Evaluation of the zinox and zeolite materials as adsorbents to remove H2S from natural gas

MELO, Dulce Maria de Araújo et al. Evaluation of the Zinox and Zeolite materials as adsorbents to remove H2S from natural gas. Colloids and Surfaces. A, Physicochemical and Engineering Aspects, Estados Unidos, v. 272, p. 32-36, 2006.

Structural Time Series Modeling: an Application to Environmental Variables

A structural time series model is one which is set up in terms of components which have a direct interpretation. In this paper, the discussion focuses on the dynamic modeling procedure based on the state space approach (associated to the Kalman filter), in the context of surface water quality monitoring, in order to analyze and evaluate the temporal evolution of the environmental variables, and thus identify trends or possible changes in water quality (change point detection). The approach is applied to environmental time series: time series of surface water quality variables in a river basin. The statistical modeling procedure is applied to monthly values of physico- chemical variables measured in a network of 8 water monitoring sites over a 15-year period (1999-2014) in the River Ave hydrological basin located in the Northwest region of Portugal.

Purificação, modelagem e simulação da adsorção de bioprodutos por troca iônica em coluna de leito expandido

A produção de proteínas através de microrganismos tornou-se uma técnica muito importante na obtenção de compostos de interesse da indústria farmacêutica e alimentícia. Extratos brutos nos quais as proteínas são obtidas são geralmente complexos, contendo sólidos e células em suspensão. Usualmente, para uso industrial destes compostos, é necessário obtê-los puros, para garantir a sua atuação sem interferência. Um método que vem recebendo destaque especialmente nos últimos 10 anos é o uso da cromatografia de troca iônica em leito expandido, que combina em uma única etapa os passos de clarificação, concentração e purificação da molécula alvo, reduzindo assim o tempo de operação e também os custos com equipamentos para realização de cada etapa em separado. Combinado a este fato, a última década também é marcada por trabalhos que tratam da modelagem matemática do processo de adsorção de proteínas em resinas. Está técnica, além de fornecer informações importantes sobre o processo de adsorção, também é de grande valia na otimização da etapa de adsorção, uma vez que permite que simulações sejam feitas, sem a necessidade de gasto de tempo e material com experimentos em bancada, especialmente se é desejado uma ampliação de escala. Dessa forma, o objetivo desta tese foi realizar a modelagem e simulação do processo de adsorção de bioprodutos em um caldo bruto na presença de células, usando inulinase e C-ficocianina como objeto de estudo e purificar C-ficocianina utilizando resina de troca iônica em leito expandido. A presente tese foi então dividida em quatro artigos. O primeiro artigo teve como objeto de estudo a enzima inulinase, e a otimização da etapa de adsorção desta enzima em resina de troca iônica Streamline SP, em leito expandido, foi feita através da modelagem matemática e simulação das curvas de ruptura em três diferentes graus de expansão (GE). As máximas eficiências foram observadas quando utilizadas maiores concentrações de inulinase (120 a 170 U/mL), e altura de leito entre 20 e 30 cm. O grau de expansão de 3,0 vezes foi considerado o melhor, uma vez que a produtividade foi consideravelmente superior. O segundo artigo apresenta o estudo das condições de adsorção de C-ficocianina em resina de troca iônica, onde foi verificado o efeito do pH e temperatura na adsorção e após construída a isoterma de adsorção. A isoterma de adsorção da C-ficocianina em resina Streamline Q XL feita em pH 7,5 e a 25°C (ambiente), apresentou um bom ajuste ao modelo de Langmuir (R=0,98) e os valores qm (capacidade máxima de adsorção) e Kd (constante de equilíbrio) estimados pela equação linearizada da isoterma, foram de 26,7 mg/mL e 0,067mg/mL. O terceiro artigo aborda a modelagem do processo de adsorção de extrato não clarificado de C-ficocianina em resina de troca iônica Streamline Q XL em coluna de leito expandido. Três curvas de ruptura foram feitas em diferentes graus de expansão (2,0, 2,5 e 3,0). A condição de adsorção de extrato bruto não clarificado de C-ficocianina que se mostrou mais vantajosa, por apresentar maior capacidade de adsorção, é quando se alimenta o extrato até atingir 10% de saturação da resina, em grau de expansão 2,0, com uma altura inicial de leito de 30 cm. O último artigo originado nesta tese foi sobre a purificação de C-ficocianina através da cromatografia de troca iônica em leito expandido. Uma vez que a adsorção já havia sido estudada no artigo 2, o artigo 4 enfoca na otimização das condições de eluição, visando obter um produto com máxima pureza e recuperação. A pureza é dada pela razão entre a absorbância a 620 nm pela absorbância a 280 nm, e dizse que quando C-ficocianina apresenta pureza superior a 0,7 ela pode ser usada em como corante em alimentos. A avaliação das curvas de contorno indicou que a faixa de trabalho deve ser em pH ao redor de 6,5 e volumes de eluição próximos a 150 mL. Tais condições combinadas a uma etapa de pré-eluição com 0,1M de NaCl, permitiu obter C-ficocianina com pureza de 2,9, concentração 3 mg/mL, e recuperação ao redor de 70%.

