Modeling Biosorption Of Cadmium, Zinc And Lead Onto Native And Immobilized Citrus Peels In Batch And Fixed Bed Reactors

Thesis (Ph.D.) University of Alaska Fairbanks, 2012Biosorption, i.e., the passive uptake of pollutants (heavy metals, dyes) from aqueous phase by biosorbents, obtained cheaply from natural sources or industrial/agricultural waste, can be a cost-effective alternative to conventional metal removal methods. Conventional methods such as chemical precipitation, membrane filtration or ion exchange are not suitable to treat large volumes of dilute discharge, such as mining effluent. This study is a continuation of previous research utilizing citrus peels for metal removal in batch reactors. Since fixed bed reactors feature better mass transfer and are typically used in water or waste water treatment using ion-exchange resins, this thesis focuses on packed bed columns. A number of fixed bed experiments were conducted by varying Cd inlet concentration (5-15 mg/L), bed height (24-75 cm) and flow rate (2-15.5 ml/min). Breakthrough and saturation uptake ranged between 14-29 mg/g and 42-45 mg/g respectively. An empty bed contact time of 10 minutes was required for optimum column operation. Breakthrough curves were described by mathematical models, whereby three popular models were shown to be mathematically identical. Citrus peels were immobilized within an alginate matrix to produce uniform granules with higher uptake capacity than raw peels. All breakthrough curves of native and immobilized peels were predicted using external and intra-particle mass transfer resistances from correlations and batch experiments, respectively. Several analogous mathematical models were identified; other frequently used models were shown to be the approximate derivatives of a single parent model. To determine the influence of competing metals, batch and fixed bed experiments were conducted in different binary combinations of Pb, Cd, Zn and Ca. Equilibrium data were analyzed by applying competitive, uncompetitive and partially competitive models. In column applications, high affinity Pb replaced previously bound Zn and Cd in Pb-Zn and Pb-Cd systems, respectively. However, the Cd-Zn system did not show any overshoot. Calcium, which is weakly bound, did not affect target metal binding as much as other metals. Saturated columns were desorbed with 0.1 N nitric acid to recover the metal, achieving concentration factors of 34-129. Finally, 5 g of citrus peels purified 5.40 L mining wastewater

Thermodynamic calculations using reverse Monte Carlo: Simultaneously tuning multiple short-range order parameters for 2D lattice adsorption problem

Lattice simulations are an important class of problems in crystalline solids, surface science, alloys, adsorption, absorption, separation, catalysis, to name a few. We describe a fast computational method for performing lattice thermodynamic calculations that is based on the use of the reverse Monte Carlo (RMC) technique and multiple short-range order (SRO) parameters. The approach is comparable in accuracy to the Metropolis Monte Carlo (MC) method. The equilibrium configuration is determined in 5-10 Newton-Raphson iterations by solving a system of coupled nonlinear algebraic flux equations. This makes the RMC-based method computationally more efficient than MC, given that MC typically requires sampling of millions of configurations. The technique is applied to the interacting 2D adsorption problem. Unlike grand canonical MC, RMC is found to be adept at tackling geometric frustration, as it is able to quickly and correctly provide the ordered c(2x2) adlayer configuration for Cl adsorbed on a Cu (100) surface.Comment: 34 pages, 10 figure

Reduced collinearity, low-dimensional cluster expansion model for adsorption of halides (Cl, Br) on Cu(100) surface using principal component analysis

The cluster expansion model (CEM) provides a powerful computational framework for rapid estimation of configurational properties in disordered systems. However, the traditional CEM construction procedure is still plagued by two fundamental problems: (i) even when only a handful of site cluster types are included in the model, these clusters can be correlated and therefore they cannot independently predict the material property, and (ii) typically few tens-hundreds of datapoints are required for training the model. To address the first problem of collinearity, we apply the principal component analysis method for constructing a CEM. Such an approach is shown to result in a low-dimensional CEM that can be trained using a small DFT dataset. We use the ab initio thermodynamic modeling of Cl and Br adsorption on Cu(100) surface as an example to demonstrate these concepts. A key result is that a CEM containing 10 effective cluster interactions build with only 8 DFT energies (note, number of training configurations > number of principal components) is found to be accurate and the thermodynamic behavior obtained is consistent with experiments. This paves the way for construction of high-fidelity CEMs with sparse/limited DFT data.Comment: 36 pages, 12 figure