Pollution source identification methods and remediation technologies of groundwater: A review

Groundwater is an important source of drinking water.Groundwater pollution severely endangers drinking water safety and sustainable social development.In the case of groundwater pollution,the top priority is to identify pollution sources,and accurate information on pollution sources is the premise of efficient remediation.Then,an appropriate pollution remediation scheme should be developed according to information on pollution sources,site conditions,and economic costs.The methods for identifying pollution sources mainly include geophysical exploration,geochemistry,isotopic tracing,and numerical modeling.Among these identification methods,only the numerical modeling can recognize various information on pollution sources,while other methods can only identify a certain aspect of pollution sources.The remediation technologies of groundwater can be divided into in-situ and ex-situ remediation technologies according to the remediation location.The in-situ remediation technologies enjoy low costs and a wide remediation range,but their remediation performance is prone to be affected by environmental conditions and cause secondary pollution.The ex-situ remediation technologies boast high remediation efficiency,high processing capacity,and high treatment concentration but suffer high costs.Different methods for pollution source identification and remediation technologies are applicable to different conditions.To achieve the expected identification and remediation results,it is feasible to combine several methods and technologies according to the actual hydrogeological conditions of contaminated sites and the nature of pollutants.Additionally,detailed knowledge about the hydrogeological conditions and stratigraphic structure of the contaminated site is the basis of all work regardless of the adopted identification methods or remediation technologies.

A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders

Wayside monitoring is a promising cost-effective alternative to predict damage in the rolling stock. The main goal of this work is to present an unsupervised methodology to identify out-of-roundness(OOR) damage wheels, such as wheel flats and polygonal wheels. This automatic damage identification algorithm is based on the vertical acceleration evaluated on the rails using a virtual wayside monitoring system and involves the application of a two-step procedure. The first step aims to define a confidence boundary by using(healthy) measurements evaluated on the rail constituting a baseline. The second step of the procedure involves classifying damage of predefined scenarios with different levels of severities. The proposed procedure is based on a machine learning methodology and includes the following stages:(1) data collection,(2) damage-sensitive feature extraction from the acquired responses using a neural network model, i.e., the sparse autoencoder(SAE),(3) data fusion based on the Mahalanobis distance, and(4) unsupervised feature classification by implementing outlier and cluster analysis. This procedure considers baseline responses at different speeds and rail irregularities to train the SAE model. Then, the trained SAE is capable to reconstruct test responses(not trained) allowing to compute the accumulative difference between original and reconstructed signals. The results prove the efficiency of the proposed approach in identifying the two most common types of OOR in railway wheels.

Study on Identification and Fermentation Condition of Strain ZY-19-2 with Inhibitory Effect against Tobacco Black Shank (Phytophtora parasitica var. nicotianae Tucker)

[Objective] The paper was to identify stain ZY-19-2 with inhibitory effect against tobacco black shank （Phytophtora parasitica var.nicotianae Tucker）, and study the fermentation condition of the strain. [Method]A strain ZY-19-2 with strong inhibitory effect against P. parasitica were isolated and screened from tobacco rhizosphere soil samples, and identified according to its morphological characteristics. The chitinase production activity of the strain under different culture conditions was also studied. [Result] For stain ZY-19-2 Paecilomyces lilacinus, the optimal fermentation conditions were as follows： 1.2% colloidal chitin as carbon source, 1% peptone as nitrogen source, 0.1% Tween 80 as surfactant, initial pH of fermentation broth at 6.0, the fermentation time of 60 h, inoculum amount at 1%, shaker speed at 120 r/min. The highest enzyme activity reached 0.216 U/ml. [Conclusion]The optimization of fermentation condition of strain ZY-19-2 lay foundation for large-scale production of cheap and efficient chitinase and chitin oligosaccharides, as well as application of the strain for control of tobacco black shank.

Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics

Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions.However,material identification is a challenging task,especially when the characteristic of the material is highly nonlinear in nature,as is common in biological tissue.In this work,we identify unknown material properties in continuum solid mechanics via physics-informed neural networks(PINNs).To improve the accuracy and efficiency of PINNs,we develop efficient strategies to nonuniformly sample observational data.We also investigate different approaches to enforce Dirichlet-type boundary conditions(BCs)as soft or hard constraints.Finally,we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material space.The estimated material parameters achieve relative errors of less than 1%.As such,this work is relevant to diverse applications,including optimizing structural integrity and developing novel materials.