A Generalized Methodology to Characterize Composite Materials for Pyrolysis Models

The predictive capabilities of computational fire models have improved in recent years such that models have become an integral part of many research efforts. Models improve the understanding of the fire risk of materials and may decrease the number of expensive experiments required to assess the fire hazard of a specific material or designed space. A critical component of a predictive fire model is the pyrolysis sub-model that provides a mathematical representation of the rate of gaseous fuel production from condensed phase fuels given a heat flux incident to the material surface. The modern, comprehensive pyrolysis sub-models that are common today require the definition of many model parameters to accurately represent the physical description of materials that are ubiquitous in the built environment. Coupled with the increase in the number of parameters required to accurately represent the pyrolysis of materials is the increasing prevalence in the built environment of engineered composite materials that have never been measured or modeled. The motivation behind this project is to develop a systematic, generalized methodology to determine the requisite parameters to generate pyrolysis models with predictive capabilities for layered composite materials that are common in industrial and commercial applications. This methodology has been applied to four common composites in this work that exhibit a range of material structures and component materials. The methodology utilizes a multi-scale experimental approach in which each test is designed to isolate and determine a specific subset of the parameters required to define a material in the model. Data collected in simultaneous thermogravimetry and differential scanning calorimetry experiments were analyzed to determine the reaction kinetics, thermodynamic properties, and energetics of decomposition for each component of the composite. Data collected in microscale combustion calorimetry experiments were analyzed to determine the heats of complete combustion of the volatiles produced in each reaction. Inverse analyses were conducted on sample temperature data collected in bench-scale tests to determine the thermal transport parameters of each component through degradation. Simulations of quasi-one-dimensional bench-scale gasification tests generated from the resultant models using the ThermaKin modeling environment were compared to experimental data to independently validate the models.

A Modeling Approach to Quantify the Effects of Stomatal Behavior and Mesophyll Conductance on Leaf Water Use Efficiency

Water use efficiency (WUE) is considered as a determinant of yield under stress and a component of crop drought resistance. Stomatal behavior regulates both transpiration rate and net assimilation and has been suggested to be crucial for improving crop WUE. In this work, a dynamic model was used to examine the impact of dynamic properties of stomata on WUE. The model includes sub-models of stomatal conductance dynamics, solute accumulation in the mesophyll, mesophyll water content, and water flow to the mesophyll. Using the instantaneous value of stomatal conductance, photosynthesis, and transpiration rate were simulated using a biochemical model and Penman-Monteith equation, respectively. The model was parameterized for a cucumber leaf and model outputs were evaluated using climatic data. Our simulations revealed that WUE was higher on a cloudy than a sunny day. Fast stomatal reaction to light decreased WUE during the period of increasing light (e.g., in the morning) by up to 10.2% and increased WUE during the period of decreasing light (afternoon) by up to 6.25%. Sensitivity of daily WUE to stomatal parameters and mesophyll conductance to CO2 was tested for sunny and cloudy days. Increasing mesophyll conductance to CO2 was more likely to increase WUE for all climatic conditions (up to 5.5% on the sunny day) than modifications of stomatal reaction speed to light and maximum stomatal conductance.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Rejeito de xisto como adsorvente para remoção de fenol em águas produzidas na indústria de petróleo

