基于紫外吸收光谱的COD测量方法, 尽管具有快速、 实时、 免试剂、 无污染等优势。 但该方法对于组分多变的水样适应性不强, 构建的单一计算模型不能适用于所有待测水样类型, 导致其在复杂环境下测量准确度较低, 从而限制了其应用领域。 本研究提出一种基于水样类型识别的测量方法。 其过程包括: 动态识别水样类型→自动选择相应的“吸光度(Auv)-COD”算法模型→计算COD。 该方法有效提高了紫外光谱法COD测量的准确度和适用性。 该研究在传统的光谱识别技术的基础上, 针对COD实际测量的特点加以改进。 选取水样吸光度曲线的形貌特征作为水样类型的表征参数, 利用LM-BP神经网络作为识别算法。 并引入了“历史数据队列”、 “历史识别因子”的概念, 在此基础上形成了级联的神经网络结构。 该算法实现了COD测量应用中的高准确度的光谱识别, 进而提高了复杂环境下COD测量的精度。 大量实验测试和结果表明, 与传统的光谱识别技术相比, 该方法在COD测量应用中具有更好的鲁棒性和准确性。 水样类型识别准确率达98%以上。 同时算法结构简单, 计算量小, 适用于资源受限的小型化COD测量仪。 当仪器在复杂多变的水环境中进行测量时, 采用该算法测量得到的COD精度有显著的提高。 该方法的提出为光谱COD测量法在水体组分多变场合的应用及提高其测量精度提供了技术保证, 可望解决传统紫外光谱COD测量法难以适应变化和复杂水环境应用的问题。

Chemical oxygen demand (COD) is one of the important indicators of water quality. The COD measurement method based on UV/Vis absorption spectra has been widely used because of its advantages of high speed, real time results, void of reagents, and pollution-free. Different functional groups have different characteristic absorption spectra. In the application of the method to water samples with a stable solution, high COD measurement accuracy can be achieved by building a precise “UV/Vis Absorbance(Auv)-COD” computational model. However, when the method is used for water samples with variable components, the measurement precision is low, limiting its applicability. This study proposes a new method based on dynamic identification of water sample type. The LM-BP neural network algorithm is used for the identification of water samples in this paper. And the morphology characteristics of the absorption spectra were used as the input parameters of water sample identification models. In the application of COD measurement, the water sample’s absorption spectra have a time correlation. Based on the foundation laid by traditional spectrum identification techniques, the algorithm was optimized in accordance with the characteristics of COD measurement. The concept of historical data queue and historical identification factor was introduced into LM-BP artificial neural network and forms the cascaded network structure. Experiments show that the method exhibited better robustness and higher accuracy than traditional algorithms, because the cascaded network is relatively more able to adapt the characteristics of the COD measurement. The test yielded a 98% identification accuracy rate, which can provide a technical guarantee for the application of spectral COD measurement in a complex environment. In this paper, the sensor structure and the proposed algorithm are simple as well, which can be used in the portable instrument with limited resource. The UV/Vis-COD measurement method based on the water sample identification algorithm can achieve improved accuracy compared to the traditional method, which calculates all water sample types with the same computational model. The proposed method is expected to solve the problem that traditional UV/Vis-COD measurement methods face regarding difficulties adapting when applied to complex environments while still achieving high COD measurement accuracy.