A dynamic size-based time series feature and application in identification of zinc flotation working conditions

Conventional feature description methods have large errors in froth features due to the fact that the image during the zinc flotation process of froth flotation is dynamic,and the existing image features rarely have time series information.Based on the conventional froth size distribution characteristics,this paper proposes a size trend core feature(STCF)considering the froth size distribution,i.e.,a feature centered on the time series of the froth size distribution.The core features of the trend are extracted,the inter-frame change factor and the inter-frame stability factor are given and two calculation methods of the feature factors are proposed.Meanwhile,the STCF feature algorithm was established based on the core features by adding the inter-frame change factor and the inter-frame stability factor.Finally,a flotation condition recognition model based on BP neural network was established.The experiments show that the recognition model has achieved excellent results,proving that the method proposed effectively overcomes the limitation of the lack of dynamic information in the existing traditional size distribution features and the introduction of the two factors can improve the classification accuracy to varying degrees.

New Adaptive Approach for Road Condition Identification in ASR Control System

A novel tire-road adaptive model in longitude direction to formulate the dynamic characteristic between tire and road is proposed in this paper, based on this model, a new adaptive approach of road condition identification is presented to identify the model＇s parameters on-line in order to improve the performance of anti-slip regulation system（ASR）. The optimal slip is determined by using the drive wheel＇s slip and longitude traction force in ASR before the slipping of the drive wheel. Co-simulation is done based on the model for JETTA GTX building with ADAMS/CAR and Matlab, and results show that the adaptive model accords with Pacejka model very well. This adaptive model has simpler form, less number of parameters and higher adaptability than usual, and the new identification approach has a small amounts of operation, which is very suitful for ASR.

Identification of working conditions and prediction of FeO content in sintering process of iron ore fines

The iron oxide(FeO)content had a significant impact on both the metallurgical properties of sintered ores and the economic indicators of the sintering process.Precisely predicting FeO content possessed substantial potential for enhancing the quality of sintered ore and optimizing the sintering process.A multi-model integrated prediction framework for FeO content during the iron ore sintering process was presented.By applying the affinity propagation clustering algorithm,different working conditions were efficiently classified and the support vector machine algorithm was utilized to identify these conditions.Comparison of several models under different working conditions was carried out.The regression prediction model characterized by high precision and robust stability was selected.The model was integrated into the comprehensive multi-model framework.The precision,reliability and credibility of the model were validated through actual production data,yielding an impressive accuracy of 94.57%and a minimal absolute error of 0.13 in FeO content prediction.The real-time prediction of FeO content provided excellent guidance for on-site sinter production.

Modal Analysis Using Operating Modal Identification Method

Sometimes it is very difficult for some large-scale operating structures tomeasure the input forces. Modal parameters must be estimated on response-only. A poly-referencetime-domain operating modal identification complex exponential method is presented sincecross-correlation functions have the same form as impulse response functions. Then a poly-referencefrequency-domain operating modal identification method is proposed in this paper. An experiment onan aircraft model is performed to verify the proposed schemes. The results show that both outlinedschemes can extract the parameters from output-only and the modal parameters extracted by proposedfrequency-domain method are more accurate than those by presented time-domain complex exponentialmethod.

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Condition assessment of structures under unknown support excitation

A new method is proposed to assess the condition of structures under unknown support excitation by simultaneously detecting local damage and identifying the support excitation from several structural dynamic responses. The support excitation acting on a structure is modeled by orthogonal polynomial approximations, and the sensitivities of structural dynamic response with respect to its physical parameters and orthogonal coefficients are derived. The identification equation is based on Taylor＇s first order approximation, and is solved with the damped least-squares method in an iterative procedure. A fifteen-story shear building model and a five-story three-dimensional steel frame structure are studied to validate the proposed method. Numerical simulations with noisy measured accelerations show that the proposed method can accurately detect local damage and identify unknown support excitation from only several responses of the structure. This method provides a new approach for detecting structural damage and updating models with unknown input and incomplete measured output information.

Noise Source Identification Applied in Electric Power Industry Using Microphone Arrays

The noise source identification is an important issue in noise reduction and condition monitoring(CM) for machines in- site using microphone arrays. In this paper, we propose a new approach to optimize array configuration based on particles swarm optimization algorithm in order to improve noise source identification and condition monitoring performance. Two distinct optimized array configurations are designed under the certain conditions. Furthermore, an acoustic imaging equipment is developed to carry out experiments on transformer substation equipment and wind turbine generator, which demonstrate that the acoustic imaging system allows a high resolution in identifying main noise sources for noise reduction and abnormal noise sources for condition monitoring.

Finding the Time-dependent Term in 2D Heat Equation from Nonlocal Integral Conditions

The aim of this paper is to find the time-dependent term numerically in a two-dimensional heat equation using initial and Neumann boundary conditions and nonlocal integrals as over-determination conditions.This is a very interesting and challenging nonlinear inverse coefficient problem with important applications in various fields ranging from radioactive decay,melting or cooling processes,electronic chips,acoustics and geophysics to medicine.Unique solvability theo-rems of these inverse problems are supplied.However,since the problems are still ill-posed(a small modification in the input data can lead to bigger impact on the ultimate result in the output solution)the solution needs to be regularized.Therefore,in order to obtain a stable solution,a regularized objective function is minimized in order to retrieve the unknown coefficient.The two-dimensional inverse problem is discretized using the forward time central space(FTCS)finite-difference method(FDM),which is conditionally stable and recast as a non-linear least-squares minimization of the Tikhonov regularization function.Numerically,this is effectively solved using the MATLAB subroutine lsqnonlin.Both exact and noisy data are inverted.Numerical results for a few benchmark test examples are presented,discussed and assessed with respect to the FTCS-FDM mesh size discretisation,the level of noise with which the input data is contaminated,and the choice of the regularization parameter is discussed based on the trial and error technique.

Geochemical characteristics, forming conditions and resource evaluation of oil-cracking gas as ex-emplified by the platform area of the Tarim Basin

Based on the geochemical characteristics of oil-cracking gas and kerogen-cracking gas revealed by simu-lation experiments and the chemical composition of natural gases in actual gas reservoirs, two kinds of natural gases with different relationships between C2/C3 and C1/C2, C2/C3 and C1/C3, C2/C3 and 100×C1/(C1-C5) were identified in the Tarim Basin, and proposed further by the authors. The relationship charts of C2/C3 and C1/C2, C2/C3 and C1/C3, C2/C3 and 100×C1/(C1-C5) can be used to effectively distinguish oil-cracking gas from kerogen-cracking gas. Petro-leum geological analysis of the oil-cracking gas reservoirs showed that the distribution of oil-cracking gas is mostly related with deep-seated faults or faults with a large fault throw, and the burial depth of paleo-oil reservoir is rela-tively high; crude oil-cracking gas resources have been evaluated by using both forward and inversion methods. The plots of C2/C3 vs. C1/C2, C2/C3 vs. C1/C3, and C2/C3 vs. 100×C1/(C1-C5) were used to distinguish between oil-crack-ing gas and kerogen-cracking gas, and estimate the mixed ratios of the two kinds of natural gases in the main gas reservoirs of the platform area.

超长柔性悬挂固体充填刮板输送机异常工况表征及自主调控方法

固体充填刮板输送机是固体充填开采技术的关键装备,其高效智能化的程度制约着固体智能充填开采技术的进步,暂难突破超大采长、大功率、高可靠性的瓶颈,运行状态受地质条件、充填工艺等主控因素影响显著,异常工况自主调控问题亟待解决。总结了固体充填刮板输送机特征;构建了固体充填刮板输送机位姿形态与运输状态表征方法;通过分析异常工况的形成机理,选取异常工况判别指标,结合位姿与运输状态表征方法,给出了其空间位姿、牵引状态等异常工况判别准则与典型异常工况的调控路径,揭示了多工况状态下固体充填刮板输送机运载调控机制,提出了实时调控充填材料运输量、直线及水平程度组合的异常工况自主调控方法。全国首个260 m工作面长固体充填工程案例表明:通过异常工况自主调控,平均最大水平偏移距每减少100 mm,刮板链牵引力可减少85 kN,输送功率损耗可减少55 kW;平均最大垂直偏移距每减小100 mm时,输送功率损耗可减少7.2 kW;单位长度货载运量每减少100 kN/m,刮板链牵引力可减少31.9 kN,输送功率损耗可减少38.2 kW。所提出的异常工况自主调控方法可显著提升固体充填刮板输送机的输送效能,可助力实现固体智能充填开采。

改进和优化黑果枸杞及其制品中花青素测定的pH示差法

建立一种基于美国官方分析化学师协会(Association of Official Analytical Chemists,AOAC)方法检测黑果枸杞及其制品中花青素含量的改进pH示差法。考察了黑果枸杞及其制品中花青素的最佳提取和检测条件,通过液相色谱-三重四级杆串联质谱法鉴别出黑果枸杞中花青素的具体化学结构,并计算出混合花青素的平均摩尔质量。通过分光光度法测得混合花青素的平均摩尔消光系数,对改进后的pH示差法进行方法学验证和花青素的含量测定。结果显示,最佳提取和检测条件如下:黑果枸杞花青素提取溶剂为盐酸-80%(体积分数)乙醇(3∶97,体积比),料液比为1∶100(g∶mL),提取温度为50℃,提取时间为30 min,缓冲溶液稀释5倍后静置平衡20 min。液相色谱-三重四级杆串联质谱法鉴别黑果枸杞中主要以矮牵牛素类花青素为主(占97.96%),黑果枸杞特有的混合花青素平均摩尔质量为912.7 g/mol,平均摩尔消光系数为29591 L/(mol·cm)。pH示差法改进后能够满足方法学验证要求,固体样品和液体样品最低检出限分别为28.2 mg/100 g、0.282 mg/100 mL。方法改进后花青素提取增长率均大于20%,静置平衡20 min后单次检测结果精密度小于0.3%。以矮牵牛素类花青素代替矢车菊素-3-O-葡萄糖苷计算花青素含量平均提高了2.41倍,能真实地反映黑果枸杞及其制品中花青素的含量。

