Chemical looping combustion (CLC) is a technique for separating pure carbon dioxide from the combustion of fuels. The oxygen to burn the fuel comes from the lattice oxygen contained in solid particles of an inorganic oxide (the 'oxygen carrier'), instead of from oxygen in the air. Thus only CO2 and water leave the combustor, or fuel reactor. Next, the water is condensed, leaving pure CO2. The oxygen carrier is regenerated by oxidising it in air in a second reactor, called the air reactor. Accordingly, a stream of pure carbon dioxide can be produced, uncontaminated with gases such as nitrogen, normally present when the fuel burns in air. This intrinsic separation with CLC enables CO2 to be separated more efficiently than with other techniques, such as post-combustion scrubbing of carbon dioxide from stack gases with amine-based solvents. The design of a CLC system and its performance within an electricity system represents a multi-scale problem, ranging from the behaviour of single particles of oxygen carrier within a reactor to how a CLC-based power plant would perform in an electricity grid. To date, these scales have been studied in isolation, with little regard for the vital interactions and dependences amongst them. This Dissertation addresses this problem by considering CLC holistically for the first time, using a multi-scale approach. A stochastic model was developed, combining the particle-and reactor-scales of CLC. It included an appropriate particle model and can be coupled to a detailed reactor model. The combination represented a significant change from existing approaches, uniquely accounting for all the important factors affecting the assemblage of particles performing in the CLC reactors. It was used to determine the regimes of operation in which CLC is sensitive to factors such as the manner in which the particles are reacting, the residence time distribution of particles in the two reactors, the particle size distribution and the reaction history of particles. To demonstrate that the approach could simulate specific configurations of CLC, as well as a general system, the model was compared with results from experiments in which CLC with methane was conducted in a laboratory-scale circulating fluidised bed. The long-term performance of oxygen carrier materials is important, because, in an industrial process, they would be expected to function satisfactorily for many thousands of hours of operation. Long-term experiments were conducted to evaluate the resistance of different oxygen carrier materials to physical and chemical attrition. The evolution of their chemical kinetics was also determined. The results were used to evaluate the impact of different oxygen carrier materials in a fuel reactor at industrial-scale. Finally, a theoretical approach was developed to simulate how a fleet of CLC-based power plants would perform within the UK's national grid. By understanding how different parameters such as capital cost, operating cost and measures of efficiency, compared with other methods of generation offering carbon reduction, desirable design modifications and needs for improvement for CLC were identified by utilising the theoretical and experimental work conducted at the particle- and reactor-scales.

Remote sensors and unmanned aerial vehicles (UAVs) have the potential to dramatically improve infrastructure health monitoring in terms of accuracy of the information and frequency of data collection. UAV automation has made significant progress but that automation is also creating vast amounts of data that needs to be processed into actionable information. A key aspect of this work is the optimization (not just automation) of data collection from UAVs for targeted planning of mission objectives. This work investigates the use of camera planning for Structure from Motion for 3D modeling of infrastructure. Included in this thesis is a novel multi-scale view-planning algorithm for autonomous targeted inspection. The method presented reduced the number of photos needed and therefore reduced the processing time while maintaining desired accuracies across the test site. A second focus in this work investigates various set covering problem algorithms to use for selecting the optimal camera set. The trade-offs between solve time and quality of results are explored. The Carousel Greedy algorithm is found to be the best method for solving the problem due to its relatively fast solve speeds and the high quality of the solutions found. Finally, physical flight tests are used to demonstrate the quality of the method for determining coverage. Each of the set covering problem algorithms are used to create a camera set that achieves 95% coverage. The models from the different camera sets are comparable despite having a large amount of variability in the camera sets chosen. While this study focuses on multi-scale view planning for optical sensors, the methods could be extended to other remote sensors, such as aerial LiDAR.

Remote sensors and unmanned aerial vehicles (UAVs) have the potential to dramatically improve infrastructure health monitoring in terms of accuracy of the information and frequency of data collection. UAV automation has made significant progress but that automation is also creating vast amounts of data that needs to be processed into actionable information. A key aspect of this work is the optimization (not just automation) of data collection from UAVs for targeted planning of mission objectives. This work investigates the use of camera planning for Structure from Motion for 3D modeling of infrastructure. Included in this thesis is a novel multi-scale view-planning algorithm for autonomous targeted inspection. The method presented reduced the number of photos needed and therefore reduced the processing time while maintaining desired accuracies across the test site. A second focus in this work investigates various set covering problem algorithms to use for selecting the optimal camera set. The trade-offs between solve time and quality of results are explored. The Carousel Greedy algorithm is found to be the best method for solving the problem due to its relatively fast solve speeds and the high quality of the solutions found. Finally, physical flight tests are used to demonstrate the quality of the method for determining coverage. Each of the set covering problem algorithms are used to create a camera set that achieves 95% coverage. The models from the different camera sets are comparable despite having a large amount of variability in the camera sets chosen. While this study focuses on multi-scale view planning for optical sensors, the methods could be extended to other remote sensors, such as aerial LiDAR.

