Xylooligosaccharides Production from Alkali-Pretreated Sugarcane Bagasse Using Xylanases from Thermoascus aurantiacus

Sugarcane bagasse hemicellulose was isolated in a one-step chemical extraction using hydrogen peroxide in alkaline media. The polysaccharide containing 80.9% xylose and small amounts of L-arabinose, 4-O-methyl-D-glucuronic acid and glucose, was hydrolyzed by crude enzymatic extracts from Thermoascus aurantiacus at 50 degrees C. Conditions of enzymatic hydrolysis leading to the best yields of xylose and xylooligosaccharides (DP 2-5) were investigated using substrate concentration in the range 0.5-3.5% (w/v), enzyme load 40-80 U/g of the substrate, and reaction time from 3 to 96 h, applying a 22 factorial design. The maximum conversion to xylooligosaccharides (37.1%) was obtained with 2.6% of substrate and xylanase load of 60 U/g. The predicted maximum yield of xylobiose by a polynomial model was 41.6%. Crude enzymatic extract of T. aurantiacus generate from sugarcane bagasse hemicellulose 39% of xylose, 59% of xylobiose, and 2% of other xylooligosaccharides.

Biomimetic oxidative treatment of spruce wood studied by pyrolysis-molecular beam mass spectrometry coupled with multivariate analysis and (13)C-labeled tetramethylammonium hydroxide thermochemolysis: implications for fungal degradation of wood

In this work, pyrolysis-molecular beam mass spectrometry analysis coupled with principal components analysis and (13)C-labeled tetramethylammonium hydroxide thermochemolysis were used to study lignin oxidation, depolymerization, and demethylation of spruce wood treated by biomimetic oxidative systems. Neat Fenton and chelator-mediated Fenton reaction (CMFR) systems as well as cellulosic enzyme treatments were used to mimic the nonenzymatic process involved in wood brown-rot biodegradation. The results suggest that compared with enzymatic processes, Fenton-based treatment more readily opens the structure of the lignocellulosic matrix, freeing cellulose fibrils from the matrix. The results demonstrate that, under the current treatment conditions, Fenton and CMFR treatment cause limited demethoxylation of lignin in the insoluble wood residue. However, analysis of a water-extractable fraction revealed considerable soluble lignin residue structures that had undergone side chain oxidation as well as demethoxylation upon CMFR treatment. This research has implications for our understanding of nonenzymatic degradation of wood and the diffusion of CMFR agents in the wood cell wall during fungal degradation processes.

Search for optimum conditions of sugarcane bagasse hemicellulose extraction

A process has been elaborated for one-step low lignin content sugarcane bagasse hemicellulose extraction using alkaline solution of hydrogen peroxide. To maximize the hemicellulose yields several extraction conditions were examined applying the 2(4) factorial design: H(2)O(2) concentration from 2 to 6% (w/v), reaction time from 4 to 16 h, temperature from 20 to 60 degrees C, and magnesium sulfate absence or presence (0.5%, w/v). This approach allowed selection of conditions for the extraction of low and high lignin content hemicellulose. At midpoint the yield of hemicellulose was 94.5% with more than 88% of lignin removed. Lignin removal is suppressed at low extraction temperatures and in the absence of magnesium sulfate. Hemicellulose in 86% yield with low lignin content (5.9%) was obtained with 6% H(2)O(2) treatment for 4 h and 20 degrees C. This hemicellulose is much lighter in color than samples obtained at the midpoint condition and was found suitable for subsequent enzymatic hydrolysis. (C) 2009 Elsevier B.V. All rights reserved.

