scholarly journals Image analysis procedure for studying Back-Diffusion phenomena from low-permeability layers in laboratory tests

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Fabio Tatti ◽  
Marco Petrangeli Papini ◽  
Massimo Raboni ◽  
Paolo Viotti
Keyword(s):  
Image Analysis ◽  
Laboratory Tests ◽  
Analysis Procedure ◽  
Low Permeability ◽  
Back Diffusion ◽  
Diffusion Phenomena

Contaminant back-diffusion from low-permeability layers as affected by groundwater velocity: A laboratory investigation by box model and image analysis

2018 ◽  
Vol 622-623 ◽  
pp. 164-171 ◽  
Author(s):  
Fabio Tatti ◽  
Marco Petrangeli Papini ◽  
Giuseppe Sappa ◽  
Massimo Raboni ◽  
Firoozeh Arjmand ◽  
...  
Keyword(s):  
Image Analysis ◽  
Laboratory Investigation ◽  
Box Model ◽  
Low Permeability ◽  
Back Diffusion ◽  
Groundwater Velocity

Field-scale forward and back diffusion through low-permeability zones

2017 ◽  
Vol 202 ◽  
pp. 47-58 ◽  
Author(s):  
Minjune Yang ◽  
Michael D. Annable ◽  
James W. Jawitz
Keyword(s):  
Low Permeability ◽  
Back Diffusion ◽  
Field Scale

Validation of a new image analysis procedure for quantifying filamentous bacteria in activated sludge

2014 ◽  
Vol 70 (6) ◽  
pp. 955-963 ◽  
Author(s):  
Ewa Liwarska-Bizukojc ◽  
Marcin Bizukojc ◽  
Olga Andrzejczak
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Activated Sludge ◽  
Analysis Procedure ◽  
Automated Image Analysis ◽  
Filamentous Bacteria ◽  
Image Processing And Analysis ◽  
Manual Counting ◽  
Analysis Application ◽  
Activated Sludge Systems

Quantification of filamentous bacteria in activated sludge systems can be made by manual counting under a microscope or by the application of various automated image analysis procedures. The latter has been significantly developed in the last two decades. In this work a new method based upon automated image analysis techniques was elaborated and presented. It consisted of three stages: (a) Neisser staining, (b) grabbing of microscopic images, and (c) digital image processing and analysis. This automated image analysis procedure possessed the features of novelty. It simultaneously delivered data about aggregates and filaments in an individual calculation routine, which is seldom met in the procedures described in the literature so far. What is more important, the macroprogram performing image processing and calculation of morphological parameters was written in the same software which was used for grabbing of images. Previously published procedures required using two different types of software, one for image grabbing and another one for image processing and analysis. Application of this new procedure for the quantification of filamentous bacteria in the full-scale as well as laboratory activated sludge systems proved that it was simple, fast and delivered reliable results.

Prediction of Hydraulic Conductivity of Fly Ash Built-in Mineral Sealing Layers

2017 ◽  
Author(s):  
Mariola Wasil
Keyword(s):  
Fly Ash ◽  
Hydraulic Conductivity ◽  
Laboratory Tests ◽  
Physical Parameters ◽  
Low Permeability ◽  
Empirical Formulas ◽  
Semi Empirical ◽  
Sealing Layer ◽  
Compaction Method

Mineral barrier protects the groundwater and soil from contamination by insulating the leakage of harmful substances from landfill. One of the most important parameters, which decides about usefulness of material to built-in sealing layers, is hydraulic conductivity. Researchers have conducted investigations with the possibility of utilising fly ash as a mineral sealing layer material, which is justified by its low permeability and other properties. It is known that laboratory tests of hydraulic conductivity are often long-term and require expensive equipment. Therefore, to avoid this, researchers trying to assess permeability of tested material with empirical or semi empirical formulas. The aim of the paper is to compare the results of hydraulic conductivity of fly ash obtained from the laboratory tests and from estimation using different empirical formulas. Fly ash was compacted by the Standard Proctor compaction method at the optimum moisture content. The results obtained from empirical equations were variable. It was observed that the Kozeny-Carman formula and other, based on a few physical parameters of the soil, gave better results in prediction of hydraulic conductivity of fly ash than equations based on only one parameter.

Development of a novel image analysis procedure to quantify biological porosity and illuvial clay in large soil thin sections

Geoderma ◽  
2017 ◽  
Vol 292 ◽  
pp. 135-148 ◽  
Author(s):  
Ophélie Sauzet ◽  
Cécilia Cammas ◽  
Jean Marc Gilliot ◽  
Manon Bajard ◽  
David Montagne
Keyword(s):  
Image Analysis ◽  
Analysis Procedure ◽  
Thin Sections ◽  
Large Soil

Use of image analysis for sludge characterisation: studying the relation between floc shape and sludge settleability

2006 ◽  
Vol 54 (1) ◽  
pp. 167-174 ◽  
Author(s):  
R. Jenné ◽  
E.N. Banadda ◽  
G. Gins ◽  
J. Deurinck ◽  
I.Y. Smets ◽  
...  
Keyword(s):  
Image Analysis ◽  
Principal Component ◽  
Analysis Procedure ◽  
Volume Index ◽  
Data Sets ◽  
Shape Descriptors ◽  
Automatic Image Analysis ◽  
Sludge Volume Index ◽  
Fully Automatic ◽  
Sludge Settleability

This paper starts by presenting a fully automatic image analysis procedure for characterisation of flocs and filaments in activated sludge images. Thereafter the attention is directed towards the results of four lab-scale experiments, in which image information is related to sludge settleability in terms of sludge volume index. This relation is statistically confirmed by applying a principal component analysis to the data. In addition, the redundancy in the data sets is studied with regard to floc shape descriptors and the monitoring potential of image analysis is demonstrated by means of a multiple linear regression exercise.

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