CN113496100A - 用于外推校准光谱的系统和计算机实施的方法 - Google Patents
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Abstract
本申请涉及用于外推校准光谱的系统和计算机实施的方法。本公开涉及一种用于预测校准光谱的计算机实施的方法,该方法包括提供使用与在不同压力下的不同气体种类相对应的历史校准数据训练的机器学习模型的步骤。该计算机实施的方法还包括以下步骤:使用分析仪对在一种压力下的一个气体种类执行校准扫描,并且使用机器学习模型和校准扫描为分析仪生成与在多种压力下的一个或多个气体种类相对应的校准曲线。此后,使用分析仪获得光谱,并且使用该光谱和至少一条校准曲线生成浓度测量。
Description
技术领域
本公开涉及使用机器学习模型对校准光谱进行预测。
背景技术
为了校准分析装置,将已知含量和数量的校准流体通过该装置,以产生已知浓度的测量。如果这些测量与校准流体中的已知量不一致,则相应地对分析装置进行调整。对复杂分析装置(诸如分析仪,或更具体地过程分析仪)的校准会既费时又费力。为了使测量对外部影响和随时间产生的变化具有鲁棒性,在工厂需要校准许多因素。
例如,使用TDLAS(可调谐二极管激光吸收光谱)分析仪,必须在不同的环境参数(包括压力和温度)下测量目标分析物的光谱和背景气体矩阵的主要成分。最新技术包括假设恒定的装置功能(其允许为每个仪器使用相同的矩阵),或者为每个单独的仪器测量校准矩阵所需的所有光谱。由于装置之间的差异,前一种使用相同校准矩阵的方法在节省时间和资源的同时会固有地将不确定性引入到测量结果中。在考虑特定装置功能的同时,测量校准矩阵的所有元素的后一种方法确实需要大量的时间和资源。除其它因素外,这两种方法均无法解决现场发生的装置老化问题。
发明内容
一方面,一种用于预测校准光谱的计算机实施的方法包括提供使用与在不同压力下的不同气体种类相对应的历史校准数据训练的机器学习模型的步骤。该计算机实施的方法还包括以下步骤:使用分析仪对在一种压力下的一个气体种类执行校准扫描,以及使用机器学习模型和校准扫描为分析仪生成与在多种压力下的一个或多个气体种类相对应的分析仪的校准曲线。此后,使用分析仪获得光谱,并且使用该光谱和至少一条生成的校准曲线来生成浓度测量。
另一方面,一种系统包括计算机硬件,该计算机硬件包括至少一个可编程处理器和存储指令的机器可读介质,在至少一个可编程处理器执行所述指令时,所述指令促使计算机硬件执行操作,所述操作包括提供使用与在不同压力下的不同气体种类相对应的历史校准数据训练的机器学习模型。附加操作包括使用分析仪对在一种压力下的一个气体种类进行校准扫描,使用机器学习模型和校准扫描为分析仪生成与在多种压力下的一个或多个气体种类相对应的校准曲线,使用分析仪获得光谱,以及使用该光谱和至少一条生成的校准曲线来生成浓度测量。
另一方面,一种计算机程序产品,包括对指令进行编码的机器可读存储介质,在一个或多个可编程处理器执行所述指令时,所述指令促使一个或多个可编程处理器执行操作,所述操作包括提供使用与在不同压力下的不同气体种类相对应的历史校准数据训练的机器学习模型。附加操作包括使用分析仪对在一种压力下的一个气体种类进行校准扫描,使用机器学习模型和校准扫描为分析仪生成与在多种压力下的一个或多个气体种类相对应的校准曲线,使用分析仪获得光谱,以及使用光谱和至少一条生成的校准曲线来生成浓度测量。
附图说明
在下文中,参考附图中示出的示例性实施例更详细地描述本公开。
图1a-1d示出多组曲线图,其描绘由分析仪生成的并且指示仪器响应如何随压力变化的校准曲线;
图2示出用于诸如通过使用来自图1a-1d的曲线图的信息来确定未知样品中物质的浓度的方法步骤的流程图;
图3a至图3d示出描绘校准曲线的一组曲线图,所述校准曲线包括由分析仪针对多种气体种类在预定数量压力下生成的一组校准曲线;
图4a至图4d示出描绘校准曲线的一组曲线图,所述校准曲线包括由分析仪针对仅一个气体种类在一个预定压力下生成的校准曲线;以及
图5示出诸如通过使用来自图3a-3d和/或图4a-4d的曲线图的信息来确定未知样品中物质的浓度的方法步骤的流程图。
具体实施方式
LAS(光吸收光谱)气体分析仪的工作原理是通过被测气体吸收光。这些分析仪仅需发出光束穿过样品室,然后测量样品吸收了多少特定波长。吸收的光量与吸收光的流体中组分的浓度成正比。应当理解,本公开适用于多种分析仪,包括例如可调谐二极管激光器/量子级联激光器/带间级联激光器(TDL/QCL/ICL)气体分析仪、近红外(NIR)或傅立叶变换红外(FTIR)光谱仪等。