Produced water is the main effluent linked to the activity of extraction of oil and their caring management is necessary due to the large volume involved, to ensure to minimize the negative impacts of discharges of these waters in the environment. This study aimed to analyze the use of retorted shale, which is a reject from the pyrolysis of pirobituminous shale, as adsorbent for the removal of phenols in produced water. The material was characterized by different techniques (grain sized analysis, thermal analysis, BET, FRX, FT-IR, XRD and SEM), showing the heterogeneity in their composition, showing its potential for the removal of varied compounds, as well as the phenols and their derivatives. For the analysis of the efficiency of the oil shale for the adsorption process, assays of adsorption balance were carried through, and also kinetic studies and dynamics adsorption, in the ETE of the UTPF of Petrobras, in Guamaré-RN. The balance assays shown a bigger conformity with the model of Langmuir and the kinetic model more adjusted to describe the adsorption of phenols in retorted shale was of pseudo-second order. The retorted shale presented a low capacity of adsorption of phenols (1,3mg/g), when related to others conventional adsorbents, however it is enough to the removal of these composites in concentrations presented in the produced water of the UTPF of Guamaré. The assays of dynamics adsorption in field had shown that the concentration of phenol in the effluent was null until reaching its rupture (58 hours). The results showed the possibility of use of the reject for removal of phenols in the final operations of the treatment process, removing as well, satisfactorily, the color and turbidity of the produced water, with more than 90% of removal

A NOVEL MODELING AND STATE OF CHARGE ESTIMATION SCHEME FOR LITHIUM-ION BATTERIES

Lithium Ion (Li-Ion) batteries have got attention in recent decades because of their undisputable advantages over other types of batteries. They are used in so many our devices which we need in our daily life such as cell phones, lap top computers, cameras, and so many electronic devices. They also are being used in smart grids technology, stand-alone wind and solar systems, Hybrid Electric Vehicles (HEV), and Plug in Hybrid Electric Vehicles (PHEV). Despite the rapid increase in the use of Lit-ion batteries, the existence of limited battery models also inadequate and very complex models developed by chemists is the lack of useful models a significant matter. A battery management system (BMS) aims to optimize the use of the battery, making the whole system more reliable, durable and cost effective. Perhaps the most important function of the BMS is to provide an estimate of the State of Charge (SOC). SOC is the ratio of available ampere-hour (Ah) in the battery to the total Ah of a fully charged battery. The Open Circuit Voltage (OCV) of a fully relaxed battery has an approximate one-to-one relationship with the SOC. Therefore, if this voltage is known, the SOC can be found. However, the relaxed OCV can only be measured when the battery is relaxed and the internal battery chemistry has reached equilibrium. This thesis focuses on Li-ion battery cell modelling and SOC estimation. In particular, the thesis, introduces a simple but comprehensive model for the battery and a novel on-line, accurate and fast SOC estimation algorithm for the primary purpose of use in electric and hybrid-electric vehicles, and microgrid systems. The thesis aims to (i) form a baseline characterization for dynamic modeling; (ii) provide a tool for use in state-of-charge estimation. The proposed modelling and SOC estimation schemes are validated through comprehensive simulation and experimental results.

A pro-active and dynamic prediction assistance using BaranC framework

Monitoring user interaction activities provides the basis for creating a user model that can be used to predict user behaviour and enable user assistant services. The BaranC framework provides components that perform UI monitoring (and collect all associated context data), builds a user model, and supports services that make use of the user model. In this case study, a Next-App prediction service is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic; it is dynamic both in responding to the current context, and also in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.

Dynamical modeling of biogas production supported by an experimental anaerobic digestion process

This paper presents the development of a combined experimental and numerical approach to study the anaerobic digestion of both the wastes produced in a biorefinery using yeast for biodiesel production and the wastes generated in the preceding microbial biomass production. The experimental results show that it is possible to valorise through anaerobic digestion all the tested residues. In the implementation of the numerical model for anaerobic digestion, a procedure for the identification of its parameters needs to be developed. A hybrid search Genetic Algorithm was used, followed by a direct search method. In order to test the procedure for estimation of parameters, first noise-free data was considered and a critical analysis of the results obtain so far was undertaken. As a demonstration of its application, the procedure was applied to experimental data.

