CN107250787B - 石脑油料流的相对评价方法 - Google Patents
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Abstract
提供了通过对原油样品进行分析,之后进行模块或步骤来确定石脑油料流的相对价值的系统和方法,以估算总液体收率,估算原始产物收率,使原始产物收率归一化、基于预定值确定每个级分的价值,和计算石脑油料流的总价值。
Description
相关申请
本申请要求2015年1月5日提交的美国临时专利申请第62/099,788号的权益,在此通过引用引入其公开。
发明领域
本发明涉及用于评价原油及其级分的样品的方法和工艺。
背景技术
世界范围内具有超过200种生产和交易的原油。原油是数千种不同的烃的非常复杂的混合物。取决于来源,原油包含各种份额的直链和支链的链烷烃、环烷烃和环烷族、芳族和多核芳族烃。原油的特性一定程度上支配可以由其制造的产品的特性和它们对于特殊应用的适合性。
世界范围内的供应和需求、区域炼化能力和配置以及原油组成是决定原油价值的关键因素。第一因素纯粹取决于市场并且不可能从原油质量预测。因此,原油价值由区域原油市场和差异如运费、质量调节、精炼成本和有竞争力的价格决定。
在典型的石油精炼中,首先在常压下蒸馏原油。气体将上升至蒸馏塔顶部,然后是低沸点液体,包括石脑油、煤油和柴油。石脑油不是最终产品,但是经受另外的处理步骤,如加氢处理和催化重整以生产重整油。然后将重整油送至汽油池用于调和。
Colin Birch的论文“Achieving Maximum Crude Oil Values Depends onAccurate Evaluation,”Oil&Gas Journal,第100卷,第2期(2002年1月14日)描述了许多用于获得来自特定来源的具体原油的价值的客观计算的评价方法。这些方法的几种的总结如下。
整体性质法:该方法将实际粗品值与整体性质相关联。将API比重和硫含量广泛用于相关性并且也可以使用其它整体性质如粘度和倾点。该方法在所需要的测试的量方面相对简单。然而,该方法在对大范围的原油估价时可能不可靠。例如,使用该方法,一些环烷族原油可能被估价得相对较高,但是该结果可能并不反映原油的实际市场价格。
精炼价值法:使用每个原油料流的精炼厂收率和工艺运行成本,典型地使用线性程序(LP)或其它模型评价原油和对其估价。精炼厂模型需要详细的物理性质信息和通过详细的原油分析测定的蒸馏馏分。工艺收率和运行成本与适当的产品价值一起使用,以计算原油之间的精炼价值差异。精炼价值法模拟了精炼者用于选择原油的工艺。详细的原油品质信息和对给定精炼厂运行精炼厂模型以产生收率的需求使得该方法比整体性质法更复杂。如果输入料流质量明显改变,则必须产生一组新的收率。在仅涉及几个具有合理稳定质量的原油的相对简单的系统中,精炼价值法通常为精炼者提供最准确的价值分配。
蒸馏收率法:这是精炼价值法的简化版本,其替代使用线性程序或其它模型将仅使用每个级分的收率。这些来自蒸馏每种原油的产品收率与产品价值一起用于计算每种原油的相对价值。在许多情况下,将蒸馏馏分的一些物理性质用于价值调节体系。来自每种原油的质量信息是相对简单的并且包括蒸馏收率和蒸馏馏分性质。蒸馏收率法比整体性质法更复杂,但是没有精炼价值法那么复杂。因为其在计算中使用产品价值,所以原油价值数据的可靠性并不是问题。然而估价的产品如石脑油并非满足限定规格的最终产品。所以存在一些关于蒸馏馏分的关键性质的价值调节的不确定性。
可以评价石脑油料流的若干性质,包括API比重,硫、氮、碳和氢含量和研究辛烷值。研究辛烷值是燃料在火花点火式发动机中防止爆震的能力的量度。在标准单气缸中测量;可变压缩比发动机与主要参考燃料比较,美国标准测试材料测试ASTM D-2699和ASTMD-2700分别描述了研究和马达法辛烷值。在温和条件下,发动机测量研究辛烷值(RON),而在苛刻条件下发动机测量马达法辛烷值(MON)。当法律要求在分配泵上张贴辛烷值时,使用抗爆震指数(AKI)。这是RON和MON的数学平均,(R+M)/2。其近似于道路辛烷值,这是“一般”汽车如何响应燃料的量度。其为石脑油/汽油料流的最关键的性质。
使用标准测试发动机和运行条件测定火花点火式发动机燃料的RON以比较其爆震特性(被定义为爆震强度(KI))与已知辛烷值的主要参考燃料(PRF)共混物(包含异辛烷和正庚烷)的爆震特性。例如,辛烷值87的汽油具有与87%异辛烷和13%正庚烷的混合物相同的辛烷值。调节压缩比(CR)和燃料空气比以产生样品燃料的标准KI,如通过比电子爆震计(specific electronic detonation meter)仪器系统测量。标准KI指南表将发动机CR与该特定方法的辛烷值水平相关。调节样品燃料和每种主要参考燃料共混物的燃料空气比以使每种燃料的KI最大。虽然汽油将会具有85以上的RON,但是石脑油将会具有低于60的RON。
