Toxic metal recovery from spent hydroprocessing catalyst

Spent hydroprocessing catalysts (HPCs) are solid wastes generated in refinery industries and typically contain various hazardous metals, such as Co, Ni, and Mo. These wastes cannot be discharged into the environment due to strict regulations and require proper treatment to remove the hazardous substances. Various options have been proposed and developed for spent catalysts treatment; however, hydrometallurgical processes are considered efficient, cost-effective and environmentally-friendly methods of metal extraction, and have been widely employed for different metal uptake from aqueous leachates of secondary materials. Although there are a large number of studies on hazardous metal extraction from aqueous solutions of various spent catalysts, little information is available on Co, Ni, and Mo removal from spent NiMo hydroprocessing catalysts. In the current study, a solvent extraction process was applied to the spent HPC to specifically remove Co, Ni, and Mo. The spent HPC is dissolved in an acid solution and then the metals are extracted using three different extractants, two of which were aminebased and one which was a quaternary ammonium salt. The main aim of this study was to develop a hydrometallurgical method to remove, and ultimately be able to recover, Co, Ni, and Mo from the spent HPCs produced at the petrochemical plant in Come By Chance, Newfoundland and Labrador. The specific objectives of the study were: (1) characterization of the spent catalyst and the acidic leachate, (2) identifying the most efficient leaching agent to dissolve the metals from the spent catalyst; (3) development of a solvent extraction procedure using the amine-based extractants Alamine308, Alamine336 and the quaternary ammonium salt, Aliquat336 in toluene to remove Co, Ni, and Mo from the spent catalyst; (4) selection of the best reagent for Co, Ni, and Mo extraction based on the required contact time, required extractant concentration, as well as organic:aqueous ratio; and (5) evaluation of the extraction conditions and optimization of the metal extraction process using the Design Expert® software. For the present study, a Central Composite Design (CCD) method was applied as the main method to design the experiments, evaluate the effect of each parameter, provide a statistical model, and optimize the extraction process. Three parameters were considered as the most significant factors affecting the process efficiency: (i) extractant concentration, (ii) the organic:aqueous ratio, and (iii) contact time. Metal extraction efficiencies were calculated based on ICP analysis of the pre- and post–leachates, and the process optimization was conducted with the aid of the Design Expert® software. The obtained results showed that Alamine308 can be considered to be the most effective and suitable extractant for spent HPC examined in the study. Alamine308 is capable of removing all three metals to the maximum amounts. Aliquat336 was found to be not as effective, especially for Ni extraction; however, it is able to separate all of these metals within the first 10 min, unlike Alamine336, which required more than 35 min to do so. Based on the results of this study, a cost-effective and environmentally-friendly solventextraction process was achieved to remove Co, Ni, and Mo from the spent HPCs in a short amount of time and with the low extractant concentration required. This method can be tested and implemented for other hazardous metals from other secondary materials as well. Further investigation may be required; however, the results of this study can be a guide for future research on similar metal extraction processes.

Estudo da prevalência e da gravidade da dermatite de contacto em 'frango do campo'