In this thesis, a hierarchical multiscale method was developed to predict rolling contact fatigue lives of mechanical systems. In the proposed multiscale method, the molecular modeling and simulation of lubricant was conducted to investigate the friction between rolling contact surfaces. The calculated friction coefficient was passed to the continuum model of rolling contact components to predict fatigue lives. Molecular dynamics modeling and simulation of thin film lubrication and lubricated contact surfaces were carried out to investigate mechanisms of hydrodynamic lubrication at nano-scale first. Although various lubricant alkane chains were considered in the molecular model, the chain length of eight united molecules were mainly employed in this thesis. In addition, the effects of temperature and nano-particles (debris) on the friction forces were discussed. It was found that the existing of nano-particles (debris) could increase the friction force between contact surfaces with hydrodynamic lubrication. In the continuum model of the developed multiscale method, finite element analysis was employed to predict rolling contact fatigue life of rolling contact components, including bearing and gear-tooth. Specifically, the fatigue crack initiation of bearing was studied, and then the fatigue crack initiation and propagation in gear-tooth. In addition, the enhancement of gear-tooth fatigue life by using composite patches was discussed as well. It should be noted that the friction coefficient used in the continuum model was calculated in the molecular model. It is one-way message passing in the developed multiscale method. Another continuum method was studied and developed in this thesis to provide alternate methods for the continuum model in the proposed multiscale framework. Peridynamics method has advantages in modeling and simulation of discontinuities, including cracks, over the conventional finite element methods. The applications of Peridynamics in predicting fatigue crack initiation and propagation lives were discussed in this thesis.

The following dissertation introduces the hazard of methane buildup in the gob zone, a caved region behind a retreating longwall face. This region serves as a reservoir for methane that can bleed into the mine workings. As this methane mixes with air delivered to the longwall panel, explosive concentrations of methane will be reached. Computational fluid dynamics (CFD) is one of the many approaches to study the gob environment. Several studies in the past have researched this topic and a general approach has been developed that addresses much of the complexity of the problem. The topic of research herein presents an improvement to the method developed by others. This dissertation details a multi-scale approach that includes the entire mine ventilation network in the computational domain. This allows one to describe these transient, difficult to describe boundaries. The gob region was represented in a conventional CFD model using techniques consistent with past efforts. The boundary conditions, however, were cross coupled with a transient network model of the balance of the ventilation airways. This allows the simulation of complex, time dependent boundary conditions for the model of the gob, including the influence of the mine ventilation system (MVS). The scenario modeled in this dissertation was a property in south western Pennsylvania, working in the Pittsburgh seam. A calibrated ventilation model was available as a result of a ventilation survey and tracer gas study conducted by NIOSH. The permeability distribution within the gob was based upon FLAC3d modeling results drawn from the literature. Using the multi-scale approach, a total of 22 kilometers of entryway were included in the computational domain, in addition to the three dimensional model of the gob. The steady state solution to the problem, modeling using this multi-scale approach, was validated against the results from the calibrated ventilation model. Close agreement between the two models was observed, with an average percent difference of less than two percent observed at points scattered throughout the MVS. Transient scenarios, including roof falls at key points in the MVS, were modeling to illustrate the impact on the gob environment.