使用标准测试发动机和运行条件测定火花点火式发动机燃料的MON以比较其爆震特性与已知辛烷值的PRF共混物的爆震特性。调节CR和燃料空气比以产生样品燃料的标准KI,如通过比电子爆震计仪器系统测量。标准KI指南表将发动机CR与该特定方法的辛烷值水平相关。调节样品燃料和每种PRF共混物的燃料空气比以使每种燃料的KI最大。
为了常规地测定瓦斯油或石脑油级分的这些性质,不得不从原油中蒸馏这些级分,然后使用费力的、昂贵的和耗时的各种分析方法进行测量/鉴定。
因此,存在对可以客观地应用来比较来自不同来源的石脑油级分而确定来自不同来源的原油级分的价值的改进的系统和方法的需求。
发明简述
提供了系统和方法用于将价值指定给石脑油料流。将所述料流重整至目标研究辛烷值,并且获得多个轻组分级分和重整油级分;分析重整油级分以获得指示内容物环烷烃和芳族物质(在某些实施方案中另外有链烷烃和异链烷烃,并且在进一步的实施方案中另外有链烷烃、异链烷烃、烯烃和异烯烃)的数据。将指示性数据用于指定进料质量。将指定的进料质量用于指定总液体收率和原始产品收率(其为单独指定的值)。因此可以基于指示性数据指定总石脑油估价。当将所述方法应用至来源于来自各种来源的原油的石脑油料流时,各自指定的值提供用于原油的相对评价的客观基础。
可以利用本发明的系统和方法来对来源于石油的石脑油级分估价,所述级分具有在–11.5至235℃范围内,在某些实施方案中36–180℃的正常沸点。石脑油级分在组成方面变化并且因此如上文所讨论辛烷值是发动机爆震特性的关键指标性质。组成和性质方面的区别使得石脑油级分的评价变得困难。在某些实施方案中,对直馏石脑油样品实践本文中公开的对比评价方法。
在本文中的方法的某些实施方案中,在催化重整工艺中将石脑油级分转变成轻质组分和目标研究辛烷值的重整油。将重整油进料至气相色谱中以获得指示内容物环烷烃和芳族物质(在某些实施方案中另外有链烷烃和异链烷烃,并且在进一步的实施方案中另外有链烷烃、异链烷烃、烯烃和异烯烃)的数据,这例如被称为PIONA分析。将算法应用至环烷烃和芳族物质的总百分比,从而确定石脑油料流的价值。基于在由使用者预定的给定时间和位置独立确定的值指定每个组分的价值。
在本文中的方法和系统的进一步的实施方案中,将石脑油级分中的环烷烃和芳香物质的含量指定为原油样品的密度和来源于原油样品分析的指数的函数。所述分析选自傅里叶变换近红外光谱法、傅里叶变换红外光谱法、傅里叶变换离子回旋质谱法、飞行时间质谱法、紫外可见光谱法、激光诱导紫外光谱法、核磁光谱法、荧光光谱法、拉曼光谱法、气相色谱法、液相色谱法、超临界流体色谱法、热重分析法或差示扫描量热法的任一者。
可以将本文中的方法和系统应用至来源于由不同储层或区域获得的不同原油的样品,以提供相同RON的相对价值,从而为精炼者提供在一个或多个销售它们的产品的市场中进行对比的基础。因此,本文的方法和系统的目的在于促进两种或多种料流的对比,从而基于其组成级分的目前价值确定哪个料流具有较高的价值。这样的数据例如可用于为精炼者提供决定应当首先加工哪个料流的基础。
本发明的另一目的在于提供评价来源于来自各种来源的原油的特定石脑油料流,以建立基于特定价值的经济对比的客观基础的方法。
在本说明书中,术语“重整器单元”、“重整器”和“重整单元”互换地使用,并且是指用于催化重整工艺的常规设备。
附图简述
当参考附图进行考虑时,本发明的另外的有利之处和特征将会由以下本发明的详述变得显而易见,其中:
图1图解地说明了石脑油的加氢处理和重整以及所得料流的色谱分析;
图2是进行另外的步骤以使用本文中的系统和方法建立石脑油料流的价值的工艺流程图;和
图3是用于实施根据一个实施方案的本发明的系统的组件的框图。
发明详述
提供了系统和方法用于确定和指定烃样品的收率和估价。将在某些实施方案中来源于原油样品的石脑油级分的重整油组分收率指定为原油样品的PIONA分析数据的函数。相关性还提供了关于石脑油组分和总收率的信息而不分馏/蒸馏(原油分析)并且使得生产者、精炼者和营销者能够建立油品质基准,并且因此对所述油估价而不进行通常广泛和耗时的原油分析。
所述系统和方法可应用于来源于原油、沥青、重油、页岩油和来源于精炼厂工艺单元(包括加氢处理、加氢操作、流化催化裂化、焦化,和减粘裂化或煤液化)的石脑油料流的评价。
图1示出了加氢处理和重整工艺100。将石脑油料流110进料至加氢处理装置115以生产经加氢处理的石脑油料流120,将其通至重整器125。从重整器125回收轻质组分料流氢气(“H2”)130、甲烷(“C1”)135、乙烷(“C2”)140、丙烷(“C3”)145和丁烷(“C4”)150和重整油(“C5+”)155。操作条件使得通过目标研究辛烷值表征重整油。因此,虽然产物收率分布将会对于所生产的每种石脑油进料而不同,但是如通过研究辛烷值测量的汽油的质量将会是均匀的。