O sector avícola enfrenta atualmente dois desafios muito estimulantes. O primeiro decorre do aumento, que se prevê continuar a crescer, nos níveis de procura de carne de aves no mercado interno e internacional; o segundo decorre do facto da criação avícola ter adotado métodos de produção mais intensivos (kg peso vivo/m2/ano) e em maior escala, i.e. com maior concentração animal na mesma exploração. Este carácter vincadamente “industrial” tem merecido uma natural atenção das sociedades e das autoridades pecuárias no sentido desta economia de escala passar a ter num conjunto de instrumentos legais e técnicos o devido contrapeso para a salvaguarda das aves enquanto ser vivo. O presente trabalho tem como ponto de partida a Directiva 2007/43/CE do Conselho de 28 de Junho, relativa ao estabelecimento de regras mínimas para a proteção de frangos de carne. Em virtude de não existir ainda informação suficiente sobre a forma como a qualidade do maneio animal pode ser monitorizada, ao nível do abate, por médicos veterinários e auxiliares oficiais, em frangos de criação especial segundo os modelos definidos no Regulamento (CE) n.º 543/2008, urge realizar estudos neste domínio. O principal objetivo da realização do presente trabalho de campo foi o estudo da ocorrência das dermatites de contacto plantar (pododermatites) e da bolsa sinovial préesternal em frangos produzidos em sistemas de produção considerados “protetores” do bem-estar animal, designadamente os seguintes: i) ar livre; e, ii) extensivo de interior. O estudo foi efetuado num centro de abate de frangos do campo, em Oliveira de Frades, entre Maio de 20012 e Março de 2013. Os animais abatidos foram criados em explorações com contratos de integração situadas no Distrito de Viseu. Os dados foram recolhidos em 39 bandos diferentes da espécie Gallus domesticus, dos quais 1021 carcaças foram avaliadas após evisceração, o que correspondeu ao exame de uma a cada quinze aves da linha de abate. Para a avaliação da pododermatite foi utilizado o método adaptado pela DGAV, enquanto para a avaliação da bursite esternal foi efetuada tendo em conta o modelo aplicado em perus por Berk em 2002. Apesar do modelo estatístico desenvolvido para a análise dos resultados obtidos no presente trabalho exigir um maior número de observações, foi possível identificar com grande precisão alguns fatores de risco que devem ser realçados pela sua relevância no contexto dos sistemas produtivos escrutinados ou no mecanismo fisiopatológico da dermatite de contacto, nomeadamente os seguintes: (i) a idade das aves que, apesar de não ter sido identificada uma relação directa com os scores de pododermatite e bursite, verificou-se que a idade elevada que os animais tipicamente atingem nos sistemas de produção extensivos está associada a uma taxa superior de rejeições pela inspecção sanitária; (ii) o peso pré-abate que, independentemente da inconsistência defendida por diversos autores em relação à influência do peso vivo do frango industrial sobre a dermatite de contacto, nos animais produzidos em regime extensivo, esta variável pode desempenhar um fator chave para a ocorrência desta lesão. De facto, há que realçar que o peso destes animais tem uma importância fulcral na modelação da biomecânica da ave, incluindo na pressão exercida sobre a superfície plantar; (iii) o tipo de sistema de abeberamento, tendo ficado demonstrado que a selecção do tipo de bebedouro tem uma importância peculiar sobre a ocorrência de pododermatite em “frango de campo”, algo que está provavelmente relacionado com a influência exercida sobre o teor de humidade da cama. Globalmente, as frequências de pododermatite e bursite apuradas neste trabalho devem ser consideradas inquietantes. Esta preocupação eleva-se quando se toma consciência que as aves provieram de regimes considerados “amigáveis” e “sustentáveis”, pelo que urge monitorizar adequadamente aqueles sistemas produtivos, melhorar as suas condições e reanalisar os benefícios ao nível do bem-estar animal.

Modelação do risco de perda dentária por doença periodontal

O prognóstico da perda dentária é um dos principais problemas na prática clínica de medicina dentária. Um dos principais fatores prognósticos é a quantidade de suporte ósseo do dente, definido pela área da superfície radicular dentária intraóssea. A estimação desta grandeza tem sido realizada por diferentes metodologias de investigação com resultados heterogéneos. Neste trabalho utilizamos o método da planimetria com microtomografia para calcular a área da superfície radicular (ASR) de uma amostra de cinco dentes segundos pré-molares inferiores obtida da população portuguesa, com o objetivo final de criar um modelo estatístico para estimar a área de superfície radicular intraóssea a partir de indicadores clínicos da perda óssea. Por fim propomos um método para aplicar os resultados na prática. Os dados referentes à área da superfície radicular, comprimento total do dente (CT) e dimensão mésio-distal máxima da coroa (MDeq) serviram para estabelecer as relações estatísticas entre variáveis e definir uma distribuição normal multivariada. Por fim foi criada uma amostra de 37 observações simuladas a partir da distribuição normal multivariada definida e estatisticamente idênticas aos dados da amostra de cinco dentes. Foram ajustados cinco modelos lineares generalizados aos dados simulados. O modelo estatístico foi selecionado segundo os critérios de ajustamento, preditibilidade, potência estatística, acurácia dos parâmetros e da perda de informação, e validado pela análise gráfica de resíduos. Apoiados nos resultados propomos um método em três fases para estimação área de superfície radicular perdida/remanescente. Na primeira fase usamos o modelo estatístico para estimar a área de superfície radicular, na segunda estimamos a proporção (decis) de raiz intraóssea usando uma régua de Schei adaptada e na terceira multiplicamos o valor obtido na primeira fase por um coeficiente que representa a proporção de raiz perdida (ASRp) ou da raiz remanescente (ASRr) para o decil estimado na segunda fase. O ponto forte deste estudo foi a aplicação de metodologia estatística validada para operacionalizar dados clínicos na estimação de suporte ósseo perdido. Como pontos fracos consideramos a aplicação destes resultados apenas aos segundos pré-molares mandibulares e a falta de validação clínica.

