement of oil-gas two-phase flow. In the measurement process, with the 66 measured capacitances obtained from ECT sensors being the inputs, the voidage is computed by using the voidage model. In the modeling stage, features are extracted from the 66 measured capacitances by ICA firstly, and then LS-SVM is used to establish the regression function, in which the features are the inputs and the corresponding voidage value is the output. Also, ES is employed to search the optimal parameters in LS-SVM. Static experimental results prove that the proposed method is effective.更多还原