液滴分析与水质定量检测的研究
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摘要
液滴分析技术主要具有两大功能：一是用于液体的定性识别，二是用于液体特性参数的定量测量。目前国内外对该技术的研究主要集中在第一个功能上，即主要进行各种液滴分析方法及液滴分析仪的研究，试图更方便的获得包含更多液体信息的液滴指纹图。这些研究为应用液滴分析技术进行液体的定性识别，奠定了很好的基础。但这些研究对于定量获知被分析液体具体的水质参数还有很大的不足。本文主要对应用液滴分析技术获得定性识别液体信息的同时，如何定量检测出水质指标进行了研究。为拓宽液滴分析仪的实际应用奠定了一定的基础。
水质好坏与生态环境息息相关，检测水的质量，进而控制保护水资源环境，其重要性不言而喻。表征水质好坏的指标有很多，其中溶解氧、盐度、pH值、微量重金属元素的含量等是重要的指标，尤其是在水产的健康养殖上，对这些参数的精确控制是健康养殖的关键。本文主要对溶解氧、盐度和pH值等的定量检测进行了研究。
本文的主要工作概括如下：
1、将光纤溶解氧检测方法与光纤、电容液滴分析方法融合，在获得液滴指纹图的同时，实现了溶解氧的定量检测。基于光纤、电容液滴分析技术，引入了光纤溶解氧探头，解决了二者融合的技术难题，搭建了实验装置。实验装置搭建过程中主要的工作包括：荧光光源、光纤溶解氧探头和光电转换元件的选择，滴头结构的设计，溶解氧处理电路的设计以及前人设计的光纤、电容液滴分析方法实验装置的重新搭建。
2、建立了最佳的溶解氧检测模型。由实验装置对30种不同溶解氧浓度的液体进行了实验，由实验结果研究了不同的溶解氧检测模型，最后通过模型的对比分析，得到了最佳的溶解氧检测模型。
3、分析了液滴分析技术中光纤信号与盐度的关系，研究了盐度的检测模型。利用实验装置对20种不同盐度的溶液进行了实验，通过分析实验结果，由液滴指纹图中的光纤信号及指纹图曲线下的面积分别建立了盐度的检测模型。对几种模型的检测结果进行对比分析，得出利用光纤信号的平均值作为三层BP神经网络的输入检测盐度的效果最佳。
4、研究了液滴分析方法与pH值的相关性。对几种pH值较低的碱性溶液进行了实验，得出了光纤、电容液滴分析方法不适合用来检测液体的pH值的结论。对几种pH值较高的强碱性溶液进行实验，分析实验结果，得出了光纤、电容液滴分析方法不适合用作强碱性溶液的分析的结论。
5、就温度对液滴指纹图鉴别液体及溶解氧和盐度检测产生的影响进行了分析，提出了温度补偿方案。
Droplet analysis technique includes two major function: firstly, it can be usedfor the qualitative identification of the liquid; secondly, it is a quantitative methodfor measuring the liquid parameters.
At present, research of the technology at home and abroad mainly concentrateson the first function, i.e. more researches on droplet analysis methods and dropletanalyzers to acquire liquid droplets fingerprints which contain more liquidinformation more convenient. The above researches laid a good foundation for thequalitative identification of the liquid drop analysis technology, but they were shortof acquiring the specific water quality parameters of the analyzed liquidquantitatively. In this paper, droplet analysis technique is used to acquire liquidinformation qualitatively, at the same time, more researches focus on how to detectthe water quality indicator parameters quantitatively, which makes a good basis forexpanding the practical application of the droplet analyzer.
Water quality and ecological environment are closely related. It is important todetect the water quality and protect the water resources. There are many indicatorscan describe water quality, among them, dissolved oxygen, salinity, pH, temperatureand trace of heavy metals content are the most important. Especially in the aquatichealthy breeding, the precise control of the above parameters is the key factor. Aboveall, we focus on the quantitative detection of the following water quality indicators-dissolved oxygen, salinity and pH, which based on the quantitative detection ofdroplet analysis technique.
The main work of this paper is summarized as followings:
1. Combining the fiber optic dissolved oxygen method and fiber optic capacitivedrop technology. The quantitative detection of dissolved oxygen can be realizedwhile acquiring the liquid droplets fingerprints. Based on fiber optic, capacitive droptechnology, introducing the fiber optic dissolved oxygen probe to solve the technicalproblem of fusing the two methods. Then the experimental device is set up, whichincludes: the choice of the fluorescent light source, fiber optic dissolved oxygenprobe and the photoelectric conversion element, the designing of emitter structure,the processing circuit of dissolved oxygen and re-building the experimental device offiber optic, capacitive drop setup.
2. Establishing the best dissolved oxygen detection model. Doing experiments based on30different dissolved oxygen concentration of the liquid, studying differentdissolved oxygen detection models based on the experiments results, through thecomparative analysis of models, the best dissolved oxygen detection model can beobtained.
3. Analyzing the relationship of fiber-optic signal in drop technology and salinity.Studying the salinity detection model. Utilizing the experiments device, conductexperiments on20different salinity solutions. Through the analysis of experimentalresults, we establish the salinity detection model based on the fiber signal and areaunder the fingerprint curves in the liquid droplets fingerprints. By comparing severalexperimental results based on several models, we obtain that three-level BP neuralnetwork model detects optimally by using fiber average signal of the input vector.
