摘要
为提高原油含水率宽量程在线测量的精度,采用一套基于多传感器的油水两相流实验室模拟系统对影响其测量的多个敏感参量进行测定,提出基于粗糙集预处理器、支持向量机分类器和遗传神经网络预测器的原油含水率智能组合测量模型.实验结果表明,该模型在很大程度上解决了油水乳化液模态、温度、矿化度等因素的交叉影响及传感器自身非线性的校正问题,可通过模糊推理与自学习实现油水混合模态辨识,并根据工况的变化调整测量模型参数,有效地提高了原油含水率宽量程在线智能测量的精度.
In order to improve the on-line measuring accuracy and widen the measuring range of water content of crude oil, a simulated multi-sensor measurement system of oil/water two-phase flow is adopted to detect the para- meters influencing the measurement, and an intelligent compound model for measuring the water content is estab- lished by combining the rough set preprocessor, the support vector machine classifier and the genetic neural network predictor. Experimental results show that the proposed model effectively eliminates the cross interference of oil/wa- ter emulsion modal, temperature and salinity content, overcomes the nonlinearity of sensor itself, realizes the modal identification of oil-water mixture via fuzzy reasoning and self-learning, and adjusts the model parameters by changing working conditions adaptively. Thus, the accuracy of on-line intelligent measurement of water content of crude oil is effectively improved in a wide measuring range.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第5期73-78,共6页
Journal of South China University of Technology(Natural Science Edition)
基金
华南理工大学优秀博士学位论文创新基金资助项目(200903023)
关键词
原油
油水两相流
粗糙集理论
模态辨识
遗传算法
神经网络
支持向量机
crude oil
oil/water two-phase flow
rough set theory
modal identification
genetic algorithm
neural network
support vector machine