Adsorption of amyloglucosidase from Aspergillus niger NRRL 3122 using ion exchange resin

Amyloglucosidase enzyme was produced by Aspergillus niger NRRL 3122 from solid-state fermentation, using deffated rice bran as substrate. The effects of process parameters (pH, temperature) in the equilibrium partition coefficient for the system amyloglucosidase - resin DEAE-cellulose were investigated, aiming at obtaining the optimum conditions for a subsequent purification process. The highest partition coefficients were obtained using 0.025M Tris-HCl buffer, pH 8.0 and 25ºC. The conditions that supplied the highest partition coefficient were specified, the isotherm that better described the amyloglucosidase process of adsorption obtained. It was observed that the adsorption could be well described by Langmuir equation and the values of Qm and Kd estimated at 133.0 U mL-1 and 15.4 U mL-1, respectively. From the adjustment of the kinetic curves using the fourth-order Runge-Kutta algorithm, the adsorption (k1) and desorption (k2) constants were obtained through optimization by the least square procedure, and the values calculated were 2.4x10-3 mL U-1 min-1 for k1 and 0.037 min-1 for k2 .

Multidisciplinary optimization of a catamaran's hydrofoil design with dynamic response surface and distributive processing

This paper presents a rational approach to the design of a catamaran's hydrofoil applied within a modern context of multidisciplinary optimization. The approach used includes the use of response surfaces represented by neural networks and a distributed programming environment that increases the optimization speed. A rational approach to the problem simplifies the complex optimization model; when combined with the distributed dynamic training used for the response surfaces, this model increases the efficiency of the process. The results achieved using this approach have justified this publication.

Phase diagram of a model for a binary mixture of nematic molecules on a Bethe lattice

We investigate the phase diagram of a discrete version of the Maier-Saupe model with the inclusion of additional degrees of freedom to mimic a distribution of rodlike and disklike molecules. Solutions of this problem on a Bethe lattice come from the analysis of the fixed points of a set of nonlinear recursion relations. Besides the fixed points associated with isotropic and uniaxial nematic structures, there is also a fixed point associated with a biaxial nematic structure. Due to the existence of large overlaps of the stability regions, we resorted to a scheme to calculate the free energy of these structures deep in the interior of a large Cayley tree. Both thermodynamic and dynamic-stability analyses rule out the presence of a biaxial phase, in qualitative agreement with previous mean-field results.

Piezoaeroelastic Modeling and Analysis of a Generator Wing with Continuous and Segmented Electrodes

Unmanned air vehicles (UAVs) and micro air vehicles (MAVs) constitute unique application platforms for vibration-based energy harvesting. Generating usable electrical energy during their mission has the important practical value of providing an additional energy source to run small electronic components. Electrical energy can be harvested from aeroelastic vibrations of lifting surfaces of UAVs and MAVs as they tend to have relatively flexible wings compared to their larger counterparts. In this work, an electromechanically coupled finite element model is combined with an unsteady aerodynamic model to develop a piezoaeroelastic model for airflow excitation of cantilevered plates representing wing-like structures. The electrical power output and the displacement of the wing tip are investigated for several airflow speeds and two different electrode configurations (continuous and segmented). Cancelation of electrical output occurs for typical coupled bending-torsion aeroelastic modes of a cantilevered generator wing when continuous electrodes are used. Torsional motions of the coupled modes become relatively significant when segmented electrodes are used, improving the broadband performance and altering the flutter speed. Although the focus is placed on the electrical power that can be harvested for a given airflow speed, shunt damping effect of piezoelectric power generation is also investigated for both electrode configurations.

Modeling and simulation of severe slugging in air-water pipeline-riser systems

A mathematical model, numerical simulations and stability and flow regime maps corresponding to severe slugging in pipeline riser systems, are presented. In the simulations air and water were used as flowing fluids. The mathematical model considers continuity equations for liquid and gas phases, with a simplified momentum equation for the mixture, neglecting inertia. A drift-flux model, evaluated for the local conditions in the riser, is used as a closure law. The developed model predicts the location of the liquid accumulation front in the pipeline and the liquid level in the riser, so it is possible to determine which type of severe slugging occurs in the system. The numerical procedure is convergent for different nodalizations. A comparison is made with experimental results corresponding to a catenary riser, showing very good results for slugging cycle and stability and flow regime maps. (c) 2010 Elsevier Ltd. All rights reserved.