所选择的预定的研究辛烷值对于来自重整单元的产品而言可以在80至100范围内,在某些实施方案中在95至100范围内,并且在进一步的实施方案中在95至98范围内,这是汽油RON规格。要注意的是,所述收率典型地随着目标辛烷值方面的增加而降低。
在某些实施方案中,加氢处理装置115在有效生产具有低于0.5ppmw的硫水平和低于0.5ppmw的氮水平的加氢处理的石脑油料流120的条件下和一种或多种催化剂存在下运行。应当将最大允许硫和氮污染物含量水平保持在为了有效使用重整器单元催化剂而建立的预定极限内。重整器催化剂由贵金属如铂和钯制成并且对杂质如硫和氮非常敏感。在操作期间存在较高水平的硫和氮将会毒化催化剂。如本领域普通技术人员已知那样,硫的主要来源是不足的加氢处理,加氢处理汽提塔扰动和硫化氢与烯烃在高温和低压的重组。氮的主要来源是不足的加氢处理、进料中裂解的石脑油和不恰当地使用抑制剂。因为重整单元催化剂可能对杂质相当敏感,所以在某些实施方案中,在加氢处理工艺中将硫和氮水平降低,以提供满足必要规格的重整器进料流。
将分离的轻质气体130、135、140、140、150通入一个或多个精炼厂气体分析仪160,例如在某些实施方案中的根据ASTM D1945分析气体的气相色谱中。
将液体重整油155进料至PIONA分析仪165。在某些实施方案中,PIONA分析仪165为根据ASTM D6839分析液体的气相色谱。在PIONA分析中,将重整油的级分通过碳数和正链烷烃、异链烷烃、环烷烃和芳族物质列表,显示每个碳数的百分比体积。在某些实施方案中,重整油来源于来自原油蒸馏的直馏石脑油,而不是来源于来自裂化反应的中间精炼厂石脑油,并且因此不存在或存在很少的烯烃。
图2显示了根据本文的一个实施方案的方法中的步骤的工艺流程图,其在原油的分析210或重整油的分析之后发生,以获得指示存在于经受估价的石脑油样品中的环烷烃和芳族物质的数据。
在某些实施方案中,分析步骤210为重整油155的PIONA分析。如从PIONA分析得出,将变量N用于表示环烷烃的总体积百分比并且将变量A用于表示芳族物质的总体积百分比。
在进一步的实施方案中,分析步骤210为选自以下的任一者的原油样品的分析:傅里叶变换近红外光谱法、傅里叶变换红外光谱法、傅里叶变换离子回旋质谱法、飞行时间质谱法、近红外光谱法、紫外可见光谱法、激光诱导紫外光谱法、核磁光谱法、荧光光谱法、拉曼光谱法、气相色谱法、液相色谱法、超临界流体色谱法、热重分析法或差示扫描量热法。将这些分析之一用于为原油样品指定指数。将指定的指数用于指定经受估价的石脑油级分的芳族物质含量和环烷烃含量。
在一个实施方案中,其中将傅里叶变换近红外光谱法用于分析步骤210,根据方程式(1)指定指数(近红外吸收指数NIRA):
其中:
吸光度=指示在预定波数范围内(例如在4,000cm-1至12,821cm-1范围内)检测到的峰的原油溶液的吸光度值。
使用该指数和原油样品的密度,计算并且指定芳族物质和环烷烃含量:
链烷烃含量=KPa+KPb*DEN+KPc*DEN2+KPd*DEN3+KPe*I+KPf*I2+KPg*I3+KPh*DEN*I (1b)
芳族物质含量=KAa+KAb*DEN+KAc*DEN2+KAd*DEN3+KAe*I+KAf*I2+KAg*I3+KAh*DEN*I (1c)
环烷烃含量=100-链烷烃含量–芳族物质含量 (1d)
其中KPa至KPh和KAa至KAh为常数,
DEN=在15℃的原油密度,和
I=指数,例如在方程式(1)中指定的FT NIR指数,或由原油样品的其它分析指定的其它指数。
在步骤220中,进料质量计算为:
进料质量=N+2A (2)
可以开发用于确定总重整器收率的方程式,在某些实施方案中使用N+2A浓度比总收率的线性回归。
在步骤230中,将总液体产物收率Y估算为进料质量和恒定RON数(即目标数)Rt的函数:
Y=KYa*(N+2A)2+KYb*(N+2A)+KYc*Rt2+KYd*Rt+KYe (3)
其中KYa至KYe为常数。
然后可以指定H2、C1、C2、C3、C4和C5+的各个收率以及重整油收率。在某些实施方案中,使用总重整油收率比在目标辛烷值的单独收率的线性回归计算这些指定的值。在步骤240中,基于总液体产物收率变量将甲烷、乙烷、丙烷、丁烷和汽油的估算的原始产物收率线性建模,同时基于总液体产物收率变量和恒定RON数Rt对氢气线性建模。
原始甲烷收率,C1r=KC1ra*Y+KC1rb (4)
原始乙烷收率,C2r=KC2ra*Y+KC2rb (5)
原始丙烷收率,C3r=KC3ra*Y+KC3rb (6)
原始丁烷收率,C4r=KC4ra*Y+KC4rb (7)
原始汽油收率,Gr=KGra*Y+KGrb (8)
原始氢气收率,Hr=KHra*Y+KHrb*Rt+KHrc (9)
其中KC1ra至KC4rb、KGra、KGrb和KHRA至KHrc为常数。