Aqueous two-phase extraction using thermoseparating copolymer: a new system for phenolic compounds removal from hemicelullosic hydrolysate

BACKGROUND: The hydrolysis of hemicellulosic material can provide liquor with high xylose concentration (which can be used as a fermentation medium) and phenolic compounds (Phs), potentially immunostimulating compounds. However, these hydrolysates must be detoxified in order to remove the Phs that can act as inhibitors in bioconversions. RESULTS: Aqueous two-phase systems composed of thermoseparating copolymers were used for rice straw hydrolysate detoxification. The hydrolysis process was able to promote chemical breakdown of 85% of the total hemicellulose content, 14% of the cellulose, and 2% of the lignin. The hydrolysate obtained contained 19.7 g L-1 of xylose and several phenolic compounds, such as vanillin, vanillic acid, ferullic acid, etc. The phenolics extraction was studied as a function of copolymer molar mass (1100 g mol(-1), 2000 g mol(-1) and 2800 g mol(-1)), their percentages (from 5% to 50%) and Phs initial concentration. Phenolic compounds extraction of around 80% was obtained under the following conditions: 20% (w/w) and 35% (w/w) copolymer 1100 g mol-1, 35% (w/w) copolymer 2000 g mol(-1) and 35% (w/w) copolymer 2800 g mol(-1) at 25 degrees C. CONCLUSIONS: The results demonstrated the viability of this method for the removal of Phs from rice straw hydrolysate, which has potential uses in bioconversion processes. (c) 2007 Society of Chemical Industry.

Human gait recognition using extraction and fusion of global motion features

This paper proposes a novel computer vision approach that processes video sequences of people walking and then recognises those people by their gait. Human motion carries different information that can be analysed in various ways. The skeleton carries motion information about human joints, and the silhouette carries information about boundary motion of the human body. Moreover, binary and gray-level images contain different information about human movements. This work proposes to recover these different kinds of information to interpret the global motion of the human body based on four different segmented image models, using a fusion model to improve classification. Our proposed method considers the set of the segmented frames of each individual as a distinct class and each frame as an object of this class. The methodology applies background extraction using the Gaussian Mixture Model (GMM), a scale reduction based on the Wavelet Transform (WT) and feature extraction by Principal Component Analysis (PCA). We propose four new schemas for motion information capture: the Silhouette-Gray-Wavelet model (SGW) captures motion based on grey level variations; the Silhouette-Binary-Wavelet model (SBW) captures motion based on binary information; the Silhouette-Edge-Binary model (SEW) captures motion based on edge information and the Silhouette Skeleton Wavelet model (SSW) captures motion based on skeleton movement. The classification rates obtained separately from these four different models are then merged using a new proposed fusion technique. The results suggest excellent performance in terms of recognising people by their gait.

Node-Depth Encoding and Multiobjective Evolutionary Algorithm Applied to Large-Scale Distribution System Reconfiguration

The power loss reduction in distribution systems (DSs) is a nonlinear and multiobjective problem. Service restoration in DSs is even computationally hard since it additionally requires a solution in real-time. Both DS problems are computationally complex. For large-scale networks, the usual problem formulation has thousands of constraint equations. The node-depth encoding (NDE) enables a modeling of DSs problems that eliminates several constraint equations from the usual formulation, making the problem solution simpler. On the other hand, a multiobjective evolutionary algorithm (EA) based on subpopulation tables adequately models several objectives and constraints, enabling a better exploration of the search space. The combination of the multiobjective EA with NDE (MEAN) results in the proposed approach for solving DSs problems for large-scale networks. Simulation results have shown the MEAN is able to find adequate restoration plans for a real DS with 3860 buses and 632 switches in a running time of 0.68 s. Moreover, the MEAN has shown a sublinear running time in function of the system size. Tests with networks ranging from 632 to 5166 switches indicate that the MEAN can find network configurations corresponding to a power loss reduction of 27.64% for very large networks requiring relatively low running time.

An algorithm for computerized automatic tuning of power system stabilizers

The main objective of this paper is to relieve the power system engineers from the burden of the complex and time-consuming process of power system stabilizer (PSS) tuning. To achieve this goal, the paper proposes an automatic process for computerized tuning of PSSs, which is based on an iterative process that uses a linear matrix inequality (LMI) solver to find the PSS parameters. It is shown in the paper that PSS tuning can be written as a search problem over a non-convex feasible set. The proposed algorithm solves this feasibility problem using an iterative LMI approach and a suitable initial condition, corresponding to a PSS designed for nominal operating conditions only (which is a quite simple task, since the required phase compensation is uniquely defined). Some knowledge about the PSS tuning is also incorporated in the algorithm through the specification of bounds defining the allowable PSS parameters. The application of the proposed algorithm to a benchmark test system and the nonlinear simulation of the resulting closed-loop models demonstrate the efficiency of this algorithm. (C) 2009 Elsevier Ltd. All rights reserved.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

