Victor R. Vasquez

Effect of thermal pretreatment on equilibrium moisture content of lignocellulosic biomass

The equilibrium moisture content (EMC) of raw lignocellulosic biomass, along with four samples subjected to thermal pretreatment, was measured at relative humidities ranging from 11% to 97% at a constant temperature of 30 °C. Three samples were prepared by treatment in hot compressed water by a process known as wet torrefaction, at temperatures of 200, 230, and 260 °C. An additional sample was prepared by dry torrefaction at 300 °C. Pretreated biomass shows EMC below that of raw biomass. This indicates that pretreated biomass, both dry and wet torrefied, is more hydrophobic than raw biomass. The EMC results were correlated with a recent model that takes into account additional non-adsorption interactions of water, such as mixing and swelling. The model offers physical insight into the water activity in lignocellulosic biomass.

Acetic acid and lithium chloride effects on hydrothermal carbonization of lignocellulosic biomass

As a renewable non-food resource, lignocellulosic biomass has great potential as an energy source or feedstock for further conversion. However, challenges exist with supply logistics of this geographically scattered and perishable resource. Hydrothermal carbonization treats any kind of biomass in 200 to 260°C compressed water under an inert atmosphere to produce a hydrophobic solid of reduced mass and increased fuel value. A maximum in higher heating value (HHV) was found when 0.4 g of acetic acid was added per g of biomass. If 1g of LiCl and 0.4 g of acetic acid were added per g of biomass to the initial reaction solution, a 30% increase in HHV was found compared to the pretreatment with no additives, along with greater mass reduction. LiCl addition also reduces reaction pressure. Addition of acetic acid and/or LiCl to hydrothermal carbonization each contribute to increased HHV and reduced mass yield of the solid product.

Reaction kinetics of hydrothermal carbonization of loblolly pine.

Hydrothermal carbonization (HTC) is a pretreatment process to convert diverse feedstocks to homogeneous energy-dense solid fuels. Understanding of reaction kinetics is necessary for reactor design and optimization. In this study, the reaction kinetics and effects of particle size on HTC were investigated. Experiments were conducted in a novel two-chamber reactor maintaining isothermal conditions for 15s to 30 min reaction times. Loblolly pine was treated at 200, 230, and 260°C. During the first few minutes of reaction, the solid-product mass yield decreases rapidly while the calorific value increases rapidly. A simple reaction mechanism is proposed and validated, in which both hemicellulose and cellulose degrade in parallel first-order reactions. Activation energy of hemicellulose and cellulose degradation were determined to be 30 and 73 kJ/mol, respectively. For short HTC times, both reaction and diffusion effects were observed.

Gas transfer in supported films made by molecular self-assembly of ionic polymers

Abstract Asymmetric membranes for gas separation were fabricated using the layer-by-layer adsorption process based on the spontaneous self-assembly of alternating layers of cationic and anionic polymers on porous and solid support membranes. The support membranes were first dipped in a dilute solution of poly (allylamine) (a polycation) followed by dipping in a dilute solution of poly (styrenesulfonate) (a polyanion). Repeating this process, 40 polymer layers were deposited on porous poly(propylene) membranes (Celgard 2400) and 100 layers on solid dimethyl silicone membranes. Gas-transfer experiments at several temperatures indicated reduced mass permeabilities due to the adsorbed films. Permeabilities of pure CO2 and N2 through coated Celgard samples were virtually equal at all temperatures indicating dominance of Knudsen diffusion through micropores in the film. Permeabilities in coated silicone membranes indicate higher CO2/N2 selectivity than the bare membrane at elevated temperatures. Microphotography indicated the presence of breaks in the polyion complex coating on the silicone membrane. Fabrication of gas transfer membranes via self-assembly of ionic polymers offers the possibility of designing highly selective membranes as well as the ability to control the thickness and architecture of films at the molecular level.

Pretreatment of rice hulls by ionic liquid dissolution

As a highly available waste product, rice hulls could be a starting block in replacing liquid fossil fuels. However, their silica covering can make further use difficult. This preliminary study investigates effects of dissolving rice hulls in the ionic liquids 1-ethyl-3-methylimidazolium acetate (EMIM Ac), 1-hexyl-3-methylimidazolium chloride, (HMIM Cl), and 1-allyl-3-methylimidazolium chloride (AMIM Cl), and what lignocellulosic components can be precipitated from the used ionic liquid with water and ethanol. EMIM Ac dissolution at 110 °C for 8 h was found to completely remove lignin from rice hulls, while ethanol was capable of precipitating lignin out of the used EMIM Ac. With 8h dissolution at 110 °C using HMIM Cl, approximately 20% of the cellulose in the rice hull sample can be precipitated out using water as co-solvent, while more than 60% of the hemicellulose can be precipitated with ethanol.