To clarify the role of ab initio molecular dynamics (AIMD) simulation, this study organizes the McMillan-Mayer (MM) theorem, the potential distribution theorem, and quasi-chemical approach to provide theory for the thermodynamic effects associated with long-length scales. This multi-scale statistical mechanical (MSSM) theory implements quasi-chemical theory after utilizing the MM theorem to integrate-out the solvent degrees of freedom. The MSSM theory treats composition fluctuations which would be accessed by larger-scale calculations, and also long-ranged interactions of special interest for electrolyte solutions. The theory is applied to a primitive electrolyte solution model proposed to investigate ion pairing in the context of tetraethyammonium tetrafluoroborate in propylene carbonate. A Gaussian statistical model is shown to be an effective physical approximation for outer-shell contributions, and they are conclusive for the free energies within the quasi-chemical formulation. Gaussian statistical theory can be more effective than the Bennett numerically exact method when exhaustive sampling is not available, i.e., for finite samples. These results lead to the analysis of the asymptotical behavior of a relative information entropy and thus a new formula for the ion excess free energies. This asymptotic perspective completely avoids the computationally limiting evaluation of the outer-shell contributions. In addition, we use AIMD to obtain the charges on tetramethylammonium and tetrafluoroborate ions contacting neutral and charge carbon nanotube electrodes, and also charges tetraethyammonium and tetrafluoroborate ions in propylene carbonate solution. / acase@tulane.edu

Le lin (Linum usitatissimum L.) est une plante aux intérêts multiples. Sa tige est source de fibres, depuis longtemps utilisées dans le domaine du textile. Ce potentiel économique justifie la sélection variétale du lin en vue de développer des variétés plus riches en fibres et offrant une meilleure résistance aux maladies et la verse. Plus récemment, les fibres de lin ont vu leur utilisation s’étendre au renfort de matériaux composites grâce à leurs étonnantes propriétés mécaniques et morphologiques. Ces propriétés singulières s’expliquent grâce à leur développement et à leurs fonctions dans la tige. Ainsi, ce travail de thèse propose une caractérisation multi-échelle du lin, de la tige jusqu’à la paroi cellulaire de la fibre, afin de comprendre le lien entre les paramètres de croissance de la plante, le développement des fibres et leurs propriétés. L’architecture générale d’une tige de lin est explorée, ainsi que les conséquences de la sélection variétale sur cette structure et sur les propriétés des fibres. De plus, l’évolution des propriétés mécaniques des parois de fibres au cours de la croissance de la plante et de la phase de rouissage est caractérisée. En complément, la contribution des fibres à la rigidité en flexion d’une tige est mise en évidence, de même que leur rôle dans la résistance des tiges au flambage. Enfin, l’influence des conditions de culture sur les architectures des tiges et propriétés des fibres est étudiée par le biais de cultures en serre ou encore en simulant un phénomène de verse. Cette approche originale met en valeur les caractéristiques remarquables du lin qui en font un modèle de bioinspiration pour les matériaux composites de demain / Flax (Linum usitatissimum L.) is a plant with multiple interests. Its stem provides fibers, which have long been used in the textile industry. The economic potential of flax explains its varietal selection, aiming at developing varieties exhibiting higher fiber yields as well as greater resistance toward diseases and lodging. More recently, flax fibers have been dedicated to the reinforcement of composite materials due to their outstanding mechanical and morphological properties. These singular characteristics are related to fiber development and functions within the stem. Thus, the present work offers a multi-scale characterization of flax, from the stem to the fiber cell wall, in order to understand the link between plant growth parameters, the development of its fibers and their properties. The general architecture of a flax stem is investigated, as well as the impact of the varietal selection on this structure and on fiber performances. Moreover, changes in mechanical properties of fiber cell walls over plant growth and retting process are characterized. In addition, the fiber contribution to the stem stiffness is highlighted, as well as the fiber role in the resistance of the stem to buckling. The influence of culture conditions on stem architecture and fiber features is also studied through cultivations in greenhouse and by simulating a lodging event. This original approach emphasizes the uncommon characteristics of flax, which make this plant an instructive model toward future bioinspired composite materials.

