基于偏振信息的污泥沉降比快速测量研究  

Rapid measurement of sludge sedimentation ratio based on polarization information

作　　者：王新强[1,3] 覃杉 孙晓兵 熊伟 叶松[1,3] 王方原[1,3] 童宣科[1,3] WANG Xinqiang;QIN Shan;SUN Xiaobing;XIONG Wei;YE Song;WANG Fangyuan;TONG Xuanke(School of Optoelectronic Engineering,Guilin University of Electronic Technology,Guilin 541004,China;Anhui Institute of Optics and Fine Mechanics,Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,China;Guangxi Key Laboratory of Optoelectronic Information Processing,Guilin 541004,China;Key Laboratory of General Optical Calibration and Characterization of Chinese Academy of Sciences,Hefei 230031,China)

机构地区：[1]桂林电子科技大学光电工程学院,广西桂林541004 [2]中国科学院合肥物质科学研究院安徽光学精密机械研究所,安徽合肥230031 [3]广西光电信息处理重点实验室,广西桂林541004 [4]中国科学院通用光学定标与表征技术重点实验室,安徽合肥230031

出　　处：《应用光学》2025年第1期121-128,共8页Journal of Applied Optics

基　　金：国家重点研发项目(2022YFB3901800);国家自然科学基金(41961050,41975033);中国科学院通用光学定标与表征技术重点实验室研究项目;桂林电子科技大学研究生教育创新计划资助项目(2023YCXS227)。

摘　　要：污水处理是人类生产、生活中保持水源清洁的重要环节,污泥沉降比是衡量污水处理效果的关键指标。现有的污泥沉降比测量需要等杂质沉淀完成,耗费时间长,检测效率低下。为了实现实时、非接触、低耗测量污泥沉降比,研究了均匀污泥水样光偏振信息与沉降比之间的关系,通过测量污泥沉淀前混合溶液的偏振图像,提取偏振特征参量,利用BP(back propagation)神经网络对偏振参量与沉降比进行训练建模,建立了输入层为I、Q、U、P、θ,隐含层节点数为13,输出层为污泥沉降比,网络拓扑结构为“5-13-1”的BP神经网络预测模型。模型训练采用L-M(Levenberg-Marquardt)算法,网络传递函数采用tansig-purelin。结果表明:预测模型的平均相对误差为4.361%,平均绝对误差为0.00821,均方误差为0.00014,均方根误差为0.01213,四者均在误差允许的范围内,能够用于污泥沉降比的快速预测。Wastewater treatment is an important part to keep water clean in human production and life,and sludge sedimentation ratio is the key index to measure the effectiveness of wastewater treatment.The existing measurement of sludge sedimentation ratio needs to wait for the completion of impurities settling,which is time-consuming and inefficient to detect.In order to achieve real-time,non-contact and low-consumption measurement of sludge sedimentation ratio,the relationship between optical polarization information and sedimentation ratio of homogeneous sludge water samples was studied.The polarization feature parameters were extracted by measuring the polarization image of the mixed solution before sludge sedimentation,the back propagation(BP)neural network was used to train and model the polarization parameters and sedimentation ratio,and the BP neural network prediction model with input layer of I,Q,U,P,θand hidden layer of 13 nodes,output layer of sludge sedimentation ratio and network topology of"5-13-1"was established.The Levenberg-Marquardt(L-M)algorithm was used for model training,and tansig-purelin was used for the network transfer function.The results show that the average relative error of the prediction model is 4.361%,the average absolute error is 0.00821,the mean square error was 0.00014,and the root mean square error was 0.01213,all of which are within the error tolerance and can be used for rapid prediction of sludge sedimentation ratio.

关 键 词：偏振信息 污泥沉降比 定量检测 BP神经网络 

分 类 号：TN206[电子电信—物理电子学] O436.3[机械工程—光学工程]