估算的总原始收率为对这些组分估计的原始收率之和:
总原始收率,Tr=C1r+C2r+C3r+C4r+Gr+Hr (10)
在步骤250,如下将通过将各个原始收率除以总原始收率而将收率归一化至100:
归一化的甲烷收率,C1n=(C1r*100)/Tr (11)
归一化的乙烷收率,C2n=(C2r*100)/Tr (12)
归一化的丙烷收率,C3n=(C3r*100)/Tr (13)
归一化的丁烷收率,C4n=(C4r*100)/Tr (14)归一化的汽油收率,Gn=(Gr*100)/Tr (15)
归一化的氢气收率,Hn=(Hr*100)/Tr (16)
在步骤260中,将每个级分的估算收率乘以其单位价值,以为每个级分指定价值:
甲烷的价值,C1v=(C1n/100)*C1P,其中C1P为甲烷的价值 (17)
乙烷的价值,C2v=(C2n/100)*C2P,其中C2P为乙烷的价值 (18)
丙烷的价值,C3v=(C3n/100)*C3P,其中C3P为丙烷的价值 (19)
丁烷的价值,C4v=(C4n/100)*C4P,其中C4P为丁烷的价值 (20)
汽油的价值,Gv=(Gn/100)*GP,其中GP为汽油的价值 (21)
氢气的价值,Hv=(Hn/100)*HP,其中HP为氢气的价值 (22)
在步骤270中,然后通过加和各个料流的计算价值来估算石脑油料流的总价值:
石脑油单位价值($/吨),NPT=C1v+C2v+C3v+C4v+Gv+Hv
(23)
也可以通过将如$/吨表示的价值除以密度并且乘以桶油的升数(159升/桶)而将石脑油料流的基质重新规定为$/桶:
NPB=(NPT/密度)*159升/桶 (24)
当要评价两个石脑油料流时,可以容易地将该过程用于为每次对比指定价值。
可以实施的可运行计算模块,例如以进行全部或部分方程式(1a)-(24)的计算机系统300的示意性框图示于图3中。计算机系统300包括处理器310,如中央处理单元、输入/输出界面320和支持电路330。在某些实施方案中,当计算机系统300需要直接人机界面时,还提供显示器340和输入装置350如键盘、鼠标或指示器(pointer)。示出显示器340、输入装置350、处理器310、输入/输出界面320和支持电路330连接至总线360,所述总线还连接至存储器单元370。存储器370包括程序储存存储器380和数据储存存储器390。应注意虽然将计算机300描述为具有直接人机界面组件显示器340和输入装置350,但是也可以在界面320上实现模块的编程和数据的传入和传出,例如当计算机300连接至网络和在另一相连的计算机上发生编程和显示器运行时,或经由可拆卸输入装置,如本领域中对于接口连接可编程逻辑控制器已知那样。
程序储存存储器380和数据储存存储器390可以各自包括易失性(RAM)和非易失性(ROM)存储器单元并且还可以包括硬盘和备用存储容量,并且程序储存存储器380和数据储存存储器390二者可以在单个存储器装置或单独地在多个存储器装置中实现。程序储存存储器380储存软件程序模块和相关数据,包括一个或多个计算模块如进料质量计算模块(例如相当于上文关于图2描述的步骤220)、收率计算模块(例如相当于上文关于图2描述的步骤230、240和250,包括用于单独或各自的模块,其用于总重整器收率;总液体收率;H2、C1、C2、C3、C4和C5+以及重整油的各个收率;总原始收率;和归一化的收率)、和估价模块(例如相当于上文关于图2描述的步骤260和270)。数据储存存储器390储存由本系统的一个或多个模块所使用和/或产生的数据,包括由本系统的一个或多个模块使用的PIONA分析数据或其部分以及由本系统的一个或多个模块产生的经计算的进料质量、收率和估价。
将根据本文的系统和方法的计算的和指定的结果显示、音频输出、打印和/或储存至存储器,如本文中所描述而使用。
应理解的是,计算机系统300可以为任意普通或特殊目的的计算机,如个人计算机、小型计算机、工作站、主机、专用控制器如可编程逻辑控制器,或其组合。虽然处于阐释目的将计算机系统300作为单个计算机单元示出,但是系统可以包括一组/一列(a group/farm of)计算机,其可以取决于处理负荷和数据库大小(例如处理的样品总数和保持在系统上的结果)进行缩放。计算机系统300可以充当普通多任务计算机。
计算设备300优选支持操作系统,例如其储存在程序储存存储器390中并且由处理器310从易失性存储器执行。根据本系统和方法,操作系统包含用于将设备300连接到一个或多个计算模块的指令。根据本发明的实施方案,操作系统包含将计算机系统300连接至互联网和/或连接至专用网络的指令。
实施例1
来自加氢处理的石脑油料流的样品的示例性PIONA分析示于表1中。要注意的是,虽然分离了存在于石脑油中的大部分轻质组分如丙烷和丁烷,但是一些轻质组分将会残留溶于液体重整油产物中,并且因此将会在PIONA分析中出现。