Doctor of Philosophy (PhD) / The aim of this thesis is to understand what drives the evolution of industrial networks and how such understanding can be used to stimulate sustainable development. A complex adaptive systems perspective has been adopted to analyse the complex interaction between organisational behaviour and industrial network evolution. This analysis has formed the basis for the development of a modelling approach that allows for quantitative exploration of how different organisational perceptions about current and future uncertainty affect their behaviour and therefore the network evolution. This analysis results in a set of potential evolutionary pathways for an industrial network and their associated performance in terms of sustainable development. Subsequently, this modelling approach has been used to explore the consequences of interventions in the network evolution and to identify robust interventions for stimulating sustainable development of industrial networks. The analysis, modelling approach and development of interventions has been developed in the context of a bioenergy network in the region of KwaZulu-Natal in South Africa. Industrial networks are an important aspect of today’s life and provide many goods and services to households and individuals all over the world. They consist of a large number of autonomous organisations, where some organisations contribute by transforming or transacting natural resources, such as oil, agricultural products or water, while other organisations contribute to networks by providing information or setting regulation or subsidies (local or national governments) or by influencing decision making processes of other organisations in networks (advocacy groups). Throughout the process from natural resource to product or service, industrial networks have important economic, environmental and social impacts on the socio-economic and biophysical systems in which they operate. The sum of complex interactions between organisations affects the rate in which natural resources are used, environmental impacts associated with transformation and transaction of resources and social impacts on local communities, regions or countries as a whole. The aim of this thesis is to understand how industrial networks evolve and how they can be stimulated towards sustainable development. The first question that has been addressed in this thesis is how to understand the complex interaction between organisational behaviour and industrial network evolution. Organisational behaviour is affected by many functional and implicit characteristics within the environment in which the organisation operates, while simultaneously the environment is a function of non-linear relationships between individual organisational actions and their consequences for both the function and structure of the network. This thesis has identified four different characteristics of industrial networks that affect organisational behaviour: 1) Functional characteristics 2) Implicit behavioural characteristics 3) Implicit relational characteristics 4) Implicit network characteristics. Functional characteristics are those characteristics that are formally recognised by all organisations within an industrial network and which affect their position within the network. Examples of functional characteristics are the price and quantity of resources available, the location and distance of organisations within a network, infrastructure availability or regulation. Implicit characteristics, on the other hand, are those characteristics that impact the decision making process of organisations, but which are not formally part of the network. From an organisational perspective, implicit characteristics are the rules, heuristics, norms and values that an organisation uses to determine its objectives, position and potential actions. Implicit relational characteristics, most importantly trust and loyalty, affect an organisations choice between potential partners and implicit network characteristics are those social norms and values that emerge through social embeddedness. Collectively, these functional and implicit characteristics and their interactions determine the outcome of organisational decisions and therefore the direction of the industrial network evolution. The complex interaction between these large numbers of characteristics requires quantitative models to explore how different network characteristics and different interactions result in different network evolutions. This thesis has developed an agent-based simulation model to explore industrial network evolutions. To represent the multi-scale complexity of industrial networks, the model consists of four scales. Each scale represents different processes that connect the functional and implicit characteristics of an industrial network to each other. The two basic scales represent the strategic actions of the organisations on the one hand and the industrial network function and structure on the other. The third scale represents the processes that take place within the mental models of organisations describing how they make sense of their environment and inform their strategic decision making process. The fourth scale represents the social embeddedness of organisations and how social processes create and destroy social institutions. The model has been developed such that it allows for exploring how changes in different network characteristics or processes affect the evolution of the network as a whole. The second question that has been addressed in this thesis is how to evaluate sustainable development of different evolutionary pathways of industrial networks. First of all, a systems approach has been adopted to explore the consequences of an industrial network to the larger socio-economic and biophysical system in which the network operates. Subsequently, a set of structural indicators has been proposed to evaluate the dynamic performance of industrial networks. These four structural indicators reflect the efficiency, effectiveness, resilience and adaptiveness of industrial networks. Efficiency and effectiveness relate to the operational features by which industrial networks provides a particular contribution to society. Resilience and adaptiveness relate to the system’s capacity to maintain or adapt its contribution to society while under stress of temporary shocks or permanent shifts, respectively. Finally, different multi-criteria decision analysis (MCDA) tools have been applied to provide a holistic evaluation of sustainable development of industrial networks. The third important question that is addressed in this thesis is how to systematically explore the potential evolutionary pathways of an industrial network, which has led to the development of agent-based scenario analysis. Agent-based scenario analysis systematically explores how industrial network evolutions might evolve depending on the perceptions of organisations towards the inherent uncertainty associated with strategic decision making in networks. The agent-based scenario analysis consists of two steps. Firstly, analysts develop a set of coherent context scenarios, which represents their view on the context in which an industrial network will operate within the future. For a bioenergy network, for example, this step results in a set of scenarios that each represent a coherent future of the socio-economic system in which the network might evolve. The second step is the development of a set of ‘agent scenarios’. Each agent-based scenario is based on a different ‘mental model’ employed by organisations within the network about how to deal with the inherent ambiguity of the future. The organisational perspective towards uncertainty is of major importance for the evolution of industrial networks, because it determines the innovative behaviour of organisations, the structure of the network and the direction in which the network evolves. One the one hand, organisations can ignore future ambiguity and base their actions on the environment