Estimation of Mass Transfer Velocity Based on Measured Turbulence Parameters

The aim of this study is to quantify the mass transfer velocity using turbulence parameters from simultaneous measurements of oxygen concentration fields and velocity fields. The surface divergence model was considered in more detail, using data obtained for the lower range of beta (surface divergence). It is shown that the existing models that use the divergence concept furnish good predictions for the transfer velocity also for low values of beta, in the range of this study. Additionally, traditional conceptual models, such as the film model, the penetration-renewal model, and the large eddy model, were tested using the simultaneous information of concentration and velocity fields. It is shown that the film and the surface divergence models predicted the mass transfer velocity for all the range of the equipment Reynolds number used here. The velocity measurements showed viscosity effects close to the surface, which indicates that the surface was contaminated with some surfactant. Considering the results, this contamination can be considered slight for the mass transfer predictions. (C) 2009 American Institute of Chemical Engineers AIChE J, 56: 2005-2017; 2010

Changes in optical properties caused by UV-irradiation of aquatic humic substances from the amazon river basin: Seasonal variability evaluation

Aquatic humic substances (AHS) isolated from two characteristic seasons of the Negro river, winter and summer corresponding to floody and dry periods, were structurally characterized by (13)C nuclear magnetic ressonance. Subsequently, AHS aqueous solutions were irradiated with a polychromatic lamp (290-475 nm) and monitored by its total organic carbon (TOC) content, ultraviolet-visible (UV-vis) absorbance, fluorescence and Fourier transformed infrared spectroscopy (FTIR). As a result, a photobleaching upto 80% after irradiation of 48 h was observed. Conformational rearrangements and formation of low molecular complexity structures were formed during the irradiation, as deduced from the pH decrement and the fluorescence shifting to lower wavelengths. Additionally a significant mineralization with the formation Of CO(2), CO, and inorganic carbon compounds was registered, as assumed by TOC losses of up to 70%. The differences in photodegradation between samples expressed by photobleaching efficiency were enhanced in the summer sample and related to its elevated aromatic content. Aromatic structures are assumed to have high autosensitization capacity effects mediated by the free radical generation from quinone and phenolic moieties.

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of Sao Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting. (C) 2008 Elsevier B.V. All rights reserved.

A mechanistic kinetic model for phenol degradation by the Fenton process

The objective of this paper is to develop and validate a mechanistic model for the degradation of phenol by the Fenton process. Experiments were performed in semi-batch operation, in which phenol, catechol and hydroquinone concentrations were measured. Using the methodology described in Pontes and Pinto [R.F.F. Pontes, J.M. Pinto, Analysis of integrated kinetic and flow models for anaerobic digesters, Chemical Engineering journal 122 (1-2) (2006) 65-80], a stoichiometric model was first developed, with 53 reactions and 26 compounds, followed by the corresponding kinetic model. Sensitivity analysis was performed to determine the most influential kinetic parameters of the model that were estimated with the obtained experimental results. The adjusted model was used to analyze the impact of the initial concentration and flow rate of reactants on the efficiency of the Fenton process to degrade phenol. Moreover, the model was applied to evaluate the treatment cost of wastewater contaminated with phenol in order to meet environmental standards. (C) 2009 Elsevier B.V. All rights reserved.

Impact of guar and xanthan gums on proofing and calorimetric parameters of frozen bread dough

The influence of guar and xanthan gum and their combined use on dough proofing rate and its calorimetric properties was investigated. Fusion enthalpy, which is related to the amount of frozen water, was influenced by frozen dough formulation and storage time; specifically gum addition reduced the fusion enthalpy in comparison to control formulation, 76.9 J/g for formulation with both gums and 81.2 J/g for control, at 28th day. Other calorimetric parameters, such as T(g) and freezable water amount, were also influenced by frozen storage time. For all formulations, proofing rate of dough after freezing, frozen storage time and thawing, decreased in comparison to non-frozen dough, indicating that the freezing process itself was more detrimental to the proofing rate than storage time. For all formulations, the mean value of proofing rate was 2.97 +/- 0.24 cm(3) min(-1) per 100 g of non-frozen dough and 2.22 +/- 0.12 cm(3) min(-1) per 100 g of frozen dough. Also the proofing rate of non-frozen dough with xanthan gum decreased significantly in relation to dough without gums and dough with only guar gum. Optical microscopy analyses showed that the gas cell production after frozen storage period was reduced, which is in agreement with the proofing rate results. (C) 2008 Elsevier Ltd. All rights reserved.