表1–石脑油料流的PIONA分析
*总=97.09V%,损失=2.91V%。(即未将收率归一化)
基于表1的数据,可以将方程式(2)–(24)用于将石脑油价值指定为进料质量和目标RON数Rt的函数。因此,在表1给出的实例中,N=14.98和A=8.05
进料质量=N+2A=14.98+2*8.05=31.08 (2)。
从N+2A浓度比总收率的线性回归开发用于指定总重整器收率的方程式和常数。从总重整油收率比在目标研究辛烷值的各个收率的线性回归计算H2、C1、C2、C3、C4和C5+的各个收率以及重整油收率。对于这些计算,应用以下常数:
方程式(3):KYa=–0.01702;KYb=2.192;KYc=–0.03333;KYd=5.531;和KYe=–206.63
方程式(4)-(9):KC1ra=–0.12393;KC1rb=11.42;KC2ra=–0.17991;KC2rb=16.8;KC3ra=–0.25714;KC3rb=24.24286;KC4ra=–0.28705;KC4rb=27.27143;KGra=0.839255;KGrb=18.09532;KHra=0.0605;KHrb=0.1;和KHrc=–12.145。
因此,对于表1中给出的实例,当选择98的汽油目标辛烷值时,收率方程式(3)为如下:
Y=KYa*(N+2A)2+KYb*(N+2A)+KYc*Rt2+KYd*Rt+KYe(3)
Y=(–0.01702)*(31.08)2+2.192*31.08–0.03333*(98)2+5.531*98–206.63
Y=66.99。
各个原始产物收率为如下:
C1r=KC1ra*Y+KC1rb=0.12393*66.99+11.42=3.11 (4)
C2r=KC2ra*Y+KC2rb=–0.17991*66.99+16.8=4.75 (5)
C3r=KC3ra*Y+KC3rb=–0.25714*66.99+24.24286=7.02
(6)
C4r=KC4ra*Y+KC4rb=–0.28705*66.99+27.27143=8.04 (7)
Gr=KGra*Y+KGrb=0.839255*66.99+18.09532=74.32
(8)
Hr=KHra*Y+KHrb*Rt+KHrc=0.0605*66.99+0.1*98–12.145=1.7 (9)
总原始收率为:
Tr=C1r+C2r+C3r+C4r+Gr+Hr (10)
=3.11+4.75+7.02+8.04+74.32+1.71
=98.95
归一化的产物收率为如下:
C1n=(C1r*100)/Tr=(3.11*100)/98.94917=3.14 (11)
C2n=(C2r*100)/Tr=(4.75*100)/98.94917=4.80 (12)
C3n=(C3r*100)/Tr=(7.02*100)/98.94917=7.09 (13)
C4n=(C4r*100)/Tr=(8.04*100)/98.94917=8.13 (14)
Gn=(Gr*100)/Tr=(74.32*100)/98.94917=75.11 (15)
Hn=(Hr*100)/Tr(1.71*100)/98.94917=1.73 (16)
因此,如果甲烷的单位值为C1P=$152.44/吨;乙烷的单位值为C2P=$149.81/吨;丙烷的单位值为C3P=$343.71/吨;丁烷的单位值为C4P=$499.03/吨;汽油的单位值为GP=$601.63/吨;和氢气的单位值为HP=$391.60/吨,则表1的石脑油料流中的那些产物的值将会被计算为:
C1v=(3.14/100)*$152.44/吨=$4.80/吨 (17)
C2v=(4.80/100)*$149.81/吨=$7.19/吨 (18)
C3v=(7.09/100)*$343.71/吨=$24.37/吨 (19)
C4v=(8.13/100)*$499.03/吨=$40.57/吨 (20)
Gv=(75.11/100)*$601.63/吨=$451.88/吨 (21)
Hv=(1.73/100)*$391.60/吨=$6.77/吨 (22)
通过该方法计算的石脑油料流的价值为:
NPT=4.80+7.19+24.37+40.57+451.88+6.77=$535.58/吨
(23)
NPB=($535.58/吨/750升/吨)*159升/桶=$113.54/桶 (24)
实施例2
通过近红外光谱法分析具有在15℃的0.8658Kg/l的密度的阿拉伯轻质原油的样品。在表3中给出光谱数据。使用方程式(1a),通过加和检测到的峰(表3)的吸光度(纵列C2=1674.09、C4=1667.16、C6=1847.95、C8=1075.85、C10=1136.82),然后除以10,000来计算近红外光谱法指数(NIRA),其中所述实例中的值计算为0.7402。
NIRA为0.7575974,其通过取表3中的吸光度(纵列C2=1685.305、C4=1678.383、C6=1949.850、C8=1120.099、C10=1142.337)之和使用方程式1b来计算。使用方程式1b、1c和1d计算在36℃-180℃范围沸腾的石脑油级分链烷烃、芳族物质和环烷族含量:
链烷烃含量=KPa+KPb*DEN+KPc*DEN2+KPd*DEN3+KPe*I+KPf*I2+KPg*I3+KPh*DEN*I (1b)
芳族物质含量=KAa+KAb*DEN+KAc*DEN2+KAd*DEN3+KAe*I+KAf*I2+KAg*I3+KAh*DEN*I (1c)
环烷烃含量=100-链烷烃含量–芳族物质含量 (1d)
使用线性回归确定常数KPa至KPh和KAa至KAh并且将其示于表2中,其中在表中示出根据方程式(1b)和(1c)计算的值。
表2
常数 | 值 | 变量 | 值 | 方程式 | 值 |
KPa | 1.7341106E+06 | - | Kpa | 1.734E+06 | |
Kpb | -5.7388549E+06 | DEN | 8.658E-01 | Kpb*DEN | -4.969E+06 |
KPc | 6.2814681E+06 | DEN^2 | 7.496E-01 | Kpc*DEN^2 | 4.709E+06 |
KPd | -2.2726340E+06 | DEN^3 | 6.490E-01 | Kpd*DEN^3 | -1.475E+06 |
Kpe | 4.3445704E+04 | I | 7.402E-01 | Kpe*NIRA | 3.216E+04 |
KPf | -7.6959428E+03 | I^2 | 5.479E-01 | Kpf*NIRA^2 | -4.217E+03 |
KPg | 4.2917337E+03 | I^3 | 4.056E-01 | Kpg*NIRA^3 | 1.741E+03 |
KPh | -4.4780051E+04 | DEN*I | 6.409E-01 | Kph*DEN*NIRA | -2.870E+04 |
链烷烃 | 链烷烃 | 76.86 |
常数 | 值 | 变量 | 值 | 方程式 | 值 |
KAa | 3.5428498E+05 | - | KPa | 3.543E+05 | |
KAb | -1.2231660E+06 | DEN | 8.658E-01 | Kpb*DEN | -1.059E+06 |
KAc | 1.4074133E+06 | DEN^2 | 7.496E-01 | KPc*DEN^2 | 1.055E+06 |
KAd | -5.3968374E+05 | DEN^3 | 6.490E-01 | KPd*DEN^3 | -3.503E+05 |
KAe | -1.8696110E+02 | I | 7.402E-01 | Kpe*I | -1.384E+02 |
KAf | 1.0846669E+02 | I^2 | 5.479E-01 | KPf*I^2 | 5.943E+01 |
KAg | -7.3293203E+01 | I^3 | 4.056E-01 | KPg*I^3 | -2.972E+01 |
KAh | 1.5524161E+02 | DEN*I | 6.409E-01 | KPh*DEN*I | 9.949E+01 |
芳族物质 | 芳族物质 | 8.05 |
如下对比实际和预测的值并且相关性非常精确地预测石脑油级分的链烷烃和芳族物质组成。链烷烃含量为:实际76.74V%比预测76.68V%。芳族物质含量为:实际8.05V%比预测8.05V%。将这些值用于方程式2-24中,以计算石脑油价值。
在替代性实施方案中,本发明可以作为与计算机化的计算系统一起使用的计算机程序产品实施。本领域技术人员将会容易意识到,限定本发明的功能的程序可以以任意适当的编程语言编写并且以任意形式传递至计算机,包括但不限于:(a)永久储存在不可写存储介质(例如只读存储器装置如ROM或CD-ROM盘)上的信息;(b)可改变地存储在可写存储介质(例如软盘和硬盘驱动器)上的信息;和/或(c)通过通信介质如局域网、电话网络或公用网络(如互联网)传输至计算机的信息。当携带实施本发明方法的计算机可读指令时,这样的计算机可读介质代表本发明的替代性实施方案。
如本文中一般性阐释那样,系统实施方案可以引入许多计算机可读介质,所述介质包括具有本文中体现的计算机可读代码装置的计算机可用介质。本领域技术人员将会认识到,与所描述的各种方法相关的软件可以以各种各样的计算机可访问介质体现,从所述介质加载并且激活所述软件。根据关于Beauregard,35U.S.P.Q.2d 1383(美国专利5,710,578),本发明考虑并且包括在本发明范围内的该类型的计算机可读介质。在某些实施方案中,根据关于Nuijten,500F.3d 1346(Fed.Cir.2007)(美国专利申请号09/211,928),本权利要求书的范围被限制在计算机可读介质,其中所述介质是有形和非临时性的。