that they can observe in their present state. On the other extreme, organisations can adopt a view that the future is inherently uncertain and in which they view social norms and values more important than functional characteristics to make sense of their environment. The mental models are differentiated according to two dimensions: 1) different mental representation of the world and 2) different cognitive processes that can be employed to inform strategic actions. Along these dimensions, different processes can be employed to make sense of the environment and to inform decision making. The thesis has shown that by systematically exploring the different perceptions possible, an adequate understanding of the different evolutionary pathways can be gained to inform the evaluation and development of interventions to stimulate sustainable development. The final part of this thesis has applied the analysis and methodology developed throughout this thesis to a bioenergy network in the province of Kwazulu-Natal in South Africa. The bioenergy network consists of a set of existing sugar mills with large quantities of bagasse, a biomass waste product, available. Bagasse is currently burned inefficiently to produce steam for the sugar mills, but can potentially be used for the production of green electricity, biodiesel, bioethanol or gelfuel. All of these products have important consequences for the region in terms of associated reductions in CO2 emissions, electrification of and/or energy provision for rural households and local economic development of the region. This thesis has modelled strategic decisions of the sugar mills, the existing electricity generator, potential independent energy producers, local and national governments and how their actions and interactions can lead to different evolutionary pathways of the bioenergy network. The agent-based scenario analysis has been used to explore how different perceptions of organisations can lead to different network evolutions. Finally, the model has been used to explore the consequences of two categories of interventions on stimulating sustainable development. The conclusions are that both categories of interventions, financial interventions by national government and the introduction of multi-criteria decision analysis (MCDA) tools to aid strategic decision making, can have both positive and negative effects on the network evolutions, depending on what ‘mental models’ are employed by organisations. Furthermore, there is no single intervention that outperforms the others in terms of stimulating both functional and structural features of sustainable development. The final conclusion is that instead of focusing on individual or collective targets, emphasis should be placed on the development of interventions that focus on evolutionary aspects of industrial networks rather than functional performance criteria. This thesis has also highlighted interesting research questions for future investigation. The methodology developed in this thesis is applied to a single case study, but there are still many questions concerning how different industrial networks might benefit from different organisational perceptions towards uncertainty. Furthermore, the role between the mental models and sustainable development requires further investigation, especially in the light of globalisation and the interconnectiveness of industrial networks in different countries and continents. Finally, this methodology has provided a platform for investigating how new technologies might be developed that anticipate needs of future generations. This thesis has provided a first and important step in developing a methodology that addresses the complex issues associated with sustainable development, benefiting both academics and practitioners that aim to stimulate sustainable development.

Using a multi-scale experimental approach, the research undertaken in this thesis investigated the role of the flood pulse in ephemeral floodplain wetlands of the River Murray, in order to better understand the impact of river regulation (and permanent inundation) on these wetlands. An ecosystem-based experiment was conducted on the River Murray floodplain, to compare changes in nutrient availability and phytoplankton productivity in three ephemeral wetlands (over a drying/reflooding cycle) with three permanently inundated wetlands. In the ephemeral wetlands, both drying and re-flooding phases were associated with significant increases in nutrient availability and, in some cases, phytoplankton productivity. It was demonstrated that the ?flood pulse?, as described by the Flood Pulse Concept (FPC), can occur in ephemeral wetlands in dryland river-floodplain systems, although considerable variation in the nature of the pulse existed amongst these wetlands. Results of this experiment suggest that factors such as the degree of drying and length of isolation during the dry phase, the rate of re-filling, timing of re-flooding and the number of drying/re-flooding cycles may be potentially important in producing the variation observed. Permanent inundation of ephemeral wetlands effectively removed these periods of peak nutrient availability and phytoplankton productivity, resulting in continuously low levels (of nutrient availability and phytoplankton productivity). It was concluded that alteration of the natural hydrological cycle in this way can significantly reduce nutrient availability, primary production and secondary production, essentially changing the structure and function, the ecology, of these wetlands. Equally, the results of this experiment indicate that some of the changes resulting from river regulation and permanent inundation can be somewhat reversed, within a relatively short period of time, given re-instatement of a more natural hydrological regime. A mesocosm experiment was used to examine the influence of the dry phase, specifically the effect of the degree of wetland drying, on patterns of nutrient availability and primary productivity comprising the flood pulse. Compared to permanent inundation, re-flooding of completely desiccated sediments increased carbon (C) and nitrogen (N) availability while partial drying generally decreased, or had little effect on, C and N availability after re-flooding. However, degree of drying had little effect on phosphorus availability or rates of primary production measured after re-flooding, and it is possible that these two factors are related. Partial drying reduced rates of community respiration after reflooding, possibly a reflection of the reduced carbon concentrations measured in these mesocosms in this phase of the experiment. Degree of drying also influenced the macrophyte community (measured after three months of flooding), with plant biomass generally decreasing and species diversity increasing as the degree of drying increased (with the exception of complete sediment desiccation which had lasting negative effects on both macrophyte biomass and species diversity). The results of the ecosystem and mesocosm experiments were utilised, in addition to results collected from the same experiment conducted at two smaller scales (minicosms and microcosms), to assess whether the effects of hydrological regime on nutrient availability at the ?wetland? scale could be replicated in smaller-scale experiments. None of the smaller-scaled experiments included in this investigation were able to replicate the specific response to hydrological regime recorded at the ecosystem scale, however the mesocosm experiment did produce results that were more similar to those at the ecosystem scale than those produced by the mini and microcosm experiments. The results of this study indicated that extrapolation of results from small-scale experiments should be undertaken with caution, and confirmed that a multi-scale approach to ecological research is wise, where large-scale field experimentation and/or monitoring provides a check on the accuracy, and hence relevance, of conclusions reached via mesocosm experiments.

