基于支持向量回归的重油催化裂化产物建模及优化

MODELING AND OPTIMIZATION FOR CATALYTIC CRACKING PRODUCTS OF HEAVY OIL BASED ON SUPPORT VECTOR REGRESSION

催化裂化反应的产物分布与反应原料组成及反应条件具有复杂的函数关系,以三种重油多个条件下的催化裂化实验结果为训练样本,利用支持向量回归方法建立汽油、柴油产物的产率模型。对于催化裂化回炼油,利用模型的泛化能力对不同操作条件下的汽油、柴油产率进行模拟仿真。以轻质油(汽油、柴油)产率最大为优化目标,利用粒子群算法寻找回炼油反应的最优操作条件。结果表明:模型对各反应条件下的实验结果有良好的拟合效果,模拟仿真的三维图可以直观显示各个反应条件对汽油、柴油产率的影响。优化得到的回炼油最佳反应条件为温度530℃,剂油质量比7.5,空速8h-1。在最佳反应条件下,轻质油产率模拟值为42.3%,实验值为41.8%,相对误差为1.20%。

The product distributions of catalytic cracking have a complex functional correlation with feedstock compositions and reaction conditions. The experimental results of three heavy oil samples having various compositions and testing under different reaction conditions were normalized as training data, by using support vector regression （SVR） method, a yield model for gasoline and diesel products of heavy oil catalytic cracking was established. For catalytic cracking of recycle stock, gasoline and diesel yields under different operation conditions were calculated by the generalization ability of SVR model. The optimal operation conditions for maximizing light oil （gasoline and diesel） yield were found by particle swarm optimization （PSO） algorithm. Calculated results show that this model has good fitting effect with experimental data under various reaction conditions. The simulated three-dimensional graphs can effectively illustrate the relationships between product yields and reaction conditions. The optimal reaction conditions obtained by PSO algorithm are as follows：reaction temperature of 530℃, catalyst to oil mass ratio of 7.5 and space velocity of 8 h-1. Under such conditions, the simulated and experimental light oil yields are 42.3% and 41.8% , respectively, the relative error is 1.20%