上文并且参考附图,已描述了本发明的系统和方法;然而,变型将对本领域普通技术人员显而易见并且本发明的保护范围待由在后的权利要求所限定。
Claims (8)
1.用于在不分馏/蒸馏条件下基于原油样品的分析为来源于原油样品的石脑油级分指定价值的系统,其中所述原油样品由密度表征,所述系统包括:
非易失性存储器装置,其储存计算模块和原油分析数据;
处理器,其连接至所述存储器;
第一计算模块,其中按下式计算芳族物质含量和环烷烃含量和将芳族物质含量和环烷烃含量指定至所述石脑油级分:
链烷烃含量=KPa+KPb*DEN+KPc*DEN2+KPd*DEN3+KPe*I+KPf*I2+KPg*I3+KPh*DEN*I (1b)
芳族物质含量=KAa+KAb*DEN+KAc*DEN2+KAd*DEN3+KAe*I+KAf*I2+KAg*I3+KAh*DEN*I (1c)
环烷烃含量=100-链烷烃含量–芳族物质含量 (1d)
其中KPa至KPh和KAa至KAh为常数,
DEN=在15℃的原油密度,和
I=由原油样品分析指定的指数,
其中变量为原油分析数据的指数和原油样品的密度,其中原油分析数据的指数由傅里叶变换近红外光谱法得到和根据下式(1a)指定为近红外吸收指数NIRA:
其中吸光度=指示在预定波数范围内检测到的峰的原油溶液的吸光度值;
第二计算模块,其中按下式计算总液体产物收率Y并且将总液体产物收率Y指定为芳族物质含量和环烷烃含量的函数:
进料质量=N+2A (2)
N用于表示环烷烃的总体积百分比和A用于表示芳族物质的总体积百分比;
其中总液体产物收率Y估算为进料质量和恒定RON数Rt的函数:
Y=KYa*(N+2A)2+KYb*(N+2A)+KYc*Rt2+KYd*Rt+KYe (3)
其中KYa至KYe为常数;
第三计算模块,其中对甲烷、乙烷、丙烷、丁烷和汽油中的每一种计算原始产物收率并且将各原始产物收率指定为指定的总液体产物收率Y的函数;
第四计算模块,其中计算氢气的原始产物收率并且将氢气的原始产物收率指定为指定的总液体产物收率Y和恒定RON数Rt的函数;和
第五计算模块,其中对氢气、甲烷、乙烷、丙烷、丁烷和汽油中的每一种计算价值并且将所述价值指定为氢气、甲烷、乙烷、丙烷、丁烷和汽油的每一种的原始产物收率和每种产物的单位价值的函数。
2.根据权利要求1所述的系统,其中在第三计算模块中按下式对甲烷、乙烷、丙烷、丁烷和汽油中的每一种计算原始产物收率并且将各原始产物收率指定为指定的总液体产物收率Y的函数:
原始甲烷收率C1r=KC1ra*Y+KC1rb (4)
原始乙烷收率C2r=KC2ra*Y+KC2rb (5)
原始丙烷收率C3r=KC3ra*Y+KC3rb (6)
原始丁烷收率C4r=KC4ra*Y+KC4rb (7)
原始汽油收率Gr=KGra*Y+KGrb (8)
其中KC1ra至KC4rb、KGra和KGrb为常数。
3.根据权利要求1或2所述的系统,其中在第四计算模块中按下式计算氢气的原始产物收率并且将氢气的原始产物收率指定为指定的总液体产物收率Y和恒定RON数Rt的函数:
原始氢气收率Hr=KHra*Y+KHrb*Rt+KHrc (9)
其中KHrA至KHrc为常数。
4.根据权利要求1或2所述的系统,其中在第五计算模块中按如下所述对氢气、甲烷、乙烷、丙烷、丁烷和汽油中的每一种计算价值并且将所述价值指定为氢气、甲烷、乙烷、丙烷、丁烷和汽油的每一种的原始产物收率和每种产物的单位价值的函数,其中:
估算总原始收率,其为上面各产物的估计的原始收率之和:
总原始收率Tr=C1r+C2r+C3r+C4r+Gr+Hr (10)
按下式通过将各原始收率除以总原始收率而将各原始收率归一化至100:
归一化的甲烷收率C1n=(C1r*100)/Tr (11)
归一化的乙烷收率C2n=(C2r*100)/Tr (12)
归一化的丙烷收率C3n=(C3r*100)/Tr (13)
归一化的丁烷收率C4n=(C4r*100)/Tr (14)
归一化的汽油收率Gn=(Gr*100)/Tr (15)
归一化的氢气收率Hn=(Hr*100)/Tr (16)
将每种产物的估算收率乘以其单位价值,以为每种产物指定价值:
甲烷的价值C1v=(C1n/100)*C1P (17)
乙烷的价值C2v=(C2n/100)*C2P (18)
丙烷的价值C3v=(C3n/100)*C3P (19)
丁烷的价值C4v=(C4n/100)*C4P (20)
汽油的价值Gv=(Gn/100)*GP (21)
氢气的价值Hv=(Hn/100)*HP (22)