Valve friction and nonlinear process model closed-loop identification

Among several process variability sources, valve friction and inadequate controller tuning are supposed to be two of the most prevalent. Friction quantification methods can be applied to the development of model-based compensators or to diagnose valves that need repair, whereas accurate process models can be used in controller retuning. This paper extends existing methods that jointly estimate the friction and process parameters, so that a nonlinear structure is adopted to represent the process model. The developed estimation algorithm is tested with three different data sources: a simulated first order plus dead time process, a hybrid setup (composed of a real valve and a simulated pH neutralization process) and from three industrial datasets corresponding to real control loops. The results demonstrate that the friction is accurately quantified, as well as ""good"" process models are estimated in several situations. Furthermore, when a nonlinear process model is considered, the proposed extension presents significant advantages: (i) greater accuracy for friction quantification and (ii) reasonable estimates of the nonlinear steady-state characteristics of the process. (C) 2010 Elsevier Ltd. All rights reserved.

An estimate of biometric parameters in eucalyptus clone plantations in Southern Bahia: an application of the airborne laser scanning (ALS) technology

The application of airborne laser scanning (ALS) technologies in forest inventories has shown great potential to improve the efficiency of forest planning activities. Precise estimates, fast assessment and relatively low complexity can explain the good results in terms of efficiency. The evolution of GPS and inertial measurement technologies, as well as the observed lower assessment costs when these technologies are applied to large scale studies, can explain the increasing dissemination of ALS technologies. The observed good quality of results can be expressed by estimates of volumes and basal area with estimated error below the level of 8.4%, depending on the size of sampled area, the quantity of laser pulses per square meter and the number of control plots. This paper analyzes the potential of an ALS assessment to produce certain forest inventory statistics in plantations of cloned Eucalyptus spp with precision equal of superior to conventional methods. The statistics of interest in this case were: volume, basal area, mean height and dominant trees mean height. The ALS flight for data assessment covered two strips of approximately 2 by 20 Km, in which clouds of points were sampled in circular plots with a radius of 13 m. Plots were sampled in different parts of the strips to cover different stand ages. The clouds of points generated by the ALS assessment: overall height mean, standard error, five percentiles (height under which we can find 10%, 30%, 50%,70% and 90% of the ALS points above ground level in the cloud), and density of points above ground level in each percentile were calculated. The ALS statistics were used in regression models to estimate mean diameter, mean height, mean height of dominant trees, basal area and volume. Conventional forest inventory sample plots provided real data. For volume, an exploratory assessment involving different combinations of ALS statistics allowed for the definition of the most promising relationships and fitting tests based on well known forest biometric models. The models based on ALS statistics that produced the best results involved: the 30% percentile to estimate mean diameter (R(2)=0,88 and MQE%=0,0004); the 10% and 90% percentiles to estimate mean height (R(2)=0,94 and MQE%=0,0003); the 90% percentile to estimate dominant height (R(2)=0,96 and MQE%=0,0003); the 10% percentile and mean height of ALS points to estimate basal area (R(2)=0,92 and MQE%=0,0016); and, to estimate volume, age and the 30% and 90% percentiles (R(2)=0,95 MQE%=0,002). Among the tested forest biometric models, the best fits were provided by the modified Schumacher using age and the 90% percentile, modified Clutter using age, mean height of ALS points and the 70% percentile, and modified Buckman using age, mean height of ALS points and the 10% percentile.

A Model for comparing reduced impact logging with conventional logging for an Eastern Amazonian Forest

Using data from a logging experiment in the eastern Brazilian Amazon region, we develop a matrix growth and yield model that captures the dynamic effects of harvest system choice on forest structure and composition. Multinomial logistic regression is used to estimate the growth transition parameters for a 10-year time step, while a Poisson regression model is used to estimate recruitment parameters. The model is designed to be easily integrated with an economic model of decisionmaking to perform tropical forest policy analysis. The model is used to compare the long-run structure and composition of a stand arising from the choice of implementing either conventional logging techniques or more carefully planned and executed reduced-impact logging (RIL) techniques, contrasted against a baseline projection of an unlogged forest. Results from log and leave scenarios show that a stand logged according to Brazilian management requirements will require well over 120 years to recover its initial commercial volume, regardless of logging technique employed. Implementing RIL, however, accelerates this recovery. Scenarios imposing a 40-year cutting cycle raise the possibility of sustainable harvest volumes, although at significantly lower levels than is implied by current regulations. Meeting current Brazilian forest policy goals may require an increase in the planned total area of permanent production forest or the widespread adoption of silvicultural practices that increase stand recovery and volume accumulation rates after RIL harvests. Published by Elsevier B.V.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil

A total of 152,145 weekly test-day milk yield records from 7317 first lactations of Holstein cows distributed in 93 herds in southeastern Brazil were analyzed. Test-day milk yields were classified into 44 weekly classes of DIM. The contemporary groups were defined as herd-year-week of test-day. The model included direct additive genetic, permanent environmental and residual effects as random and fixed effects of contemporary group and age of cow at calving as covariable, linear and quadratic effects. Mean trends were modeled by a cubic regression on orthogonal polynomials of DIM. Additive genetic and permanent environmental random effects were estimated by random regression on orthogonal Legendre polynomials. Residual variances were modeled using third to seventh-order variance functions or a step function with 1, 6,13,17 and 44 variance classes. Results from Akaike`s and Schwarz`s Bayesian information criterion suggested that a model considering a 7th-order Legendre polynomial for additive effect, a 12th-order polynomial for permanent environment effect and a step function with 6 classes for residual variances, fitted best. However, a parsimonious model, with a 6th-order Legendre polynomial for additive effects and a 7th-order polynomial for permanent environmental effects, yielded very similar genetic parameter estimates. (C) 2008 Elsevier B.V. All rights reserved.

Adapting the CROPGRO perennial forage model to predict growth of Brachiaria brizantha

Warm-season grasses are economically important for cattle production in tropical regions, and tools to aid in management and research of these forages would be highly beneficial. Crop simulation models synthesize numerous physiological processes and are important research tools for evaluating production of warm-season grasses. This research was conducted to adapt the perennial CROPGRO Forage model to simulate growth of the tropical species palisadegrass [Brachiaria brizantha (A. Rich.) Stapf. cv. Xaraes] and to describe model adaptation for this species. In order to develop the CROPGRO parameters for this species, we began with values and relationships reported in the literature. Some parameters and relationships were calibrated by comparison with observed growth, development, dry matter accumulation and partitioning during a 2-year experiment with Xaraes palisadegrass in Piracicaba, SP, Brazil. Starting with parameters for the bahiagrass (Paspalum notatum Flugge) perennial forage model, dormancy effects had to be minimized, and partitioning to storage tissue/root decreased, and partitioning to leaf and stem increased to provide for more leaf and stem growth and less root. Parameters affecting specific leaf area (SLA) and senescence of plant tissues were improved. After these changes were made to the model, biomass accumulation was better simulated, mean predicted herbage yield per cycle was 3573 kg ha(-1), with a RMSE of 538 kg DM ha(-1) (D-Stat = 0.838, simulated/observed ratio = 1.028). The results of the adaptation suggest that the CROPGRO model is an efficient tool to integrate physiological aspects of palisadegrass and can be used to simulate growth. (C) 2010 Elsevier B.V. All rights reserved.

A Log-Linear Regression Model for the Beta-Weibull Distribution

We introduce the log-beta Weibull regression model based on the beta Weibull distribution (Famoye et al., 2005; Lee et al., 2007). We derive expansions for the moment generating function which do not depend on complicated functions. The new regression model represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to censored survival data. We employ a frequentist analysis, a jackknife estimator, and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Further, for different parameter settings, sample sizes, and censoring percentages, several simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to evaluate the model assumptions. The extended regression model is very useful for the analysis of real data and could give more realistic fits than other special regression models.

Influence of a probiotic soy product on fecal microbiota and its association with cardiovascular risk factors in an animal model

Background: Previous work showed that daily ingestion of an aqueous soy extract fermented with Enterococcus faecium CRL 183 and Lactobacillus helveticus 416, supplemented or not with isoflavones, reduced the total cholesterol and non-HDL-cholesterol levels, increased the high-density lipoprotein (HDL) concentration and inhibited the raising of autoantibody against oxidized low-density lipoprotein (ox-LDL Ab) and the development of atherosclerotic lesions. Objective: The aim of this study was to characterize the fecal microbiota in order to investigate the possible correlation between fecal microbiota, serum lipid parameters and atherosclerotic lesion development in rabbits with induced hypercholesterolemia, that ingested the aqueous soy extract fermented with Enterococcus faecium CRL 183 and Lactobacillus helveticus 416. Methods: The rabbits were randomly allocated to five experimental groups (n = 6): control (C), hypercholesterolemic (H), hypercholesterolemic plus unfermented soy product (HUF), hypercholesterolemic plus fermented soy product (HF) and hypercholesterolemic plus isoflavone-supplemented fermented soy product (HIF). Lipid parameters and microbiota composition were analyzed on days 0 and 60 of the treatment and the atherosclerotic lesions were quantified at the end of the experiment. The fecal microbiota was characterized by enumerating the Lactobacillus spp., Bifidobacterium spp., Enterococcus spp., Enterobacteria and Clostridium spp. populations. Results: After 60 days of the experiment, intake of the probiotic soy product was correlated with significant increases (P < 0.05) on Lactobacillus spp., Bifidobacterium spp. and Enterococcus spp. and a decrease in the Enterobacteria population. A strong correlation was observed between microbiota composition and lipid profile. Populations of Enterococcus spp., Lactobacillus spp. and Bifidobacterium spp. were negatively correlated with total cholesterol, non-HDL-cholesterol, autoantibodies against oxidized LDL (ox-LDL Ab) and lesion size. HDL-C levels were positively correlated with Lactobacillus spp., Bifidobacterium spp., and Enterococcus spp. populations. Conclusion: In conclusion, daily ingestion of the probiotic soy product, supplemented or not with isoflavones, may contribute to a beneficial balance of the fecal microbiota and this modulation is associated with an improved cholesterol profile and inhibition of atherosclerotic lesion development.

In vitro characterization of rosiglitazone metabolites and determination of the kinetic parameters employing rat liver microsomal fraction

Rosiglitazone (RSG), a thiazolidinedione antidiabetic drug, is metabolized by CYP450 enzymes into two main metabolites: N-desmethyl rosiglitazone (N-Dm-R) and rho-hydroxy rosiglitazone (rho-OH-R). In humans, CYP2C8 appears to have a major role in RSG metabolism. On the other hand, the in vitro metabolism of RSG in animals has not been described in literature yet. Based on these concerns, the kinetic metabolism study of RSG using rat liver microsomal fraction is described for the first time. Maximum velocity (V (max)) values of 87.29 and 51.09 nmol/min/mg protein were observed for N-Dm-R and rho-OH-R, respectively. Michaelis-Menten constant (K (m)) values were of 58.12 and 78.52 mu M for N-Dm-R and rho-OH-R, respectively. Therefore, these results demonstrated that this in vitro metabolism model presents the capacity of forming higher levels of N-Dm-R than of rho-OH-R, which also happens in humans. Three other metabolites were identified employing mass spectrometry detection under positive electrospray ionization: ortho-hydroxy-rosiglitazone (omicron-OH-R) and two isomers of N-desmethyl hydroxy-rosiglitazone. These metabolites have also been observed in humans. The results observed in this study indicate that rats could be a satisfactory model for RSG metabolism.

A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively).