Effect of Chlorine Concentration on Determining COD by Acid and Alkaline Potassium Permanganate

2013 ◽  
Vol 807-809 ◽  
pp. 232-235 ◽  
Author(s):  
Mo Di E ◽  
Tao Ding ◽  
Sheng Chao Zhan ◽  
Fu Wei Yao ◽  
Hong Ke Wan
Keyword(s):  
Potassium Permanganate ◽  
Water Body ◽  
Potassium Dichromate ◽  
Chloride Ion ◽  
Chlorine Concentration ◽  
Detection Methods ◽  
Tidal Period ◽  
Freshwater Sample ◽  
Standard Solution ◽  
The Difference

In water monitoring, effect of chloride ion in water body on determining COD can not be ignored. Potassium dichromate has a strong oxidizing, and it can oxidize most of the organic in matter. But it generally is used to determine the water body which COD is larger than 30mg/L, and easily influenced by Chloride ion. So we often use alkaline potassium permanganate method which has weak impact by Chloride ion in high chlorine concentration wastewater. For the tidal estuary, we often use acidic potassium permanganate method in upstream and alkaline potassium permanganate method in downstream. The different detection methods cause the disagreement of water quality evaluation. This paper describes the influence of chlorine level on the COD standard solution prepared by laboratory and river freshwater sample from Qiantang River upstream in detail, and analyses the difference between the acid potassium permanganate method and alkaline potassium permanganate by comparing the COD standard solution prepared by laboratory and the freshwater sample. Last, this paper discusses the variation of COD during a tidal period.

Elimination of outlier measurements for damage detection of structures under changing environmental conditions

2021 ◽  
pp. 147592172199847
Author(s):  
William Soo Lon Wah ◽  
Yining Xia
Keyword(s):  
Damage Detection ◽  
Multiple Regression ◽  
Natural Frequencies ◽  
Detection Method ◽  
Detection Methods ◽  
Structure Model ◽  
Bridge Structure ◽  
Beam Structure ◽  
Dependent Variables ◽  
The Difference

Damage detection methods developed in the literature are affected by the presence of outlier measurements. These measurements can prevent small levels of damage to be detected. Therefore, a method to eliminate the effects of outlier measurements is proposed in this article. The method uses the difference in fits to examine how deleting an observation affects the predicted value of a model. This allows the observations that have a large influence on the model created, to be identified. These observations are the outlier measurements and they are eliminated from the database before the application of damage detection methods. Eliminating the outliers before the application of damage detection methods allows the normal procedures to detect damage, to be implemented. A multiple-regression-based damage detection method, which uses the natural frequencies as both the independent and dependent variables, is also developed in this article. A beam structure model and an experimental wooden bridge structure are analysed using the multiple-regression-based damage detection method with and without the application of the method proposed to eliminate the effects of outliers. The results obtained demonstrate that smaller levels of damage can be detected when the effects of outlier measurements are eliminated using the method proposed in this article.

scholarly journals Single-input, multiple-output iterative algorithm for the calculation of volume, area, elevation, and shape using 3D topobathymetric models

2020 ◽  
Author(s):  
Hugo Luis Rojas-Villalobos ◽  
Blair Stringam ◽  
Zohrab Samani ◽  
Luis Carlos Alatorre Cejudo ◽  
Christopher Brown
Keyword(s):  
Water Body ◽  
Input Data ◽  
Reference Value ◽  
The Body ◽  
Digital Terrain ◽  
Terrain Models ◽  
Input Multiple Output ◽  
Single Input ◽  
The Difference ◽  
Area And Volume

Most methods for estimating the morphometric values of water bodies use equations derived from hypsographic curves or digital terrain models (DTMs) that relate depth, volume (V), and area (A) and that model the uncertainty inherent in the complex underwater morphology. This research focuses directly on the use of topobathymetric models that include the bathymetry and topography of the surrounding area next to the water body. The projection of the water surface height (H) on each DTM pixel generates a water column with intrinsic attributes such as volume and area. The process is replicated among all cells and estimates the total area and volume of the water body. If the V or A is the input data, an algorithm that iterates height values is used to generate the new data, which is compared with the entered value that functions as a reference. If the difference between the reference value and the calculated value is less than an error threshold, the iteration stops, and the maximum and average depths are calculated. The raster and the shape that represent the body of water are created. The cross comparison of H-V-A showed that there is an error between 0.0034% and 0.000039% when any of the parameters are used as input data. Performance tests determined that pixel dimensions are directly proportional to the processing time for each iteration. The results of the implementation of this algorithm were satisfactory since, for the DTM of Bustillos Lagoon, Chihuahua, Mexico, the simulation took less than 17 seconds in at most 22 iterations.

Potassium Permanganate and Potassium Dichrornate Standard Solutions

1965 ◽  
Vol 48 (4) ◽  
pp. 701-702
Author(s):  
Radmilo Ristich
Keyword(s):  
Potassium Permanganate ◽  
Potassium Dichromate ◽  
Methods Of Analysis ◽  
Official Methods Of Analysis ◽  
Standard Solutions

Abstract A review of Official Methods of Analysis revealed no revision necessary for the preparation and standardization of potassium dichromate solutions. Recommendations are made for the standardization of potassium permanganate solutions for three methods of analysis.

scholarly journals Defect Detection in Striped Images Using a One-Dimensional Median Filter

2020 ◽  
Vol 10 (3) ◽  
pp. 1012
Author(s):  
Wei-Chen Lee ◽  
Pei-Ling Tai
Keyword(s):  
Image Processing ◽  
Defect Detection ◽  
Median Filter ◽  
Detection Methods ◽  
Manufacturing Processes ◽  
Image Binarization ◽  
Image Processing Algorithm ◽  
One Dimensional ◽  
Modern Manufacturing ◽  
The Difference

Defect detection is a key element of quality assurance in many modern manufacturing processes. Defect detection methods, however, often involve a great deal of time and manual work. Image processing has become widely used as a means of reducing the required detection time and effort in manufacturing. To this end, this study proposes an image-processing algorithm for detecting defects in images with striped backgrounds—defect types include scratches and stains. In order to detect defects, the proposed method first pre-processes images and rotates them to align the stripes horizontally. Then, the images are divided into two parts: blocks and intervals. For the blocks, a one-dimensional median filter is used to generate defect-free images, and the difference between the original images and the defect-free images is calculated to find defects. For the intervals, defects are identified using image binarization. Finally, the method superposes the results found in the blocks and intervals to obtain final images with all defects marked. This study evaluated the performance of the proposed algorithm using 65 synthesized images and 20 actual images. The method achieved an accuracy of 97.2% based on the correctness of the defect locations. The defects that could not be identified were those whose greyscales were very close to those of the background.

scholarly journals Assessing the Self-Purification Capacity of Surface Waters in Lake Baikal Watershed

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1505 ◽  
Author(s):  
Mikhail Yu. Semenov ◽  
Yuri M. Semenov ◽  
Anton V. Silaev ◽  
Larisa A. Begunova
Keyword(s):  
Lake Baikal ◽  
Water Body ◽  
Removal Rate ◽  
Water Discharge ◽  
Contaminant Removal ◽  
Removal Rates ◽  
Selenga River ◽  
Removal Capacity ◽  
Polycyclic Aromatic ◽  
The Difference

The removal of trace metals (TM), dissolved organic carbon (DOC), mineral nitrogen (Nmin.), and polycyclic aromatic hydrocarbons (PAHs) from the water of Lake Baikal and its tributaries was evaluated. The contaminant removal rate (CRR) and the contaminant removal capacity (CRC) were used as water self-purification parameters. The CRR was calculated as the difference between contaminant mass flow rates at downstream and upstream gauging stations. The CRC was calculated as the quotient of the CRR and the change in water discharge between downstream and upstream gauging stations. Whether the CRR and CRC have positive or negative values depends on whether contaminant release or removal occurs in the water body. The CRR depends on the size of the water body. The lowest and the highest CRRs observed for Baikal were equal to −15 mg/s (PAHs) to −7327 g/s (DOC), whereas the highest PAH and DOC removal rates observed for Selenga River (the major Baikal tributary) in summer were equal to −9 mg/s and −3190 g/s correspondingly. The highest PAH and DOC removal rates observed for small tributaries were equal to 0.0004 mg/s and −0.7 g/s respectively. The amplitude of annual CRR oscillations depends on contaminant abundance. The highest amplitude was typical for most abundant contaminants such as Nmin. and DOC. In unpolluted sections of the Selenga River the highest rates of N and C removal (−85 g/s and −3190 g/s, respectively) were observed in summer and the lowest rates (4 g/s and 3869 g/s, respectively) were observed in the spring. The lowest amplitude was typical for PAHs and some low-abundance TM such as V and Ni. The highest summer rates of V and Ni removal were equal to −378 mg/s and −155 mg/s respectively, whereas lowest spring rates are equal to 296 mg/s and 220 mg/s. The intermediate CRR amplitudes were typical for most abundant TM such as Sr, Al, and Fe. The spatial CRR variability depends on water chemistry and the presence of pollution sources. The lowest (up to 38 g/s) rates of Nmin. removal was observed for polluted lower Selenga sections characterized by low water mineralization and high DOC concentrations. The highest rates (−85 g/s) were observed for unpolluted upper sections. Seepage loss from the river to groundwater was also recognized as an important means of contaminant removal. The CRC values depend mostly on water residence time. The DOC removing capacity value of Baikal (−26 g/m3) were lower than those of Selenga in summer (−35 g/m3) but higher than the CRCs of all tributaries during the other seasons (from 30 mg/m3 to −10 g/m3).

scholarly journals Development and detection of genetically modified crops

2020 ◽  
Vol 145 ◽  
pp. 01013
Author(s):  
Zhao Yu-jia ◽  
Fan Pei-lei ◽  
Liang Liang ◽  
Liu Yin-yin ◽  
Zhao Hai-bo ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Genetically Modified ◽  
Genetically Modified Crops ◽  
Detection Methods ◽  
Social Benefits ◽  
Nucleic Acid Detection ◽  
Genetically Modified Crop ◽  
The World ◽  
The Difference

Genetically modified crops (GMCs) have been known for the excellent qualities. The commercializing of GMCs has taken great economic and social benefits. However, the bio-security of GMCs was still an issue. To solve this problem, countries around the world were constantly strengthening regulations on planting, processing and detecting of GMCs. This paper reviewed the development of commercialization and detection of GMCs. The difference between protein and nucleic acid detection methods of genetically modified crop was further discussed. This paper will provide new insights for the application of genetically modified crops.

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