Degenerate Gases of Strontium for Studies of Quantum Magnetism

We describe the construction and characterization of a new apparatus that can produce degenerate quantum gases of strontium. The realization of degenerate gases is an important first step toward future studies of quantum magnetism. Three of the four stable isotopes of strontium have been cooled into the degenerate regime. The experiment can make nearly pure Bose-Einstein condensates containing approximately 1x10^4 atoms, for strontium-86, and approximately 4x10^5 atoms, for strontium-84. We have also created degenerate Fermi gases of strontium-87 with a reduced temperature, T/T_F of approximately 0.2. The apparatus will be able to produce Bose-Einstein condensates of strontium-88 with straightforward modifications. We also report the first experimental and theoretical results from the strontium project. We have developed a technique to accelerate the continuous loading of strontium atoms into a magnetic trap. By applying a laser addressing the 3P1 to 3S1 transition in our magneto-optical trap, the rate at which atoms populate the magnetically-trapped 3P2 state can be increased by up to 65%. Quantum degenerate gases of atoms in the metastable 3P0 and 3P2 states are a promising platform for quantum simulation of systems with long-range interactions. We have performed an initial numerical study of a method to transfer the ground state degenerate gases that we can currently produce into one of the metastable states via a three-photon transition. Numerical simulations of the Optical Bloch equations governing the three-photon transition indicate that >90% of a ground state degenerate gas can be transferred into a metastable state.

Shortcuts to adiabaticity in the double well

123 p.

The role of science and technology management companies in the set-up and coordination of multi-partners scientific alliances

Mestrado em Economia e Gestão de Ciência, Tecnologia e Inovação

Performance and Robustness Analysis of Co-Prime and Nested Sampling

Coprime and nested sampling are well known deterministic sampling techniques that operate at rates significantly lower than the Nyquist rate, and yet allow perfect reconstruction of the spectra of wide sense stationary signals. However, theoretical guarantees for these samplers assume ideal conditions such as synchronous sampling, and ability to perfectly compute statistical expectations. This thesis studies the performance of coprime and nested samplers in spatial and temporal domains, when these assumptions are violated. In spatial domain, the robustness of these samplers is studied by considering arrays with perturbed sensor locations (with unknown perturbations). Simplified expressions for the Fisher Information matrix for perturbed coprime and nested arrays are derived, which explicitly highlight the role of co-array. It is shown that even in presence of perturbations, it is possible to resolve $O(M^2)$ under appropriate conditions on the size of the grid. The assumption of small perturbations leads to a novel ``bi-affine" model in terms of source powers and perturbations. The redundancies in the co-array are then exploited to eliminate the nuisance perturbation variable, and reduce the bi-affine problem to a linear underdetermined (sparse) problem in source powers. This thesis also studies the robustness of coprime sampling to finite number of samples and sampling jitter, by analyzing their effects on the quality of the estimated autocorrelation sequence. A variety of bounds on the error introduced by such non ideal sampling schemes are computed by considering a statistical model for the perturbation. They indicate that coprime sampling leads to stable estimation of the autocorrelation sequence, in presence of small perturbations. Under appropriate assumptions on the distribution of WSS signals, sharp bounds on the estimation error are established which indicate that the error decays exponentially with the number of samples. The theoretical claims are supported by extensive numerical experiments.

Estimation of leaf area in grapevine cv. Syrah using empirical models

Aiming to obtain empirical models for the estimation of Syrah leaf area a set of 210 fruiting shoots was randomly collected during the 2013 growing season in an adult experimental vineyard, located in Lisbon, Portugal. Samples of 30 fruiting shoots were taken periodically from the stage of inflorescences visible to veraison (7 sampling dates). At the lab, from each shoot, primary and lateral leaves were separated and numbered according to node insertion. For each leaf, the length of the central and lateral veins was recorded and then the leaf area was measured by a leaf area meter. For single leaf area estimation the best statistical models uses as explanatory variable the sum of the lengths of the two lateral leaf veins. For the estimation of leaf area per shoot it was followed the approach of Lopes & Pinto (2005), based on 3 explanatory variables: number of primary leaves and area of the largest and smallest leaves. The best statistical model for estimation of primary leaf area per shoot uses a calculated variable obtained from the average of the largest and smallest primary leaf area multiplied by the number of primary leaves. For lateral leaf area estimation another model using the same type of calculated variable is also presented. All models explain a very high proportion of variability in leaf area. Our results confirm the already reported strong importance of the three measured variables (number of leaves and area of the largest and smallest leaf) as predictors of the shoot leaf area. The proposed models can be used to accurately predict Syrah primary and secondary leaf area per shoot in any phase of the growing cycle. They are inexpensive, practical, non-destructive methods which do not require specialized staff or expensive equipment.