Estimation of effective diffusion coefficients is essential to be able to describe the diffusive transport of solutes in porous media. It has been shown in theory that in the case of uncharged porous materials the effective diffusion coefficient of solutes is a function of the pore morphology of the material and can be described by their tortuosity (tensor). To estimate the apparent diffusion coefficients, the values of tortuosity and porosity should be known first. In contrast with calculation of porosity, which can be easily obtained, estimation of tortuosity is intricate, particularly with increasing micro-geometry complexity in porous media. Moreover, many engineering materials (e.g, clays and shales) are characterized by electrical surface charges on particles of the porous material which can strongly affect the diffusive transport properties of ions. For these materials, estimation of effective diffusion coefficients have been mostly based on phenomenological equations with no link to underlying microscale properties of these charged materials although a few recent studies have used alternative methods to obtain the diffusion parameters. / In the first part of this thesis a numerical method based on a recently proposed up-scaled Poisson-Nernst-Planck type of equation (PNP) and its microscale counterpart is employed to estimate the tortuosity and thus the effective and apparent diffusion coefficients in thin charged membranes. Beside this, a new mathematical approach for estimation of tortuosity is applied and validated. This mathematical approach is also derived while upscaling of micro-scale Poisson-Nernst-Planck system of equations using the volume averaging method. A variety of different pore 2D and 3D micro-geometries together with different electrochemical conditions are studied here. To validate the new approaches, the relation between porosity and tortuosity has been obtained using a multi-scale approach and compared with published results. These include comparison with the results from a recently developed numerical method that is based on macro and micro-scale PNP equations. / Results confirm that the tortuosity value is the same for porous media with electrically uncharged and charged particles but only when using a consistent set of PNP equations. The effects of charged particles are captured by the ratio of average concentration to effective intrinsic concentration in the macroscopic PNP equations. Using this ratio allows to consistently take into account electro-chemical interactions of ions and charges on particles and so excludes any ambiguity generally encountered in phenomenological equations. / Steady-state diffusion studies dominate this thesis; however, understanding of transient ion transport in porous media is also important. The last section of this thesis briefly introduces transient diffusion through bentonite. To do so, the micro Nernst-Planck equation with electro-neutrality condition (NPE) is solved for a porous medium which consists of compacted bentonite. This system has been studied before in another research using an experimental approach and the results are available for both transient and steady-state phases. Three different conditions are assumed for NPE governing equations and then the numerical results from these three conditions are compared to the experimental values and analytical phenomenological solution. The tortuosity is treated as a fitting parameter and the effective diffusion coefficient can be calculated based on these tortuosity values. The results show that including a sorption term in the NPE equations can render similar results as the experimental values in transient and steady state phases. Also, as a fitting parameter, the tortuosity values were found varying with background concentration. This highlights the need to monitor multiple diffusing ion fluxes and membrane potential to fully characterize electro-diffusive transport from fundamental principles (which have been investigated in first part of this thesis) rather than phenomenological equations for predictive studies. / This research has lead to two different journal articles submissions, one already accepted in Computers and Geotechnics (October 22, 2009, 5-yrs Impact Factor 0.884) and the other one still under review.