重质船用燃料油黏度预测模型研究
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摘要
研究了现有黏度预测模型应用于重质船用燃料油黏度预测的可行性,筛选几种常见的黏度物理模型,进行试验数据对比和最优模型选取,基于重质船用燃料油数据库对Cragoe模型进行修正,并结合掺稀降黏试验数据分析混合机制对预测模型相对误差的影响。结果表明,针对目前市场上常用的重质船用燃料油调合组分,采用Cragoe黏度模型进行预测误差较小。这是由于Cragoe黏度模型的预测不受组分油黏度比的限制,在重质船用燃料油中的适用性最好。采用所提出的修正模型,可进一步降低对重质船用燃料油黏度预测的误差。分析多组分调合的结果显示,若组分中的黏度呈梯度分布时可降低预测误差。另外,渣油与稀组分油(简称稀油)调合时,沥青质的络合效应在一定程度上会影响模型的预测准确性。
The feasibility to predict the viscosity of heavy marine fuel oil using the existing viscosity prediction models was studied.Several common viscosity physical models were compared and selected the best one based on the analysis of experimental data.The Cragoe model was modified based on the heavy marine fuel oil database and the viscosity reduction data by dilution,the impact of the blending mechanism on the relative error of the prediction model was analyzed.The results showed that for the heavy fuel oil blending components commonly used in the current market,the Cragoe viscosity model has minor prediction errors due to the fact that the Cragoe viscosity prediction model is not limited by the viscosity ratio of the component oils,and is the best model suited for heavy marine fuel oils.The proposed correction model can further reduce the error in predicting the viscosity of heavy marine fuel oil.The analysis of multi-component blending results showed that if the profile of the viscosities distribution of the components is in a gradient form,the prediction error can be reduced.In addition,when the residual oil and dilute components are blended,the complexing effect of asphaltenes affects the prediction accuracy of the model to some extent.
The feasibility to predict the viscosity of heavy marine fuel oil using the existing viscosity prediction models was studied.Several common viscosity physical models were compared and selected the best one based on the analysis of experimental data.The Cragoe model was modified based on the heavy marine fuel oil database and the viscosity reduction data by dilution,the impact of the blending mechanism on the relative error of the prediction model was analyzed.The results showed that for the heavy fuel oil blending components commonly used in the current market,the Cragoe viscosity model has minor prediction errors due to the fact that the Cragoe viscosity prediction model is not limited by the viscosity ratio of the component oils,and is the best model suited for heavy marine fuel oils.The proposed correction model can further reduce the error in predicting the viscosity of heavy marine fuel oil.The analysis of multi-component blending results showed that if the profile of the viscosities distribution of the components is in a gradient form,the prediction error can be reduced.In addition,when the residual oil and dilute components are blended,the complexing effect of asphaltenes affects the prediction accuracy of the model to some extent.
引文
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[2]王丹.浅谈低硫船用燃料油[J].中国远洋航务,2016(2):52-54
[3]杨海宏,原英.汽油调合生产中主要指标的计算[J].化工技术与开发,2013,42(2):56-59
[4]李遵照,徐可忠,薛倩,等.残渣型船用燃料油连续调合工艺研究[J].石油炼制与化工,2018,49(5):92-96
[5]张锐,周家庆,陈夕松,等.遗传算法在非线性原油调合中的应用研究[J].炼油与化工,2013,24(5):16-18
[6]郑瑜,赵会军,赵书华.顺序输送汽油与柴油混油回掺试验[J].油气储运,2008,27(5):32-34
[7]冯玉海,沈本贤,高晋生,等.用神经网络模型预测催化裂化原料油结构族组成的研究[J].石油炼制与化工,2001,32(4):59-61
[8]孟肖丽,刘保相.求解非线性方程的蛛网-迭代算法[J].计算机工程与应用,2015,51(15):28-31
[9]刘名瑞,肖文涛,张雨,等.最小二乘支持向量机在重质燃料油调合中的应用[J].石油化工自动化,2017,53(1):33-36
[10]童刚,陈丽君,冷健,等.液体黏度模型的建立与实现[J].青岛科技大学学报(自然科学版),2007,28(6):539-541
[11]郑云萍,李勋,汪玉春,等.混合原油黏温数学模型研究进展[J].油气储运,2010,29(9):683-686
[12]宋延贵.基础油黏度调合计算程序讨论[J].胜炼科技,1999(2):37-43
[13]孟庆萍.混合原油黏度计算模型[J].油气储运,2007,26(10):22-24
[14]赵瑞玉,展学成,张超,等.特超稠油黏度的影响因素研究[J].油田化学,2016,33(2):319-324
[15]王婷,阳绪斌,张衡,等.稠油掺稀后混油黏度计算方法的研究[J].管道技术与设备,2012(1):18-19
[16]李闯文.混合原油流变性及其配伍规律的研究[D].北京:中国石油大学(北京),1992
[17]明亮,敬加强,代科敏,等.塔河稠油掺稀黏度预测模型[J].油气储运,2013,32(3):263-266
[18]蒋学文.新疆混合原油凝点、黏度计算模型研究及其应用[D].北京:中国石油大学(北京),2005
[19]张俊,曹学文,张楠,等.油品二元掺混黏度预测模型评价研究[J].石油化工高等学校学报,2013,26(6):65-70
[20]杨筱蘅.油管道设计与管理[M].东营:中国石油大学出版社,2006:146-147
[21]李遵照,刘名瑞,程亚松,等.非石油基油品对渣油掺稀降黏效果及掺稀模型研究[J].石油炼制与化工,2017,48(1):38-42