中文摘要：

实时优化技术（RTO）在汽油调合过程中的应用开始得到推广。实时优化技术要去能够对组分油和成品油的属性进行测量,并以此为基础在线优化和更新配方。为了降低实时优化技术对组分油属性测量的依赖,本文采用了无迹卡尔曼滤波器根据成品油属性测量值对组分油属性进行在线更新,并以此为依据进行在线优化。由于采用了无迹变换,无迹卡尔曼滤波器,能够很好地处理辛烷值与雷德蒸汽压在调合过程中表现出的非线性特征。在文中,无迹卡尔曼滤波器和相应的实时优化技术在1个汽油调合问题上进行了测试。结果表明通过无迹卡尔曼滤波器估计出的组分油属性精度可达到0.004%以内,以此为基础的实时优化算法,可以成功完成汽油调合,从而使得实时优化算法摆脱对组分油属性的硬件测量。

英文摘要：

The real-time optimization（RTO） technique has been quite popular recently with the gasoline blending process.However,the implementation of the RTO requires the measurements of the components and the product,on the basis of which the optimization is performed.In order to get rid of the dependency on the component measurement,the Unscented Kalman Filter（UKF） is used to estimate the component parameters with the measurement for the product.Using the Unscented Transformation,the UKF is able to handle the nonlinearity in the critical properties,such as Octane number and Reid vapor pressure.Next,the UKF and the corresponding RTO are tested on a gasoline blending benchmark problem.The results show that the accuracy of the parameter estimation done with the UKF is up to 0.004%,while the RTO method based on the estimated parameter is able to carry out the gasoline blending successfully.Therefore,the hardware measurement for the component is possible to get rid of in the RTO application.