其中C1P为甲烷的价值,C2P为乙烷的价值,C3P为丙烷的价值,C4P为丁烷的价值,GP为汽油的价值,和HP为氢气的价值。
5.运行计算机以在不分馏/蒸馏条件下基于原油样品的分析为来源于原油样品的石脑油级分指定价值的方法,其中所述原油样品由密度表征,所述方法包括:
向计算机中输入原油分析数据;
按下式计算芳族物质含量和环烷烃含量并且将芳族物质含量和环烷烃含量指定至所述石脑油级分:
链烷烃含量=KPa+KPb*DEN+KPc*DEN2+KPd*DEN3+KPe*I+KPf*I2+KPg*I3+KPh*DEN*I (1b)
芳族物质含量=KAa+KAb*DEN+KAc*DEN2+KAd*DEN3+KAe*I+KAf*I2+KAg*I3+KAh*DEN*I (1c)
环烷烃含量=100-链烷烃含量–芳族物质含量 (1d)
其中KPa至KPh和KAa至KAh为常数,
DEN=在15℃的原油密度,和
I=由原油样品分析指定的指数,
其中变量为原油分析数据的指数和原油样品的密度,其中原油分析数据的指数由傅里叶变换近红外光谱法得到和根据下式(1a)指定为近红外吸收指数NIRA:
其中吸光度=指示在预定波数范围内检测到的峰的原油溶液的吸光度值;
按下式计算总液体产物收率Y并且将总液体产物收率Y指定为芳族物质含量和环烷烃含量的函数:
进料质量=N+2A (2)
N用于表示环烷烃的总体积百分比和A用于表示芳族物质的总体积百分比;
其中总液体产物收率Y估算为进料质量和恒定RON数Rt的函数:
Y=KYa*(N+2A)2+KYb*(N+2A)+KYc*Rt2+KYd*Rt+KYe (3)
其中KYa至KYe为常数;
对甲烷、乙烷、丙烷、丁烷和汽油中的每一种计算原始产物收率并且将各原始产物收率指定为指定的总液体产物收率Y的函数;
计算氢气的原始产物收率并且将氢气的原始产物收率指定为指定的总液体产物收率Y和恒定RON数Rt的函数;和
对氢气、甲烷、乙烷、丙烷、丁烷和汽油中的每一种计算价值并且将所述价值指定为氢气、甲烷、乙烷、丙烷、丁烷和汽油中的每一种的原始产物收率和每种产物的单位价值的函数。
6.根据权利要求5所述的方法,其中按下式对甲烷、乙烷、丙烷、丁烷和汽油中的每一种计算原始产物收率并且将各原始产物收率指定为指定的总液体产物收率Y的函数:
原始甲烷收率C1r=KC1ra*Y+KC1rb (4)
原始乙烷收率C2r=KC2ra*Y+KC2rb (5)
原始丙烷收率C3r=KC3ra*Y+KC3rb (6)
原始丁烷收率C4r=KC4ra*Y+KC4rb (7)
原始汽油收率Gr=KGra*Y+KGrb (8)
其中KC1ra至KC4rb、KGra和KGrb为常数。
7.根据权利要求5或6所述的方法,其中按下式计算氢气的原始产物收率并且将氢气的原始产物收率指定为指定的总液体产物收率Y和恒定RON数Rt的函数:
原始氢气收率Hr=KHra*Y+KHrb*Rt+KHrc (9)
其中KHrA至KHrc为常数。
8.根据权利要求5或6所述的方法,其中按如下所述对氢气、甲烷、乙烷、丙烷、丁烷和汽油中的每一种计算价值并且将所述价值指定为氢气、甲烷、乙烷、丙烷、丁烷和汽油中的每一种的原始产物收率和每种产物的单位价值的函数,其中:
估算总原始收率,其为上面各产物的估计的原始收率之和:
总原始收率Tr=C1r+C2r+C3r+C4r+Gr+Hr(10)
按下式通过将各原始收率除以总原始收率而将各原始收率归一化至100:
归一化的甲烷收率C1n=(C1r*100)/Tr (11)
归一化的乙烷收率C2n=(C2r*100)/Tr (12)
归一化的丙烷收率C3n=(C3r*100)/Tr (13)
归一化的丁烷收率C4n=(C4r*100)/Tr (14)
归一化的汽油收率Gn=(Gr*100)/Tr (15)
归一化的氢气收率Hn=(Hr*100)/Tr (16)
将每种产物的估算收率乘以其单位价值,以为每种产物指定价值:
甲烷的价值C1v=(C1n/100)*C1P (17)
乙烷的价值C2v=(C2n/100)*C2P (18)
丙烷的价值C3v=(C3n/100)*C3P (19)
丁烷的价值C4v=(C4n/100)*C4P (20)
汽油的价值Gv=(Gn/100)*GP (21)
氢气的价值Hv=(Hn/100)*HP (22)
其中C1P为甲烷的价值,C2P为乙烷的价值,C3P为丙烷的价值,C4P为丁烷的价值,GP为汽油的价值,和HP为氢气的价值。
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KR20170118713A (ko) | 2017-10-25 |
CN107250787A (zh) | 2017-10-13 |
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