Ionic Concentration Effects on Reverse Micelle Size and Stability: Implications for the Synthesis of Nanoparticles

We present a systematic investigation and analysis of the structure and stability of reverse micelle systems with the addition of NH(4)OH, ZrOCl(2), and Al(NO(3))(3) salts. We demonstrate that the reverse micelle size decreases with increasing salt additions until one reaches a critical concentration, which characterizes the onset of system destabilization. The concept of an electrical double layer, as it applies to reverse micelles, is considered for explaining features of destabilization, including the initial decrease in reverse micelle size, the destabilization concentration, and the effect of cation valence. We propose that the reduction in size prior to instability is caused by compression of the reverse micelle electrical double layers, as higher concentrations of salts are present. The reduced thickness of the electrical double layers allows the decaying potentials to move into closer proximity to each other before generating enough repulsion to balance the forces for reverse micelle formation and form a new equilibrium average reverse micelle size. The point of reverse micelle instability has been related to the formation of a two-phase system as a result of the inability to further compress the salt co-ions in the core of the reverse micelles, which would cause an excessive repulsive force between the overlapping potentials. We have extracted a critical potential of -89 nV between the two overlapping potentials for the AOT/water/isooctane (ω(0) = 10) systems studied. All these effects have important implications for the preparation of nanopowders by reverse micelle synthesis. If the reverse micelles are unstable before the precipitates are formed, then the advantage of reverse micelle synthesis is immediately lost.

Helical structures from an isotropic homopolymer model

We present Monte Carlo simulation results for square-well homopolymers at a series of bond lengths. Although the model contains only isotropic pairwise interactions, under appropriate conditions this system shows spontaneous chiral symmetry breaking, where the chain exists in either a left- or a right-handed helical structure. We investigate how this behavior depends upon the ratio between bond length and monomer radius.

Techniques for assessing the effects of uncertainties in thermodynamic models and data

Thermodynamic models and experimental data exhibit the usual systematic and random errors. The severity of their errors depends on their use, such as for process calculations in a process simulator. Similarly, the value of better thermodynamic models and/or data should be measured with reference to such use. We have developed techniques for quantification of such thermodynamic-induced process uncertainties via Monte Carlo simulation, regression analyses, and analogies to optimization. The influence of experimental data sources and data types on the uncertainty of thermodynamic models is studied. Details and applications of our new sampling strategy (EPS), which accounts for the high degree of correlation between thermodynamics model parameters, is given. This procedure directly uses the regression results in a way that is much more powerful and mathematically accurate than traditional covariance matrix techniques. Level sets are used for the Monte Carlo samples so that unbiased accurate sampling of the entire feasible region is obtained. Comparison with traditional Monte Carlo sampling, Latin Hypercube sampling (LHS), and Shifted Hammersley sampling (SHS) are shown. The result is an unbiased estimate of uncertainties that reduces the over- and under-estimations common in traditional techniques. The approaches presented can be used for safety-factor/risk analysis, guidelines for simulator use, experimental design, and model comparisons. They allow determinations of the value of obtaining additional phase-equilibrium data and the potential value of improved phase-equilibrium models. Examples and case studies of these applications are provided.

Accounting for Both Random Errors and Systematic Errors in Uncertainty Propagation Analysis of Computer Models Involving Experimental Measurements With Monte Carlo Methods

A Monte Carlo method is presented to study the effect of systematic and random errors on computer models mainly dealing with experimental data. It is a common assumption in this type of models (linear and nonlinear regression, and nonregression computer models) involving experimental measurements that the error sources are mainly random and independent with no constant background errors (systematic errors). However, from comparisons of different experimental data sources evidence is often found of significant bias or calibration errors. The uncertainty analysis approach presented in this work is based on the analysis of cumulative probability distributions for output variables of the models involved taking into account the effect of both types of errors. The probability distributions are obtained by performing Monte Carlo simulation coupled with appropriate definitions for the random and systematic errors. The main objectives are to detect the error source with stochastic dominance on the uncertainty propagation and the combined effect on output variables of the models. The results from the case studies analyzed show that the approach is able to distinguish which error type has a more significant effect on the performance of the model. Also, it was found that systematic or calibration errors, if present, cannot be neglected in uncertainty analysis of models dependent on experimental measurements such as chemical and physical properties. The approach can be used to facilitate decision making in fields related to safety factors selection, modeling, experimental data measurement, and experimental design.

Regression of binary interaction parameters for thermodynamic models using an inside-variance estimation method (IVEM)

An inside-variance estimation method (IVEM) for binary interaction parameter regression in thermodynamic models is proposed. This maximum likelihood method involves the re-computation of the variance for each iteration of the optimization procedure, automatically re-weighting the objective function. Most of the maximum likelihood approaches currently used to regress the parameters of thermodynamic models fix the variances, converting the problem into a traditional weighted least squares minimization. However, such approaches lead to residual variances (between measured and calculated values) that are inconsistent with the fixed variances and, thus, do not necessarily produce optimum parameters for prediction purposes. The new method (IVEM) substantially improves fluid phase equilibria predictions (as shown by the examples presented) by maintaining consistency between the residual variances and the variance used in the objective function. This results in better parameter estimation and to a direct measure of the uncertainty in the model prediction.