A novel multiobjective optimization algorithm based on bacterial chemotaxis

In this article a novel algorithm based on the chemotaxis process of Echerichia coil is developed to solve multiobjective optimization problems. The algorithm uses fast nondominated sorting procedure, communication between the colony members and a simple chemotactical strategy to change the bacterial positions in order to explore the search space to find several optimal solutions. The proposed algorithm is validated using 11 benchmark problems and implementing three different performance measures to compare its performance with the NSGA-II genetic algorithm and with the particle swarm-based algorithm NSPSO. (C) 2009 Elsevier Ltd. All rights reserved.

A Constructive Genetic Algorithm for permutation flowshop scheduling

The general flowshop scheduling problem is a production problem where a set of n jobs have to be processed with identical flow pattern on in machines. In permutation flowshops the sequence of jobs is the same on all machines. A significant research effort has been devoted for sequencing jobs in a flowshop minimizing the makespan. This paper describes the application of a Constructive Genetic Algorithm (CGA) to makespan minimization on flowshop scheduling. The CGA was proposed recently as an alternative to traditional GA approaches, particularly, for evaluating schemata directly. The population initially formed only by schemata, evolves controlled by recombination to a population of well-adapted structures (schemata instantiation). The CGA implemented is based on the NEH classic heuristic and a local search heuristic used to define the fitness functions. The parameters of the CGA are calibrated using a Design of Experiments (DOE) approach. The computational results are compared against some other successful algorithms from the literature on Taillard`s well-known standard benchmark. The computational experience shows that this innovative CGA approach provides competitive results for flowshop scheduling; problems. (C) 2007 Elsevier Ltd. All rights reserved.

A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification

This study proposes a new PSOS-model based damage identification procedure using frequency domain data. The formulation of the objective function for the minimization problem is based on the Frequency Response Functions (FRFs) of the system. A novel strategy for the control of the Particle Swarm Optimization (PSO) parameters based on the Nelder-Mead algorithm (Simplex method) is presented; consequently, the convergence of the PSOS becomes independent of the heuristic constants and its stability and confidence are enhanced. The formulated hybrid method performs better in different benchmark functions than the Simulated Annealing (SA) and the basic PSO (PSO(b)). Two damage identification problems, taking into consideration the effects of noisy and incomplete data, were studied: first, a 10-bar truss and second, a cracked free-free beam, both modeled with finite elements. In these cases, the damage location and extent were successfully determined. Finally, a non-linear oscillator (Duffing oscillator) was identified by PSOS providing good results. (C) 2009 Elsevier Ltd. All rights reserved

Identification of new ozonation disinfection byproducts of 17 beta-estradiol and estrone in water

Estrogens are a class of micro-pollutants found in water at low concentrations (in the ng L(-1) range), but often sufficient to exert estrogenic effects due to their high estrogenic potency. Disinfection of waters containing estrogens through oxidative processes has been shown to lead to the formation of disinfection byproducts, which may also be estrogenic. The present work investigates the formation of disinfection byproducts of 17 beta-estradiol (E2) and estrone (E1) in the treatment of water with ozone. Experiments have been carried out at two different concentrations of the estrogens in ground water (100 ng L(-1) and 100 mu g L(-1)) and at varying ozone dosages (0-30 mg L(-1)). Detection of the estrogens and their disinfection byproducts in the water samples has been performed by means of ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) with a triple quadrupole (QqQ) and a quadrupole-time of flight (QqTOF) instrument. Both E2 and El have been found to form two main byproducts, with molecular mass (MM) 288 and 278 in the case of E2, and 286 and 276 in the case of El, following presumably the same reaction pathways. The E2 byproduct with MM 288 has been identified as 10epsilon-17beta-dihydroxy-1,4-estradieno-3-one (DEO), in agreement with previously published results. The molecular structures and the formation pathways of the other three newly identified byproducts have been suggested. These byproducts have been found to be formed at both high and low concentrations of the estrogens and to be persistent even after application of high ozone dosages. (C) 2011 Elsevier Ltd. All rights reserved.

Performance and molecular evaluation of an anaerobic system with suspended biomass for treating wastewater with high fat content after enzymatic hydrolysis

The effect of a lipase-rich fungal enzymatic preparation, produced by a Penicillium sp. during solid-state fermentation, was evaluated in an anaerobic digester treating dairy wastewater with 1200 mg of oil and grease/L The oil and grease hydrolysis step was carried out with 0.1% (w/v) of solid enzymatic preparation at 30 degrees C for 24 h, and resulted in a final free acid concentration eight times higher than the initial value. The digester operated in sequential batches of 48 h at 30 degrees C for 245 days, and had high chemical oxygen demand (COD) removal efficiencies (around 90%) when fed with pre-hydrolyzed wastewater. However, when the pre-hydrolysis step was removed, the anaerobic digester performed poorly (with an average COD removal of 32%), as the oil and grease accumulated in the biomass and effluent oil and grease concentration increased throughout the operational period. PCR-DGGE analysis of the Bacteria and Archaea domains revealed remarkable differences in the microbial profiles in trials conducted with and without the pre-hydrolysis step, indicating that differences observed in overall parameters were intrinsically related to the microbial diversity of the anaerobic sludge. (C) 2009 Elsevier Ltd. All rights reserved.

Morphological and molecular characterization of cyanobacteria from a Brazilian facultative wastewater stabilization pond and evaluation of microcystin production

The cyanobacterial population in the Cajati waste stabilization pond system (WSP) from Sao Paulo State, Brazil was assessed by cell isolation and direct microscope counting techniques. Ten strains, belonging to five genera (Synechococcus, Merismopedia, Leptolyngbya, Limnothrix, and Nostoc), were isolated and identified by morphological and molecular analyses. Morphological identification of the isolated strains was congruent with their phylogenetic analyses based on 16S rDNA gene sequences. Six cyanobacterial genera (Synechocystis, Aphanocapsa, Merismopedia, Lyngbya, Phormidium, and Pseudanabaena) were identified by direct microscope inspection. Both techniques were complementary, since, of the six genera identified by direct microscopic inspection, only Merismopedia was isolated, and the four other isolated genera were not detected by direct inspection. Direct microscope counting of preserved cells showed that cyanobacteria were the dominant members (> 90%) of the phytoplankton community during both periods evaluated (summer and autumn). ELISA tests specific for hepatotoxicmicrocystins gave positive results for six strains (Synechococcus CENA108, Merismopedia CENA106, Leptolyngbya CENA103, Leptolyngbya CENA112, Limnothrix CENA109, and Limnothrix CENA110), and for wastewater samples collected from raw influent (3.70 mu g microcystins/l) and treated effluent (3.74 mu g microcystins/l) in summer. Our findings indicate that toxic cyanobacteria in WSP systems are of concern, since the treated effluent containing cyanotoxins will be discharged into rivers, irrigation channels, estuaries, or reservoirs, and can affect human and animal health.

Application of an anaerobic packed-bed bioreactor for the production of hydrogen and organic acids

This study investigates the feasibility of an anaerobic bioreactor for treating low contents of organic matter to generate organic acids and hydrogen. The device employed for this purpose was a horizontal packed-bed bioreactor fed with glucose-based synthetic wastewater and operated with hydraulic retention times from 0.5 to 2 h. A microbial biofilm was developed without previous inoculation, using expanded clay beads (4.8-6.3 mm) as support material. Alkalinity was found to be the main parameter affecting the production of hydrogen and organic acids, and the system produced optimal output when operating without a buffer agent. The average hydrogen production was 2.48, 2.15 and 1.81 molH(2) mol(-1) of glucose for NaHCO3 influent concentrations of 0, 1000 and 2000 mg L-1, respectively. The operational regime of the bioreactor, the support material and the controlled alkalinity were effective in selecting and immobilizing microbial fermenting biofilms, which successfully produced hydrogen and organic acids throughout the operating period. Exploratory assays indicated the feasibility of organic acid extraction using an anionic polymeric resin. (C) 2007 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved.