Dilute Acid Hydrolysis of Sugar Cane Bagasse at High Temperatures: A Kinetic Study of Cellulose Saccharification and Glucose Decomposition. Part I: Sulfuric Acid as the Catalyst

The kinetics of sugar cane bagasse cellulose saccharification and the decomposition of glucose under extremely low acid (ELA) conditions, (0.07%), 0.14%, and 0.28% H2SO4, and at high temperatures were investigated using batch reactors. The first-order rate constants were obtained by weight loss, remaining glucose, and fitting glucose concentration profiles determined with HPLC using the Saeman model. The maximum glucose yields reached 67.6% (200 degrees C, 0.07% H2SO4, 30 min), 69.8% (210 degrees C, 0.14% H2SO4, 10 min), and 67.3% (210 degrees C, 0.28% H2SO4, 6 min). ELA conditions produced remarkable glucose yields when applied to bagasse cellulose. The first-order rate constants were used to calculate activation energies and extrathermodynamic parameters to elucidate the reaction mechanism under ELA conditions. The effect of acid concentration on cellulose hydrolysis and glucose decomposition was also investigated. The observed activation energies and reaction orders with respect to hydronium ion for cellulose hydrolysis and glucose decomposition were 184.9 and 124.5 kJ/mol and 1.27 and 0.75, respectively.

Evaluating links through spectral decomposition

Spectral decomposition has rarely been used to investigate complex networks. In this work we apply this concept in order to define two kinds of link-directed attacks while quantifying their respective effects on the topology. Several other kinds of more traditional attacks are also adopted and compared. These attacks had substantially diverse effects, depending on each specific network (models and real-world structures). It is also shown that the spectrally based attacks have special effects in affecting the transitivity of the networks.

The importance of mesofauna and decomposition environment on leaf decomposition in three forests in southeastern Brazil

We examined the effects of soil mesofauna and the litter decomposition environment (above and belowground) on leaf decomposition rates in three forest types in southeastern Brazil. To estimate decomposition experimentally, we used litterbags with a standard substrate in a full-factorial experimental design. We used model selection to compare three decomposition models and also to infer the importance of forest type, decomposition environment, mesofauna, and their interactions on the decomposition process. Rather than the frequently used simple and double-exponential models, the best model to describe our dataset was the exponential deceleration model, which assumed a single organic compartment with an exponential decrease of the decomposition rate. Decomposition was higher in the wet than in the seasonal forest, and the differences between forest types were stronger aboveground. Regarding litter decomposition environment, decomposition was predominantly higher below than aboveground, but the magnitude of this effect was higher in the seasonal than in wet forests. Mesofauna exclusion treatments had slower decomposition, except aboveground into the Semi-deciduous Forest, where the mesofauna presence did not affect decomposition. Furthermore, the effect of mesofauna was stronger in the wet forests and belowground. Overall, our results suggest that, in a regional scale, both decomposers activity and the positive effect of soil mesofauna in decomposition are constrained by abiotic factors, such as moisture conditions.

Cash balance management: A comparison between genetic algorithms and particle swarm optimization

This work aimed to apply genetic algorithms (GA) and particle swarm optimization (PSO) in cash balance management using Miller-Orr model, which consists in a stochastic model that does not define a single ideal point for cash balance, but an oscillation range between a lower bound, an ideal balance and an upper bound. Thus, this paper proposes the application of GA and PSO to minimize the Total Cost of cash maintenance, obtaining the parameter of the lower bound of the Miller-Orr model, using for this the assumptions presented in literature. Computational experiments were applied in the development and validation of the models. The results indicated that both the GA and PSO are applicable in determining the cash level from the lower limit, with best results of PSO model, which had not yet been applied in this type of problem.

New Optimization Algorithms for Structural Reliability Analysis

Solution of structural reliability problems by the First Order method require optimization algorithms to find the smallest distance between a limit state function and the origin of standard Gaussian space. The Hassofer-Lind-Rackwitz-Fiessler (HLRF) algorithm, developed specifically for this purpose, has been shown to be efficient but not robust, as it fails to converge for a significant number of problems. On the other hand, recent developments in general (augmented Lagrangian) optimization techniques have not been tested in aplication to structural reliability problems. In the present article, three new optimization algorithms for structural reliability analysis are presented. One algorithm is based on the HLRF, but uses a new differentiable merit function with Wolfe conditions to select step length in linear search. It is shown in the article that, under certain assumptions, the proposed algorithm generates a sequence that converges to the local minimizer of the problem. Two new augmented Lagrangian methods are also presented, which use quadratic penalties to solve nonlinear problems with equality constraints. Performance and robustness of the new algorithms is compared to the classic augmented Lagrangian method, to HLRF and to the improved HLRF (iHLRF) algorithms, in the solution of 25 benchmark problems from the literature. The new proposed HLRF algorithm is shown to be more robust than HLRF or iHLRF, and as efficient as the iHLRF algorithm. The two augmented Lagrangian methods proposed herein are shown to be more robust and more efficient than the classical augmented Lagrangian method.

Formation of oxysulfide LnO(2)S(2) and oxysulfate LnO(2)SO(4) phases in the thermal decomposition process of lanthanide sulfonates (Ln = La, Sm)

This study investigates two lanthanide compounds (La(3+) and Sm(3+)) obtained in water/ethyl alcohol solutions employing the anionic surfactant diphenyl-4-amine sulfonate (DAS) as ligand. Both sulfonates were characterized through IR, TG/DTG (O(2) and N(2)). The thermal treatment of both compounds at 1273 K under air leaves residues containing variable percentages of lanthanide oxysulfide/oxysulfate phases shown by synchrotron high-resolution XRD pattern including the Rietveld analysis. The phase distributions found in the residues evidence the differences in the relative stability of the precursors.

Accelerating benders decomposition with heuristicmaster problem solutions

In this paper, a general scheme for generating extra cuts during the execution of a Benders decomposition algorithm is presented. These cuts are based on feasible and infeasible master problem solutions generated by means of a heuristic. This article includes general guidelines and a case study with a fixed charge network design problem. Computational tests with instances of this problem show the efficiency of the strategy. The most important aspect of the proposed ideas is their generality, which allows them to be used in virtually any Benders decomposition implementation.

Development of plurimetallic electrocatalysts prepared by decomposition of polymeric precursors for EtOH/O2 fuel cell

This work aimed to develop plurimetallic electrocatalysts composed of Pt, Ru, Ni, and Sn supported on C by decomposition of polymeric precursors (DPP), at a constant metal:carbon ratio of 40:60 wt.%, for application in direct ethanol fuel cell (DEFC). The obtained nanoparticles were physico-chemically characterized by X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDX). XRD results revealed a face-centered cubic crystalline Pt with evidence that Ni, Ru, and Sn atoms were incorporated into the Pt structure. Electrochemical characterization of the nanoparticles was accomplished by cyclic voltammetry (CV) and chronoamperometry (CA) in slightly acidic medium (0.05 mol L-1 H2SO4), in the absence and presence of ethanol. Addition of Sn to PtRuNi/C catalysts significantly shifted the ethanol and CO onset potentials toward lower values, thus increasing the catalytic activity, especially for the quaternary composition Pt64Sn15Ru13Ni8/C. Electrolysis of ethanol solutions at 0.4 V vs. RHE allowed determination of acetaldehyde and acetic acid as the main reaction products. The presence of Ru in alloys promoted formation of acetic acid as the main product of ethanol oxidation. The Pt64Sn15Ru13Ni8/C catalyst displayed the best performance for DEFC.

Reliability algorithms applied to reinforced concrete structures durability assessment

This paper addresses the analysis of probabilistic corrosion time initiation in reinforced concrete structures exposed to ions chloride penetration. Structural durability is an important criterion which must be evaluated in every type of structure, especially when these structures are constructed in aggressive atmospheres. Considering reinforced concrete members, chloride diffusion process is widely used to evaluate the durability. Therefore, at modelling this phenomenon, corrosion of reinforcements can be better estimated and prevented. These processes begin when a threshold level of chlorides concentration is reached at the steel bars of reinforcements. Despite the robustness of several models proposed in the literature, deterministic approaches fail to predict accurately the corrosion time initiation due to the inherently randomness observed in this process. In this regard, the durability can be more realistically represented using probabilistic approaches. A probabilistic analysis of ions chloride penetration is presented in this paper. The ions chloride penetration is simulated using the Fick's second law of diffusion. This law represents the chloride diffusion process, considering time dependent effects. The probability of failure is calculated using Monte Carlo simulation and the First Order Reliability Method (FORM) with a direct coupling approach. Some examples are considered in order to study these phenomena and a simplified method is proposed to determine optimal values for concrete cover.

Effects of low-salinity and high-turbidity waters one mpirical ocean colour algorithms: an example for Southwestern Atlantic waters

The spectral reflectance of the sea surface recorded using ocean colour satellite sensors has been used to estimate chlorophyll-a concentrations for decades. However, in bio-optically complex coastal waters, these estimates are compromised by the presence of several other coloured components besides chlorophyll, especially in regions affected by low-salinity waters. The present work aims to (a) describe the influence of the freshwater plume from the La Plata River on the variability of in situ remote sensing reflectance and (b) evaluate the performance of operational ocean colour chlorophyll algorithms applied to Southwestern Atlantic waters, which receive a remarkable seasonal contribution from La Plata River discharges. Data from three oceanographic cruises are used, in addition to a historical regional bio-optical dataset. Deviations found between measured and estimated concentrations of chlorophyll-a are examined in relation to surface water salinity and turbidity gradients to investigate the source of errors in satellite estimates of pigment concentrations. We observed significant seasonal variability in surface reflectance properties that are strongly driven by La Plata River plume dynamics and arise from the presence of high levels of inorganic suspended solids and coloured dissolved materials. As expected, existing operational algorithms overestimate the concentration of chlorophyll-a, especially in waters of low salinity (S<33.5) and high turbidity (Rrs(670)>0.0012 sr−1). Additionally, an updated version of the regional algorithm is presented, which clearly improves the chlorophyll estimation in those types of coastal environment. In general, the techniques presented here allow us to directly distinguish the bio-optical types of waters to be considered in algorithm studies by the ocean colour community.

A contribution for developing more efficient dynamic modelling algorithms of parallel robots

Parallel kinematic structures are considered very adequate architectures for positioning and orienti ng the tools of robotic mechanisms. However, developing dynamic models for this kind of systems is sometimes a difficult task. In fact, the direct application of traditional methods of robotics, for modelling and analysing such systems, usually does not lead to efficient and systematic algorithms. This work addre sses this issue: to present a modular approach to generate the dynamic model and through some convenient modifications, how we can make these methods more applicable to parallel structures as well. Kane’s formulati on to obtain the dynamic equations is shown to be one of the easiest ways to deal with redundant coordinates and kinematic constraints, so that a suitable c hoice of a set of coordinates allows the remaining of the modelling procedure to be computer aided. The advantages of this approach are discussed in the modelling of a 3-dof parallel asymmetric mechanisms.