Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

Mahdi, Faiz M.; Holdich, Richard

doi:10.1080/01496395.2016.1232735

Cited by 14 publications

(8 citation statements)

References 40 publications

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“…The correlation between the water content and the dry unit weight is given in Figure 3. As a result of the standard compaction test, the optimal water content (w opt ) of the soil is found to be 30% and the dry unit weight (γ dmax ) is 12.4 kN/m 3 [40]. optimal water content (wopt) of the soil is found to be 30% and the dry unit weight (γdmax) is 12.4 kN/m 3 [40].…”

Section: Compaction Test Resultsmentioning

confidence: 97%

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Modelling the Effects of Nanomaterial Addition on the Permeability of the Compacted Clay Soil Using Machine Learning-Based Flow Resistance Analysis

et al. 2021

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Impermeable base layers that are made of materials with low permeability, such as clay soil, are necessary to prevent leachate in landfills from harming the environment. However, over time, the permeability of the clay soil changes. Therefore, to reduce and minimize the risk, the permeability-related characteristics of the base layers must be improved. Thus, this study aims to serve this purpose by experimentally investigating the effects of nanomaterial addition (aluminum oxide, iron oxide) into kaolin samples. The obtained samples are prepared by applying standard compaction, and the permeability of the soil sample is experimentally investigated by passing leachate from the reactors, in which these samples are placed. Therefore, Flow Resistance (FR) analysis is conducted and the obtained results show that the Al additives are more successful than the Fe additive in reducing leachate permeability. Besides, the concentration values of some polluting parameters (Chemical Oxygen Demand (COD), Total Kjeldahl Nitrogen (TKN), and Total Phosphorus (TP)) at the inlet and outlet of the reactors are analyzed. Three different models (Artificial Neural Networks (ANN), Multiple Linear Regression (MLR), Support Vector Machine (SVM)) are applied to the data obtained from the experimental study. The results have shown that polluting parameters produce high FR regression similarity rates (>75%), TKN, TP, and COD features are highly correlated with the FR value (>60%) and the most successful method is found to be the SVM model.

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Section: Compaction Test Resultsmentioning

confidence: 97%

“…Finally, Mahdi and Holdich's research predicts the permeability of loosely packed granular materials is made using ANN and MLR. One can see that ANN gives the best results [40].…”

Section: Introductionmentioning

confidence: 96%

Modelling the Effects of Nanomaterial Addition on the Permeability of the Compacted Clay Soil Using Machine Learning-Based Flow Resistance Analysis

et al. 2021

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“…This result may be related to a number of factors, including the shape of the particles or the enhanced contribution to resistance from the fine particles with the size distribution, as discussed by number of authors [11][12][13][14][15][16][17]. The particle shape and contribution of fines to the overall resistance is also being investigated using numerical models (ANN and MVR) to determine permeabilities [40]. It may also be the case that particle agglomeration may increase the tortuosity of permeation pathways and could contribute to the overestimation in the K-C approach.…”

Section: Resultsmentioning

confidence: 99%

A Study of Cake Filtration Parameters Using the Constant Rate Process

2019

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The minerals calcium carbonate and talc were filtered under various conditions of filtrate flow rate and suspension concentration, using constant rate conditions with the aid of a peristaltic pump to draw the filtrate. Cake concentrations of between 0.41 and 0.53 v/v for calcium carbonate and 0.19 and 0.26 v/v for talc were recorded. The mean sizes of the two different minerals were very similar, but the average specific resistances obtained from the experiments were 5.9 × 1010 and 7.4 × 1011 m/kg for calcium carbonate and talc, respectively. These results do not agree with what would be predicted from an analytical equation for permeability, such as Kozeny-Carman. In addition, discontinuities were observed in all cases on the curves of filtrate volume with time for the initial stage of filtration. This behaviour is attributed to retarded packing compressibility (RPC) complicating the analysis of the filter medium resistance. RPC is an important component in determining the filter cake resistance and its functionality with cake forming pressure. It is found that there are additional effects that enhance the resistance to permeation in different cake materials, which is not recognised in the standard analytical approaches. These complexities can be related to shape, polydispersity, or agglomeration within the material sample and not to the experimental equipment or procedure. Furthermore, a complete and straightforward methodology is presented in this work for investigating the significance, or otherwise, of medium resistance on the later stages of the filtration.

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“…In another relevant study by Farizhandi, Zhao & Lau (2016), a ML method was developed towards modelling the change in the distribution of particles in fluidized beds. ML methods have also been applied to predict the permeability of loosely packed granular systems based on experimental and literature data (Mahdi & Holdich 2017). More importantly, ML has been applied in an Euler-Euler fluid-particle coupling problem to improve a filtered two-fluid model by estimating a drag correction for coarse-mesh simulations (Jiang et al 2019).…”

Section: Introductionmentioning

confidence: 99%

High-resolution fluid–particle interactions: a machine learning approach

Davydzenka

Tahmasebi

2022

J. Fluid Mech.

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Modelling of fluid–particle interactions is a major area of research in many fields of science and engineering. There are several techniques that allow modelling of such interactions, among which the coupling of computational fluid dynamics (CFD) and the discrete element method (DEM) is one of the most convenient solutions due to the balance between accuracy and computational costs. However, the accuracy of this method is largely dependent upon mesh size, where obtaining realistic results always comes with the necessity of using a small mesh and thereby increasing computational intensity. To compensate for the inaccuracies of using a large mesh in such modelling, and still take advantage of rapid computations, we extended the classical modelling by combining it with a machine learning model. We have conducted seven simulations where the first one is a numerical model with a fine mesh (i.e. ground truth) with a very high computational time and accuracy, the next three models are constructed on coarse meshes with considerably less accuracy and computational burden and the last three models are assisted by machine learning, where we can obtain large improvements in terms of observing fine-scale features yet based on a coarse mesh. The results of this study show that there is a great opportunity in machine learning towards improving classical fluid–particle modelling approaches by producing highly accurate models for large-scale systems in a reasonable time.

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Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

Cited by 14 publications

References 40 publications

Modelling the Effects of Nanomaterial Addition on the Permeability of the Compacted Clay Soil Using Machine Learning-Based Flow Resistance Analysis

Modelling the Effects of Nanomaterial Addition on the Permeability of the Compacted Clay Soil Using Machine Learning-Based Flow Resistance Analysis

A Study of Cake Filtration Parameters Using the Constant Rate Process

High-resolution fluid–particle interactions: a machine learning approach

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