Hydrogeological Properties of Gypseous soils in South Africa

2019 ◽  
Vol 122 (3) ◽  
pp. 389-396
Author(s):  
M. Greyling ◽  
J.L. Van Rooy
Keyword(s):  
South Africa ◽  
Particle Size ◽  
Hydraulic Conductivity ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Small Scale ◽  
Prepared Sample ◽  
Brine Sample ◽  
Gypsum Content ◽  
Hydrogeological Properties

Abstract Gypseous soils occur in the western arid and semi-arid regions of South Africa and Namibia. These soils exhibit a complex nature and abnormal behaviour due to their gypsum content and as such they have become more prevalent in research. As these soils are finding more use in industry, an astute understanding of their hydrogeological properties and behaviour is required. Powdery gypseous soil samples collected from the Northern Cape (Geelvloer) and Western Cape (Rooiberg and R355) Provinces, as well as a prepared sample, are subject to XRD analysis, particle size distribution determination and falling-head permeability tests using both water and brine. The testing served as preliminary research to guide further studies into the topic. The prepared sample, with 19% fines, comprises 35% gypsum and 65% sand. Geelvloer samples, with 91.95% gypsum content, are comprised mostly of sand-sized particles with 45% fines. Rooiberg samples contain 75% fines with a slightly lower gypsum content of 83.25%, while R355 samples have 50% fines with 75.35% gypsum. It is generally understood that particle size distribution contributes to the hydraulic conductivity of soils, where a higher portion fines will result in a lower conductivity. In the case of gypseous soils, the solubility is of importance as well, as it may have long term effects. With the intent of evaluating the effect of the aforementioned factors on the hydraulic conductivity of gypseous soils in South Africa, the samples taken represent differences in particle size distribution and origin. Geelvloer had k-values in the order of 8.82×10-6 m/s, with the brine sample giving 9.43×10-6 m/s, while the k-values for Rooiberg and R355 were in the order of 3.90×10-6 m/s and 5.87×10-6 m/s, respectively. The brine resulted in 5.63×10-6 m/s for Rooiberg and 9.90×10-6 m/s for the R355 sample. The made sample, having less fines, had k values in the order of 2.15×10-5 m/s, and 4.19×10-5 m/s for the brine. The differences between the results are largely negligible and show that despite what is believed to influence the hydraulic conductivity, in the case of gypseous soils in South Africa, on a small scale, it remained unaffected.

Relationship between the Hydraulic Conductivity Function and the Particle-Size Distribution

2003 ◽  
Vol 67 (1) ◽  
pp. 373
Author(s):  
Lalit M. Arya ◽  
Feike J. Leij ◽  
Peter J. Shouse ◽  
Martinus Th. van Genuchten
Keyword(s):  
Particle Size ◽  
Hydraulic Conductivity ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Conductivity Function

Simplified estimation of unsaturated soil hydraulic conductivity using bulk electrical conductivity and particle size distribution

Soil Research ◽  
2013 ◽  
Vol 51 (1) ◽  
pp. 23 ◽  
Author(s):  
Mohammad Reza Neyshabouri ◽  
Mehdi Rahmati ◽  
Claude Doussan ◽  
Boshra Behroozinezhad
Keyword(s):  
Electrical Conductivity ◽  
Particle Size ◽  
Hydraulic Conductivity ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Unsaturated Soil ◽  
Soil Core ◽  
Soil Hydraulic Conductivity ◽  
Core Samples ◽  
Soil Hydraulic

Unsaturated soil hydraulic conductivity K is a fundamental transfer property of soil but its measurement is costly, difficult, and time-consuming due to its large variations with water content (θ) or matric potential (h). Recently, C. Doussan and S. Ruy proposed a method/model using measurements of the electrical conductivity of soil core samples to predict K(h). This method requires the measurement or the setting of a range of matric potentials h in the core samples—a possible lengthy process requiring specialised devices. To avoid h estimation, we propose to simplify that method by introducing the particle-size distribution (PSD) of the soil as a proxy for soil pore diameters and matric potentials, with the Arya and Paris (AP) model. Tests of this simplified model (SM) with laboratory data on a broad range of soils and using the AP model with available, previously defined parameters showed that the accuracy was lower for the SM than for the original model (DR) in predicting K (RMSE of logK = 1.10 for SM v. 0.30 for DR; K in m s–1). However, accuracy was increased for SM when considering coarse- and medium-textured soils only (RMSE of logK = 0.61 for SM v. 0.26 for DR). Further tests with 51 soils from the UNSODA database and our own measurements, with estimated electrical properties, confirmed good agreement of the SM for coarse–medium-textured soils (<35–40% clay). For these textures, the SM also performed well compared with the van Genuchten–Mualem model. Error analysis of SM results and fitting of the AP parameter showed that most of the error for fine-textured soils came from poorer adequacy of the AP model’s previously defined parameters for defining the water retention curve, whereas this was much less so for coarse-textured soils. The SM, using readily accessible soil data, could be a relatively straightforward way to estimate, in situ or in the laboratory, K(h) for coarse–medium-textured soils. This requires, however, a prior check of the predictive efficacy of the AP model for the specific soil investigated, in particular for fine-textured/structured soils and when using previously defined AP parameters.

scholarly journals CFD simulation of solids suspension in stirred tanks: Review

2010 ◽  
Vol 64 (5) ◽  
pp. 365-374 ◽  
Author(s):  
Aoyi Ochieng ◽  
Mrice Onyango
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Concentration Distribution ◽  
Cfd Simulation ◽  
Fluid Dynamic ◽  
Cfd Modeling ◽  
Small Scale ◽  
Stirred Tanks ◽  
Solids Concentration

Many chemical reactions are carried out using stirred tanks, and the efficiency of such systems depends on the quality of mixing, which has been a subject of research for many years. For solid-liquid mixing, traditionally the research efforts were geared towards determining mixing features such as off-bottom solid suspension using experimental techniques. In a few studies that focused on the determination of solids concentration distribution, some methods that have been used have not been accurate enough to account for some small scale flow mal-distribution such as the existence of dead zones. The present review shows that computational fluid dynamic (CFD) techniques can be used to simulate mixing features such as solids off-bottom suspension, solids concentration and particle size distribution and cloud height. Information on the effects of particle size and particle size distribution on the solids concentration distribution is still scarce. Advancement of the CFD modeling is towards coupling the physical and kinetic data to capture mixing and reaction at meso- and micro-scales. Solids residence time distribution is important for the design; however, the current CFD models do not predict this parameter. Some advances have been made in recent years to apply CFD simulation to systems that involve fermentation and anaerobic processes. In these systems, complex interaction between the biochemical process and the hydrodynamics is still not well understood. This is one of the areas that still need more attention.

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