Modelagem analítica e experimental da filtração em meios porosos

Deep bed filtration occurs in several industrial and environmental processes like water filtration and soil contamination. In petroleum industry, deep bed filtration occurs near to injection wells during water injection, causing injectivity reduction. It also takes place during well drilling, sand production control, produced water disposal in aquifers, etc. The particle capture in porous media can be caused by different physical mechanisms (size exclusion, electrical forces, bridging, gravity, etc). A statistical model for filtration in porous media is proposed and analytical solutions for suspended and retained particles are derived. The model, which incorporates particle retention probability, is compared with the classical deep bed filtration model allowing a physical interpretation of the filtration coefficients. Comparison of the obtained analytical solutions for the proposed model with the classical model solutions allows concluding that the larger the particle capture probability, the larger the discrepancy between the proposed and the classical models

SAMPLING BIAS IN EVALUATING THE PROBABILITY OF SEISMICALLY INDUCED SOIL LIQUEFACTION WITH SPT & CPT CASE HISTORIES

Several deterministic and probabilistic methods are used to evaluate the probability of seismically induced liquefaction of a soil. The probabilistic models usually possess some uncertainty in that model and uncertainties in the parameters used to develop that model. These model uncertainties vary from one statistical model to another. Most of the model uncertainties are epistemic, and can be addressed through appropriate knowledge of the statistical model. One such epistemic model uncertainty in evaluating liquefaction potential using a probabilistic model such as logistic regression is sampling bias. Sampling bias is the difference between the class distribution in the sample used for developing the statistical model and the true population distribution of liquefaction and non-liquefaction instances. Recent studies have shown that sampling bias can significantly affect the predicted probability using a statistical model. To address this epistemic uncertainty, a new approach was developed for evaluating the probability of seismically-induced soil liquefaction, in which a logistic regression model in combination with Hosmer-Lemeshow statistic was used. This approach was used to estimate the population (true) distribution of liquefaction to non-liquefaction instances of standard penetration test (SPT) and cone penetration test (CPT) based most updated case histories. Apart from this, other model uncertainties such as distribution of explanatory variables and significance of explanatory variables were also addressed using KS test and Wald statistic respectively. Moreover, based on estimated population distribution, logistic regression equations were proposed to calculate the probability of liquefaction for both SPT and CPT based case history. Additionally, the proposed probability curves were compared with existing probability curves based on SPT and CPT case histories.

What to do when K-means clustering fails:a simple yet principled alternative algorithm

The K-means algorithm is one of the most popular clustering algorithms in current use as it is relatively fast yet simple to understand and deploy in practice. Nevertheless, its use entails certain restrictive assumptions about the data, the negative consequences of which are not always immediately apparent, as we demonstrate. While more flexible algorithms have been developed, their widespread use has been hindered by their computational and technical complexity. Motivated by these considerations, we present a flexible alternative to K-means that relaxes most of the assumptions, whilst remaining almost as fast and simple. This novel algorithm which we call MAP-DP (maximum a-posteriori Dirichlet process mixtures), is statistically rigorous as it is based on nonparametric Bayesian Dirichlet process mixture modeling. This approach allows us to overcome most of the limitations imposed by K-means. The number of clusters K is estimated from the data instead of being fixed a-priori as in K-means. In addition, while K-means is restricted to continuous data, the MAP-DP framework can be applied to many kinds of data, for example, binary, count or ordinal data. Also, it can efficiently separate outliers from the data. This additional flexibility does not incur a significant computational overhead compared to K-means with MAP-DP convergence typically achieved in the order of seconds for many practical problems. Finally, in contrast to K-means, since the algorithm is based on an underlying statistical model, the MAP-DP framework can deal with missing data and enables model testing such as cross validation in a principled way. We demonstrate the simplicity and effectiveness of this algorithm on the health informatics problem of clinical sub-typing in a cluster of diseases known as parkinsonism.

