Geopolymeric adsorbents from fly ash for dye removal from aqueous solution

Adsorbents from coal fly ash treated by a solid-state fusion method using NaOH were prepared. It was found that amorphous aluminosilicate, geopolymers would be formed. These fly ash-derived inorganic polymers were assessed as potential adsorbents for removal of some basic dyes, methylene blue and crystal violet, from aqueous solution. It was found that the adsorption capacity of the synthesised adsorbents depends on the preparation conditions such as NaOH:fly-ash ratio and fusion temperature with the optimal conditions being at 121 weight ratio of Na:fly-ash at 250-350 degrees C. The synthesised materials exhibit much higher adsorption capacity than fly ash itself and natural zeolite. The adsorption isotherm can be fitted by Langmuir and Freundlich models while the two-site Langmuir model producing the best results. It was also found that the fly ash derived geopolymeric adsorbents show higher adsorption capacity for crystal violet than methylene blue and the adsorption temperature influences the adsorption capacity. Kinetic studies show that the adsorption process follows the pseudo second-order kinetics. (c) 2006 Elsevier Inc. All rights reserved.

Testing predictions of macroscopic binary diffusion coefficients using lattice models with site heterogeneity

Quantitatively predicting mass transport rates for chemical mixtures in porous materials is important in applications of materials such as adsorbents, membranes, and catalysts. Because directly assessing mixture transport experimentally is challenging, theoretical models that can predict mixture diffusion coefficients using Only single-component information would have many uses. One such model was proposed by Skoulidas, Sholl, and Krishna (Langmuir, 2003, 19, 7977), and applications of this model to a variety of chemical mixtures in nanoporous materials have yielded promising results. In this paper, the accuracy of this model for predicting mixture diffusion coefficients in materials that exhibit a heterogeneous distribution of local binding energies is examined. To examine this issue, single-component and binary mixture diffusion coefficients are computed using kinetic Monte Carlo for a two-dimensional lattice model over a wide range of lattice occupancies and compositions. The approach suggested by Skoulidas, Sholl, and Krishna is found to be accurate in situations where the spatial distribution of binding site energies is relatively homogeneous, but is considerably less accurate for strongly heterogeneous energy distributions.

Adsorption study for removal of basic red dye using bentonite

Colored wastewater poses a challenge to the conventional wastewater treatment techniques. Solid-liquid phase adsorption has been found to be effective for the removal of dyes from effluent. In this paper, the ability of bentonite as an adsorbent for the removal of a commercial dye, Basic Red 2 (BR2), from an aqueous solution has been investigated under various experimental conditions. The adsorption kinetics was shown to be pseudo-second-order. It was found that bentonite had high adsorption capacity for BR2 due to cation exchange. The adsorption equilibrium data can be fitted well by the Langmuir adsorption isotherm model. The effect of the experimental parameters, such as temperature, salt, and pH was investigated through a number of batch adsorption experiments. It was found that the removal of dye increased with the increase in solution pH. However, the change of temperature (15-45 degrees C) and the addition of sodium chloride were found to have little effect on the adsorption process. The results show that electrostatic interactions are not dominant in the interaction between BR2 and bentonite. It was found that the adsorption was a rapid process with 80-90% of the dye removed within the first 2-3 min. Bentonite as an adsorbent is promising for color removal from wastewater.

A mathematical model for dynamic efficiency using data envelopment analysis

In this paper we propose a data envelopment analysis (DEA) based method for assessing the comparative efficiencies of units operating production processes where input-output levels are inter-temporally dependent. One cause of inter-temporal dependence between input and output levels is capital stock which influences output levels over many production periods. Such units cannot be assessed by traditional or 'static' DEA which assumes input-output correspondences are contemporaneous in the sense that the output levels observed in a time period are the product solely of the input levels observed during that same period. The method developed in the paper overcomes the problem of inter-temporal input-output dependence by using input-output 'paths' mapped out by operating units over time as the basis of assessing them. As an application we compare the results of the dynamic and static model for a set of UK universities. The paper is suggested that dynamic model capture the efficiency better than static model. © 2003 Elsevier Inc. All rights reserved.