4. Discussing the relativity between droplet analysis methods and the pH. Doingsome experiments with several lower alkaline solution, we find that fiber, capacitivedrop analysis is not appropriate for the detection of pH. We have also do someresearch on the strong alkaline solution of several high pH, after analyzing theexperimental results, concluding that fiber capacitive drop analysis method is notsuitable for the strong alkaline solution.
5. Analyzing the possible impact of the temperature on the fingerprint anddetection of liquid, dissolved oxygen and salinity. A method of temperaturecompensation is given.
水质好坏与生态环境息息相关，检测水的质量，进而控制保护水资源环境，其重要性不言而喻。表征水质好坏的指标有很多，其中溶解氧、盐度、pH值、微量重金属元素的含量等是重要的指标，尤其是在水产的健康养殖上，对这些参数的精确控制是健康养殖的关键。本文主要对溶解氧、盐度和pH值等的定量检测进行了研究。
本文的主要工作概括如下：
1、将光纤溶解氧检测方法与光纤、电容液滴分析方法融合，在获得液滴指纹图的同时，实现了溶解氧的定量检测。基于光纤、电容液滴分析技术，引入了光纤溶解氧探头，解决了二者融合的技术难题，搭建了实验装置。实验装置搭建过程中主要的工作包括：荧光光源、光纤溶解氧探头和光电转换元件的选择，滴头结构的设计，溶解氧处理电路的设计以及前人设计的光纤、电容液滴分析方法实验装置的重新搭建。
2、建立了最佳的溶解氧检测模型。由实验装置对30种不同溶解氧浓度的液体进行了实验，由实验结果研究了不同的溶解氧检测模型，最后通过模型的对比分析，得到了最佳的溶解氧检测模型。
3、分析了液滴分析技术中光纤信号与盐度的关系，研究了盐度的检测模型。利用实验装置对20种不同盐度的溶液进行了实验，通过分析实验结果，由液滴指纹图中的光纤信号及指纹图曲线下的面积分别建立了盐度的检测模型。对几种模型的检测结果进行对比分析，得出利用光纤信号的平均值作为三层BP神经网络的输入检测盐度的效果最佳。
4、研究了液滴分析方法与pH值的相关性。对几种pH值较低的碱性溶液进行了实验，得出了光纤、电容液滴分析方法不适合用来检测液体的pH值的结论。对几种pH值较高的强碱性溶液进行实验，分析实验结果，得出了光纤、电容液滴分析方法不适合用作强碱性溶液的分析的结论。
5、就温度对液滴指纹图鉴别液体及溶解氧和盐度检测产生的影响进行了分析，提出了温度补偿方案。
Droplet analysis technique includes two major function: firstly, it can be usedfor the qualitative identification of the liquid; secondly, it is a quantitative methodfor measuring the liquid parameters.
At present, research of the technology at home and abroad mainly concentrateson the first function, i.e. more researches on droplet analysis methods and dropletanalyzers to acquire liquid droplets fingerprints which contain more liquidinformation more convenient. The above researches laid a good foundation for thequalitative identification of the liquid drop analysis technology, but they were shortof acquiring the specific water quality parameters of the analyzed liquidquantitatively. In this paper, droplet analysis technique is used to acquire liquidinformation qualitatively, at the same time, more researches focus on how to detectthe water quality indicator parameters quantitatively, which makes a good basis forexpanding the practical application of the droplet analyzer.
Water quality and ecological environment are closely related. It is important todetect the water quality and protect the water resources. There are many indicatorscan describe water quality, among them, dissolved oxygen, salinity, pH, temperatureand trace of heavy metals content are the most important. Especially in the aquatichealthy breeding, the precise control of the above parameters is the key factor. Aboveall, we focus on the quantitative detection of the following water quality indicators-dissolved oxygen, salinity and pH, which based on the quantitative detection ofdroplet analysis technique.
The main work of this paper is summarized as followings:
1. Combining the fiber optic dissolved oxygen method and fiber optic capacitivedrop technology. The quantitative detection of dissolved oxygen can be realizedwhile acquiring the liquid droplets fingerprints. Based on fiber optic, capacitive droptechnology, introducing the fiber optic dissolved oxygen probe to solve the technicalproblem of fusing the two methods. Then the experimental device is set up, whichincludes: the choice of the fluorescent light source, fiber optic dissolved oxygenprobe and the photoelectric conversion element, the designing of emitter structure,the processing circuit of dissolved oxygen and re-building the experimental device offiber optic, capacitive drop setup.
2. Establishing the best dissolved oxygen detection model. Doing experiments based on30different dissolved oxygen concentration of the liquid, studying differentdissolved oxygen detection models based on the experiments results, through thecomparative analysis of models, the best dissolved oxygen detection model can beobtained.
3. Analyzing the relationship of fiber-optic signal in drop technology and salinity.Studying the salinity detection model. Utilizing the experiments device, conductexperiments on20different salinity solutions. Through the analysis of experimentalresults, we establish the salinity detection model based on the fiber signal and areaunder the fingerprint curves in the liquid droplets fingerprints. By comparing severalexperimental results based on several models, we obtain that three-level BP neuralnetwork model detects optimally by using fiber average signal of the input vector.
4. Discussing the relativity between droplet analysis methods and the pH. Doingsome experiments with several lower alkaline solution, we find that fiber, capacitivedrop analysis is not appropriate for the detection of pH. We have also do someresearch on the strong alkaline solution of several high pH, after analyzing theexperimental results, concluding that fiber capacitive drop analysis method is notsuitable for the strong alkaline solution.
5. Analyzing the possible impact of the temperature on the fingerprint anddetection of liquid, dissolved oxygen and salinity. A method of temperaturecompensation is given.
引文
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