校准是指调整诸如分析仪之类的测量仪器的精度并且使任何测量不确定性最小化的行为。为了校准分析仪,例如,将已知含量和数量的校准流体通过分析仪,以生成组分浓度的测量。如果这些测量与校准流体中的已知量不一致,则相应地调整过程分析仪。
校准曲线用于基于已知浓度的样品的先前测量来确定未知物质的浓度。测量的精度和准确性取决于校准曲线。曲线越好,精度越高;曲线越差,精度越差。
图1a示出包括多条校准曲线12或光谱的曲线图10,校准曲线12或光谱也称为参考曲线,其示出每个气体种类的仪器响应如何随压力变化。根据本公开的示例性实施例,示出了多个曲线图10、20、30和40,每个曲线图分别包含多条(或一组)校准曲线12、22、32和42。每一个曲线图10、20、30和40的每一组示例性校准曲线12、22、32和42都对应于在多种压力下的一个气体种类,正如可以使用分析仪手动产生的。
例如,图1a的每条校准曲线12都对应于处于多种不同的预定压力之一下的甲烷。图1b的校准曲线22分别对应于处于多种不同预定压力之一下的H2S,图1c的校准曲线32对应于处于多个不同预定压力之一下的CO2,而图1d的校准曲线42对应于处于多种不同预定压力之一下的乙烷。
尽管在附图中示出了四种不同的气体种类,但是应当理解,本公开可以适用于多种不同的气体种类。例如,本公开可以适用于另外的气体种类,诸如例如硫化氢、乙炔、氨、二氧化碳、水蒸气/水分、氧气、氯化氢、甲烷、一氧化碳、甲醇、乙烷、乙烯、甲基乙炔、丙二烯、氮氧化物和硫氧化物。
在大多数测量过程中,仪器校准是必不可少的步骤。它包含一组操作,这些操作可以建立测量系统的输出与校准标准的可接受值之间的关系。这通常包含:准备一组标准物,在标准物中包含已知量的目标分析物;测量每种标准物的仪器响应;以及建立仪器响应与分析物浓度之间的关系。然后使用这种关系将对测试样品进行的测量转换为对所存在的分析物的量的估计。
现在转到图2,示出了局部流程图50。在步骤1,框52处,诸如手动地生成不同气体在各种压力和特定浓度下的一组校准曲线,并且将所述一组校准曲线保存在分析仪的数据存储装置中,诸如图1a、1b、1c和1d所示的那些校准曲线。将在校准期间生成并保存预定数量的校准曲线,以供以后在未知气体样品的实时测量中使用。然而,可以生成更多或更少数量的校准曲线,并且可以结合本公开的教导。
在步骤2中,当测量未知气体样品的光谱时,将该光谱与多条校准曲线拟合。这些使用的校准曲线中的每一条都代表分析仪对在未知气体样品的特定压力下的一个气体种类的光谱响应。在步骤3处,将代表目标分析物的参考曲线的拟合系数乘以在校准期间已知的目标分析物的浓度,以生成未知测量样品中目标分析物的浓度。
如上所讨论的,针对每个气体种类在多个不同压力下生成校准曲线。除了压力之外,还可以考虑温度信号、碰撞加宽信号和/或背景信号。这导致手动生成大量曲线。甚至校准单个分析仪都会花费相当长的一段时间。此外,每个分析仪略有不同,需要单独的校准。例如,当前过程是保存不同气体在各种压力下的参考曲线。保存曲线的整个过程可能要花费数小时,如果过程期间发生错误,则甚至可能需要重复进行整个过程。此外,系统在保存曲线期间并不总是稳定的,并且系统中没有任何内容用以解决保存曲线时产生的误差。
神经网络是一组算法,可以根据人脑进行松散地建模,旨在识别模式。它们通过一种机器感知、标记或聚集原始输入来解释感官数据。它们识别的模式是向量中所包含的数字,所有真实世界的数据(图像、声音、文本或时间序列)都必须转换成数字。在神经网络中用于执行学习过程的过程称为优化算法。
根据本公开,神经网络模型可以从一小组校准样品中生成校准/参考曲线,了解分析仪之间的差异,并且创建映射到不同压力或不同气体种类的函数。根据一种架构,可以使用Keras API开发神经网络,并且该神经网络可以是三层完全连接的神经网络。例如,第一层使用ReLu激活函数并且包含100个以上的节点,第二层使用tanh激活函数并且包含大约100个节点,而第三层使用ReLu激活函数并且包含100个以下的节点。
神经网络和其它形式的人工智能是已知的,因此在此不再详细讨论。应当理解,本公开可以利用多种已知的机器学习策略中的任何一种。例如,作为神经网络模型的替代,本公开可以使用替代的机器学习模型,诸如例如偏最小二乘(PLS)模型、逆最小二乘(ILS)模型、经典最小二乘(CLS)模型或主成分回归(PCR)模型。