Simple approximate MAP inference for Dirichlet processes mixtures

The Dirichlet process mixture model (DPMM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibbs sampling are required. As a result, DPMM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here, we develop a simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithm for DPMMs. This algorithm is as simple as DP-means clustering, solves the MAP problem as well as Gibbs sampling, while requiring only a fraction of the computational effort. (For freely available code that implements the MAP-DP algorithm for Gaussian mixtures see http://www.maxlittle.net/.) Unlike related small variance asymptotics (SVA), our method is non-degenerate and so inherits the “rich get richer” property of the Dirichlet process. It also retains a non-degenerate closed-form likelihood which enables out-of-sample calculations and the use of standard tools such as cross-validation. We illustrate the benefits of our algorithm on a range of examples and contrast it to variational, SVA and sampling approaches from both a computational complexity perspective as well as in terms of clustering performance. We demonstrate the wide applicabiity of our approach by presenting an approximate MAP inference method for the infinite hidden Markov model whose performance contrasts favorably with a recently proposed hybrid SVA approach. Similarly, we show how our algorithm can applied to a semiparametric mixed-effects regression model where the random effects distribution is modelled using an infinite mixture model, as used in longitudinal progression modelling in population health science. Finally, we propose directions for future research on approximate MAP inference in Bayesian nonparametrics.

A Bayesian approach to the determination of ignition delay

A novel in-cylinder pressure method for determining ignition delay has been proposed and demonstrated. This method proposes a new Bayesian statistical model to resolve the start of combustion, defined as being the point at which the band-pass in-cylinder pressure deviates from background noise and the combustion resonance begins. Further, it is demonstrated that this method is still accurate in situations where there is noise present. The start of combustion can be resolved for each cycle without the need for ad hoc methods such as cycle averaging. Therefore, this method allows for analysis of consecutive cycles and inter-cycle variability studies. Ignition delay obtained by this method and by the net rate of heat release have been shown to give good agreement. However, the use of combustion resonance to determine the start of combustion is preferable over the net rate of heat release method because it does not rely on knowledge of heat losses and will still function accurately in the presence of noise. Results for a six-cylinder turbo-charged common-rail diesel engine run with neat diesel fuel at full, three quarters and half load have been presented. Under these conditions the ignition delay was shown to increase as the load was decreased with a significant increase in ignition delay at half load, when compared with three quarter and full loads.

Effective web service discovery using a combination of a semantic model and a data mining technique

With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s interest. By considering the semantic relationships of words used in describing the services as well as the use of input and output parameters can lead to accurate Web service discovery. Appropriate linking of individual matched services should fully satisfy the requirements which the user is looking for. This research proposes to integrate a semantic model and a data mining technique to enhance the accuracy of Web service discovery. A novel three-phase Web service discovery methodology has been proposed. The first phase performs match-making to find semantically similar Web services for a user query. In order to perform semantic analysis on the content present in the Web service description language document, the support-based latent semantic kernel is constructed using an innovative concept of binning and merging on the large quantity of text documents covering diverse areas of domain of knowledge. The use of a generic latent semantic kernel constructed with a large number of terms helps to find the hidden meaning of the query terms which otherwise could not be found. Sometimes a single Web service is unable to fully satisfy the requirement of the user. In such cases, a composition of multiple inter-related Web services is presented to the user. The task of checking the possibility of linking multiple Web services is done in the second phase. Once the feasibility of linking Web services is checked, the objective is to provide the user with the best composition of Web services. In the link analysis phase, the Web services are modelled as nodes of a graph and an allpair shortest-path algorithm is applied to find the optimum path at the minimum cost for traversal. The third phase which is the system integration, integrates the results from the preceding two phases by using an original fusion algorithm in the fusion engine. Finally, the recommendation engine which is an integral part of the system integration phase makes the final recommendations including individual and composite Web services to the user. In order to evaluate the performance of the proposed method, extensive experimentation has been performed. Results of the proposed support-based semantic kernel method of Web service discovery are compared with the results of the standard keyword-based information-retrieval method and a clustering-based machine-learning method of Web service discovery. The proposed method outperforms both information-retrieval and machine-learning based methods. Experimental results and statistical analysis also show that the best Web services compositions are obtained by considering 10 to 15 Web services that are found in phase-I for linking. Empirical results also ascertain that the fusion engine boosts the accuracy of Web service discovery by combining the inputs from both the semantic analysis (phase-I) and the link analysis (phase-II) in a systematic fashion. Overall, the accuracy of Web service discovery with the proposed method shows a significant improvement over traditional discovery methods.