大型抽水蓄能发电电动机变压器组保护的研发与应用

针对抽水蓄能机组的特点,本文给出了发电电动机变压器组保护的配置方案;介绍了基于采样数据源分区的全工况保护算法,满足抽水蓄能机组保护算法的需求;提出了抽水蓄能机组工况综合判别方法,引入断路器、电气隔离开关以及监控信息的双位置辅助接点,同时设计了一种状态机机制、电气量辅助判别和开关位置报警功能,保证了抽水蓄能机组工况识别的准确性和可靠性;介绍了一种电气量和开入量综合判别的相序保护方案。研制的发电电动机变压器组保护装置已在300MW抽水蓄能机组投入应用,应用效果良好。

一株高效解钾菌的筛选、鉴定及培养条件的优化

以黑土水稻田中解钾菌为研究目标,共分离筛选出5株解钾菌,通过其形态特征以及测定菌株解钾能力筛选出1株高效解钾菌株S1。利用16S rDNA鉴定S1的种属,并通过单因素试验和正交试验优化该菌株的培养条件。结果表明,分离筛选得到的5株解钾菌均能有效分解钾长石,菌株S1解钾能力最强,解钾率为57.03%,发酵液中解钾量为3.852 mg/L,为最优解钾菌。初步鉴定解钾菌S1为伯克霍尔德菌(Burkholderia ambifaria)。经单因素试验和正交试验优化后的培养组分为碳源1.0%甘露醇、氮源1.0%蛋白胨、无机盐0.5%K_(2)HPO_(4),培养条件为温度30℃、时间48 h、培养基装液量(培养瓶250 mL)80 mL、初始pH 6.5、接种量5.0%。

基于随机邻域嵌入的无监督复杂工况识别

现代工业生产设备通常结构复杂并交替运行于不同工况,基于监测数据进行准确的工况识别是对系统进行健康监测的基础,但系统的监测数据通常维度较高、数据量较大。针对设备复杂工况的识别问题,提出了一种基于随机邻域嵌入的无监督工况识别方法。采用随机邻域嵌入算法,能够保留数据的局部和全局结构特性;计算了高维和低维空间中数据点的概率相似性,可实现设备高维监测数据的降维和无监督聚类,在不建立系统模型的基础上达成准确识别系统工况的目的。结果表明:该方法可有效实现高维监测数据的复杂工况识别,是一种有效的无监督聚类学习方法。

基于改进DeeplabV3+的水面多类型漂浮物分割方法研究

【目的】为解决传统图像处理方法鲁棒性差、常用深度学习检测方法无法准确识别大片漂浮物的边界等问题,【方法】提出一种基于改进DeeplabV3+的水面多类型漂浮物识别的语义分割方法,提高水面漂浮的识别能力。对所收集实际水面漂浮物进行分类,采用自制数据集进行对比试验。算法选择xception网络作为主干网络以获得初步漂浮物特征,在加强特征提取网络部分引入注意力机制以强调有效特征信息,在后处理阶段加入全连接条件随机场模型,将单个像素点的局部信息与全局语义信息融合。【结果】对比图像分割性能指标,改进后的算法mPA(Mean Pixel Accuracy)提升了5.73%,mIOU(Mean Intersection Over Union)提升了4.37%。【结论】相比于其他算法模型,改进后的DeeplabV3+算法对漂浮物特征的获取能力更强,同时能获得丰富的细节信息以更精准地识别多类型水面漂浮物的边界与较难分类的漂浮物,在对多个水库场景测试后满足实际水域环境中漂浮物检测的需求。

基于命名实体识别的水电工程施工安全规范实体识别模型

为准确识别水电工程施工安全规范实体,通过预训练模型中双向编码器表征法(BERT)挖掘文本中丰富的语义信息,利用双向长短期记忆神经网络(BILSTM)提取规范实体语义特征,依靠条件随机场(CRF)分析实体之间的依赖关系,构建水电工程施工安全规范的命名实体识别模型;以《水利水电工程施工安全防护技术规范》(SL714—2015)为例,计算命名实体识别模型精确率。结果表明:BERT-BILSTM-CRF模型准确率为94.35%,相比于3种传统方法,准确率显著提高。研究成果有助于水电工程施工安全规范知识智能管理,为施工安全隐患智能判别提供支撑。