图3a至图3d示出了描绘校准曲线62、72、82和92的一组曲线图60、70、80和90,所述校准曲线62、72、82和92分别针对每个气体种类(例如,甲烷、硫化氢、二氧化碳和乙烷)在预定压力下手动生成。即,可以为每个气体种类在一个预定压力下生成一条曲线62、72、82和92,并且可以使用机器学习模型(诸如例如神经网络模型)来生成与每个气体种类在另外的预定压力下相对应的一组另外的曲线64、74、84和94。从一条曲线到另一条曲线的信息可能高度相关。手动生成的曲线62、72、82和92可以用于以已知方式进一步训练神经网络模型。
图4a至图4d示出了一组曲线图100、110、120和130,其描绘了针对每个气体种类在一种预定压力下生成的校准曲线102、112、122和132。特别地,校准曲线102和112各自对应于在一种预定压力下的一个气体种类(例如,甲烷和硫化氢),并且可以手动生成。即,可以使用分析仪对在一种压力下的一个气体种类进行校准扫描。
针对其它气体种类122、132在相同的一个预定压力下以及针对用于分析仪的与多个气体种类在多种压力下相对应的多条预定校准曲线的其它预定压力104、114、124和134的其它校准曲线组,可以使用神经网络模型或替代机器学习模型和来自校准扫描的最终光谱加以生成。
参考图5的局部流程图140,描述了本公开的示例性方法,其可以包括利用历史校准数据来训练神经网络,以及使用经训练的神经网络来校准拟合到参数空间中(例如,气体组分和要由分析仪测量的样品压力)的多个分析仪。在第一步骤142,提供神经网络模型,该神经网络模型使用对应于不同压力下的不同气体种类的历史校准数据来训练。例如,神经网络模型可以使用从上述手动生成的曲线收集的历史数据。
在另一步骤144,使用分析仪对在一种压力下的一个气体种类进行校准扫描。或者,可以使用分析仪进行多次校准扫描,每个校准扫描都针对不同的气体并且都在一种压力下进行。在那之后的146,可以使用神经网络模型和校准扫描或最终光谱来生成分析仪的与在多种压力下的多种气体相对应的校准曲线。例如,可以使用经训练的神经网络来生成图1a、图1b、图1c和图1d中所示的每组校准曲线的其余部分。
在步骤148,可以使用针对特定分析仪的一组所生成的校准曲线来使用未知样品的测量光谱和所生成的校准曲线的子集来确定浓度测量。
数据表明,申请人公开的方法至少与其它传统方法一样好。
Claims (10)
1.一种用于预测校准光谱的计算机实施的方法,包括:
提供使用与在不同压力下的不同气体种类相对应的历史校准数据训练的机器学习模型;
使用分析仪对在一种压力下的一个气体种类进行校准扫描;
使用所述机器学习模型和所述校准扫描为所述分析仪生成与在多种压力下的一个或多个气体种类相对应的校准曲线;
使用所述分析仪获得光谱;以及
使用所述光谱和至少一条所生成的校准曲线来生成浓度测量。
2.根据权利要求1所述的计算机实施的方法,还包括:为所述分析仪生成与在多种压力下的多个气体种类相对应的校准曲线。
3.根据权利要求1所述的计算机实施的方法,还包括:使用所述分析仪在一种压力下执行多次校准扫描,其中,每次所述校准扫描对应于在所述一种压力下的不同气体种类。
4.根据权利要求1所述的计算机实施的方法,其中,所述一种气体是硫化氢、乙炔、氨、二氧化碳或水。
5.根据权利要求1所述的计算机实施的方法,其中,所述一种气体是氧气、氯化氢、甲烷或一氧化碳。
6.根据权利要求1所述的计算机实施的方法,其中,所述一种气体是甲醇、乙烷、乙烯、甲基乙炔、丙二烯、氮氧化物或硫氧化物。
7.根据权利要求1所述的计算机实施的方法,其中,所述机器学习模型包括神经网络模型、偏最小二乘模型、逆最小二乘模型、经典最小二乘模型和主成分回归模型中的至少一个。
8.根据权利要求1所述的计算机实施的方法,其中,使用与在不同压力和温度下的不同气体种类相对应的历史校准数据来进一步训练所述神经网络模型。
9.一种计算机程序产品,包括对指令进行编码的机器可读存储介质,在一个或多个可编程处理器执行所述指令时,所述指令促使所述一个或多个可编程处理器执行如前述权利要求中的至少一项所述的操作。
10.一种用于预测校准光谱的系统,包括:
计算机硬件,所述计算机硬件包括:
至少一个可编程处理器;以及
存储指令的机器可读介质,当所述至少一个可编程处理器执行所述指令时,所述指令促使所述计算机硬件执行如权利要求1至8中的至少一项所述的操作。
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