基于Kalman预测的在线油品调合

ON-LINE OIL BLENDING BASED ON KALMAN PREDICTION

汽油调合过程是大多数炼油厂重要的加工过程之一 ,对汽油调合配方进行在线优化能够大大提高炼油厂的竞争能力。笔者针对组分油质量的波动 ,采用Kalman算法预测组分油的质量 ,并在此基础上 ,通过对整个调合时域的在线滚动优化得到油品调合配方。应用实例证明了该算法是可行的 ,并且能够获得较高的利润。

Gasoline blending is a key process in most petroleum refineries, the online optimized gasoline blend recipes provide the potential to gain a competitive benefit. The quality of feedstock is predicted by Kalman prediction algorithm with respect to the fluctuation in its quality, and the blend recipes are obtained through online rolling optimization over the entire time horizon based on the predicted quality data. The given example shows the feasibility of the proposed method, which makes the process maintain a higher level of economic benefit.