S Zorb精制汽油辛烷值优化模型及工业应用

DEVELOPMENT AND COMMERCIAL APPLICATION OF AN OPTIMIZATION MODEL OF OCTANE NUMBER FOR REFINED GASOLINE FROM S Zorb UNIT

针对S Zorb精制汽油研究法辛烷值(RON)损失较大的问题,以某石化企业S Zorb装置近3年的运行数据为基础,采用最大互信息系数(MIC)和Pearson相关系数并结合BP神经网络,从包括原料油性质、吸附剂性质、产品性质和操作变量在内的273个变量中筛选出22个建模变量,构建了结构为21-14-1的汽油RON预测模型,并进行验证.结果表明:建立的预测模型具有较好的拟合优度和泛化能力,其对测试集的平均绝对误差(MAE)和决定系数(R2)分别为0.1163、0.9601.在此基础上,针对具体原料性质,采用遗传算法(GA)优化操作变量,发现该模型通过优化能有效降低汽油RON损失;工业试验验证结果表明,通过模型优化操作变量可使汽油RON损失降低25%.

Aiming at the large loss problem of research octane number(RON)of refined gasoline from S Zorb unit,based on the historical data from a S Zorb unit in the past 3 years,the maximal information coefficient(MIC)and Pearson correlation coefficient were used in combination with BP neural network,22 modeling variables were selected from 273 variables including feed oil properties,adsorbent properties,product properties and operation variables,and a RON prediction model with the structure of 21-14-1 was established and validated.The results show that the prediction model has good goodness of fit and generalization ability,and its average absolute error and determination coefficient(R 2)of the model on the test set are 0.1163 and 0.9601,respectively.On this basis,genetic algorithm(GA)was used to optimize the best operation variables to improve the product RON.It was found that the model could effectively reduce RON loss of gasoline through optimization,and the RON loss of gasoline could be reduced by 25%through the optimization model.