Liquid-phase adsorption of n-paraffins on type 5A molecular sieves

As a basis for the commercial separation of normal paraffins a detailed study has been made of factors affecting the adsorption of binary liquid mixtures of high molecular weight normal paraffins (C12, C16, and C20) from isooctane on type 5A molecular sieves. The literature relating to molecular sieve properties and applications, and to liquid-phase adsorption of high molecular weight normal paraffin compounds by zeolites, was reviewed. Equilibrium isotherms were determined experimentally for the normal paraffins under investigation at temperatures of 303oK, 323oK and 343oK and showed a non-linear, favourable- type of isotherm. A higher equilibrium amount was adsorbed with lower molecular weight normal paraffins. An increase in adsorption temperature resulted in a decrease in the adsorption value. Kinetics of adsorption were investigated for the three normal paraffins at different temperatures. The effective diffusivity and the rate of adsorption of each normal paraffin increased with an increase in temperature in the range 303 to 343oK. The value of activation energy was between 2 and 4 kcal/mole. The dynamic properties of the three systems were investigated over a range of operating conditions (i.e. temperature, flow rate, feed concentration, and molecular sieve size in the range 0.032 x 10-3 to 2 x 10-3m) with a packed column. The heights of adsorption zones calculated by two independent equations (one based on a constant width, constant velocity and adsorption zone and the second on a solute material balance within the adsorption zone) agreed within 3% which confirmed the validity of using the mass transfer zone concept to provide a simple design procedure for the systems under study. The dynamic capacity of type 5A sieves for n-eicosane was lower than for n-hexadecane and n-dodecane corresponding to a lower equilibrium loading capacity and lower overall mass transfer coefficient. The values of individual external, internal, theoretical and experimental overall mass transfer coefficient were determined. The internal resistance was in all cases rate-controlling. A mathematical model for the prediction of dynamic breakthrough curves was developed analytically and solved from the equilibrium isotherm and the mass transfer rate equation. The experimental breakthrough curves were tested against both the proposed model and a graphical method developed by Treybal. The model produced the best fit with mean relative percent deviations of 26, 22, and 13% for the n-dodecane, n-hexadecane, and n-eicosane systems respectively.

Atomistic simulation of zeolite surfaces

The aim of this paper is to describe the current state of atomistic simulation of zeolite surfaces by describing what has been achieved and to show how the surface structures are modelled. This is illustrated by using atomistic simulation techniques to model the {100} surface of zeolite LTA. The pure siliceous and aluminated CaNa-A and Na-A with Si/Al = 1 structures were considered. The surface showed three stable terminations but the relative stability varied with composition. The resulting surface structures and geometries show extensive framework distortions, especially in the aluminated forms where the cations formed strong interaction with the zeolite framework thereby increasing their adsorption energies and stabilising their cation position. © 2001 Published by Elsevier Science Ltd.

Dynamic Distribution Simulation Model Objects Based on Knowledge

This paper presents the process of load balancing in simulation system Triad.Net, the architecture of load balancing subsystem. The main features of static and dynamic load balancing are discussed and new approach, controlled dynamic load balancing, needed for regular mapping of simulation model on the network of computers is proposed. The paper considers linguistic constructions of Triad language for different load balancing algorithms description.

The calculation of dynamic ecological footprint on the basis of the dynamic input-output model

The Leontief input-output model is widely used to determine the ecological footprint of consumption in a region or a country. It is able to capture spillover environmental effects along the supply change, thus its popularity is increasing in ecology related economic research. These studies are static and the dynamic investigations are neglected. The dynamic Leontief model makes it possible to involve the capital and inventory investment in the footprint calculation that projects future growth of GDP and environmental impacts. We show a new calculation method to determine the effect of capital accumulation on ecological footprint. Keywords: Dynamic Leontief model, Dynamic ecological footprint, Environmental management, Allocation method

The efficiency of remanufacturing in a dynamic input–output model

For industrialised economy of ourdays, remanufacturing represents perhaps the largest unexploited resource and opportunity for realising a greater growth of the economy in an environmental-conscious manner. The aim of this paper is to investigate of the impact of remanufacturing in the economy from an economic-efficiency point of view. In static context this phenomenon was analysed in the literature. We use the multi-sector input–output framework in a dynamic context to study intra-period relationships of the sectors of economy. We extend the classical dynamic input–output model taking into consideration the activity of remanufacturing .We try to answer the question, whether the remanufacturing/reuse increases the growth possibility of an economy. We expose a sufficient condition concerning the effectivity of an economy with remanufacturing. By this evaluation we analyse a possible sustainable development of the economy on the basis of the product recovery management of industries.

Integrated surface-ground water modeling in wetlands with improved methods to simulate vegetative resistance to flow

In topographically flat wetlands, where shallow water table and conductive soil may develop as a result of wet and dry seasons, the connection between surface water and groundwater is not only present, but perhaps the key factor dominating the magnitude and direction of water flux. Due to their complex characteristics, modeling waterflow through wetlands using more realistic process formulations (integrated surface-ground water and vegetative resistance) is an actual necessity. This dissertation focused on developing an integrated surface – subsurface hydrologic simulation numerical model by programming and testing the coupling of the USGS MODFLOW-2005 Groundwater Flow Process (GWF) package (USGS, 2005) with the 2D surface water routing model: FLO-2D (O’Brien et al., 1993). The coupling included the necessary procedures to numerically integrate and verify both models as a single computational software system that will heretofore be referred to as WHIMFLO-2D (Wetlands Hydrology Integrated Model). An improved physical formulation of flow resistance through vegetation in shallow waters based on the concept of drag force was also implemented for the simulations of floodplains, while the use of the classical methods (e.g., Manning, Chezy, Darcy-Weisbach) to calculate flow resistance has been maintained for the canals and deeper waters. A preliminary demonstration exercise WHIMFLO-2D in an existing field site was developed for the Loxahatchee Impoundment Landscape Assessment (LILA), an 80 acre area, located at the Arthur R. Marshall Loxahatchee National Wild Life Refuge in Boynton Beach, Florida. After applying a number of simplifying assumptions, results have illustrated the ability of the model to simulate the hydrology of a wetland. In this illustrative case, a comparison between measured and simulated stages level showed an average error of 0.31% with a maximum error of 2.8%. Comparison of measured and simulated groundwater head levels showed an average error of 0.18% with a maximum of 2.9%. The coupling of FLO-2D model with MODFLOW-2005 model and the incorporation of the dynamic effect of flow resistance due to vegetation performed in the new modeling tool WHIMFLO-2D is an important contribution to the field of numerical modeling of hydrologic flow in wetlands.

Characterization of wood biochar and evaluation of its adsorption potential for removal of sulphate from produced water in offshore oil and gas industry

This thesis analyses the potential of wood biochar as an adsorbent in removal of sulphate from produced water. In worldwide offshore oil and gas industry, a large volume of waste water is generated as produced water. Sulphur compounds present in these produced water streams can cause environmental problems, regulatory problems and operational issues. Among the various sulphur removal technologies, the adsorption technique is considered as a suitable method since the design is simple, compact, economical and robust. Biochar has been studied as an adsorbent for removal of contaminants from water in a number of studies due to its low cost, potential availability, and adsorptive characteristics. In this study, biochar produced through fast pyrolysis of bark, hardwood sawdust, and softwood sawdust were characterized through a series of tests and were analysed for adsorbent properties. Treating produced water using biochar sourced from wood waste is a two-fold solution to environmental problems as it reduces the volume of these wastes. Batch adsorption tests were carried out to obtain adsorption capacities of each biochar sample using sodium sulphate solutions. The highest sulphur adsorption capacities obtained for hardwood char, softwood char and bark char were 11.81 mg/g, 9.44 mg/g, and 7.94 mg/g respectively at 10 °C and pH=4. The adsorption process followed the second order kinetic model and the Freundlich isotherm model. Adsorption reaction was thermodynamically favourable and exothermic. The overall analysis concludes that the wood biochar is a feasible, economical, and environmental adsorbent for removal of sulphate from produced water.

Sensor Planning for Bayesian Nonparametric Target Modeling

Bayesian nonparametric models, such as the Gaussian process and the Dirichlet process, have been extensively applied for target kinematics modeling in various applications including environmental monitoring, traffic planning, endangered species tracking, dynamic scene analysis, autonomous robot navigation, and human motion modeling. As shown by these successful applications, Bayesian nonparametric models are able to adjust their complexities adaptively from data as necessary, and are resistant to overfitting or underfitting. However, most existing works assume that the sensor measurements used to learn the Bayesian nonparametric target kinematics models are obtained a priori or that the target kinematics can be measured by the sensor at any given time throughout the task. Little work has been done for controlling the sensor with bounded field of view to obtain measurements of mobile targets that are most informative for reducing the uncertainty of the Bayesian nonparametric models. To present the systematic sensor planning approach to leaning Bayesian nonparametric models, the Gaussian process target kinematics model is introduced at first, which is capable of describing time-invariant spatial phenomena, such as ocean currents, temperature distributions and wind velocity fields. The Dirichlet process-Gaussian process target kinematics model is subsequently discussed for modeling mixture of mobile targets, such as pedestrian motion patterns.

Novel information theoretic functions are developed for these introduced Bayesian nonparametric target kinematics models to represent the expected utility of measurements as a function of sensor control inputs and random environmental variables. A Gaussian process expected Kullback Leibler divergence is developed as the expectation of the KL divergence between the current (prior) and posterior Gaussian process target kinematics models with respect to the future measurements. Then, this approach is extended to develop a new information value function that can be used to estimate target kinematics described by a Dirichlet process-Gaussian process mixture model. A theorem is proposed that shows the novel information theoretic functions are bounded. Based on this theorem, efficient estimators of the new information theoretic functions are designed, which are proved to be unbiased with the variance of the resultant approximation error decreasing linearly as the number of samples increases. Computational complexities for optimizing the novel information theoretic functions under sensor dynamics constraints are studied, and are proved to be NP-hard. A cumulative lower bound is then proposed to reduce the computational complexity to polynomial time.

Three sensor planning algorithms are developed according to the assumptions on the target kinematics and the sensor dynamics. For problems where the control space of the sensor is discrete, a greedy algorithm is proposed. The efficiency of the greedy algorithm is demonstrated by a numerical experiment with data of ocean currents obtained by moored buoys. A sweep line algorithm is developed for applications where the sensor control space is continuous and unconstrained. Synthetic simulations as well as physical experiments with ground robots and a surveillance camera are conducted to evaluate the performance of the sweep line algorithm. Moreover, a lexicographic algorithm is designed based on the cumulative lower bound of the novel information theoretic functions, for the scenario where the sensor dynamics are constrained. Numerical experiments with real data collected from indoor pedestrians by a commercial pan-tilt camera are performed to examine the lexicographic algorithm. Results from both the numerical simulations and the physical experiments show that the three sensor planning algorithms proposed in this dissertation based on the novel information theoretic functions are superior at learning the target kinematics with

little or no prior knowledge

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Freshwater Resource Supply Modeling for Developed and Undeveloped Watersheds

Globally, the current state of freshwater resource management is insufficient and impeding the chance at a sustainable future. Human interference within the natural hydrologic cycle is becoming dangerously irreversible and the need to redefine resource managerial approaches is imminent. This research involves the development of a coupled natural-human freshwater resource supply model using a System Dynamics approach. The model was applied to two case studies, Somalia, Africa and the Phoenix Active Management Area in Arizona, USA. It is suggested that System Dynamic modeling would be an invaluable tool for achieving sustainable freshwater resource management in individual watersheds. Through a series of thought experiments, a thorough understanding of the systems’ dynamic behaviors is obtainable for freshwater resource managers and policy-makers to examine various courses of action for alleviating freshwater supply concerns. This thesis reviews the model, its development and an analysis of several thought experiments applied to the case studies.

Evaluation of the zinox and zeolite materials as adsorbents to remove H2S from natural gas

MELO, Dulce Maria de Araújo et al. Evaluation of the Zinox and Zeolite materials as adsorbents to remove H2S from natural gas. Colloids and Surfaces. A, Physicochemical and Engineering Aspects, Estados Unidos, v. 272, p. 32-36, 2006.