Destruction of the tar present in syngas by combustion in porous media

The cleaning of syngas is one of the most important challenges in the development of technologies based on gasification of biomass. Tar is an undesired byproduct because, once condensed, it can cause fouling and plugging and damage the downstream equipment. Thermochemical methods for tar destruction, which include catalytic cracking and thermal cracking, are intrinsically attractive because they are energetically efficient and no movable parts are required nor byproducts are produced. The main difficulty with these methods is the tendency for tar to polymerize at high temperatures. An alternative to tar removal is the complete combustion of the syngas in a porous burner directly as it leaves the particle capture system. In this context, the main aim of this study is to evaluate the destruction of the tar present in the syngas from biomass gasification by combustion in porous media. A gas mixture was used to emulate the syngas, which included toluene as a tar surrogate. Initially, CHEMKIN was used to assess the potential of the proposed solution. The calculations revealed the complete destruction of the tar surrogate for a wide range of operating conditions and indicated that the most important reactions in the toluene conversion are C6H5CH3 + OH <-> C6H5CH2 + H2O, C6H5CH3 + OH <-> C6H4CH3 + H2O, and C6H5CH3 + O <-> OC6H4CH3 + H and that the formation of toluene can occur through C6H5CH2 + H <-> C6H5CH3. Subsequently, experimental tests were performed in a porous burner fired with pure methane and syngas for two equivalence ratios and three flow velocities. In these tests, the toluene concentration in the syngas varied from 50 to 200 g/Nm(3). In line with the CHEMKIN calculations, the results revealed that toluene was almost completely destroyed for all tested conditions and that the process did not affect the performance of the porous burner regarding the emissions of CO, hydrocarbons, and NOx.

Analysis of Forest Fires by means of Pseudo Phase Plane and Multidimensional Scaling Methods

Forest fires dynamics is often characterized by the absence of a characteristic length-scale, long range correlations in space and time, and long memory, which are features also associated with fractional order systems. In this paper a public domain forest fires catalogue, containing information of events for Portugal, covering the period from 1980 up to 2012, is tackled. The events are modelled as time series of Dirac impulses with amplitude proportional to the burnt area. The time series are viewed as the system output and are interpreted as a manifestation of the system dynamics. In the first phase we use the pseudo phase plane (PPP) technique to describe forest fires dynamics. In the second phase we use multidimensional scaling (MDS) visualization tools. The PPP allows the representation of forest fires dynamics in two-dimensional space, by taking time series representative of the phenomena. The MDS approach generates maps where objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to better understand forest fires behaviour.

Evaluation of different methods for Plasmodia detection, in well defined population groups in an endemic area of Brazil

In Brazil, more than 500,000 new cases of malaria were notified in 1992. Plasmodium falciparum and P.vivax are the responsible species for 99.3% of the cases. For adequate treatment, precoce diagnosis is necessary. In this work, we present the results of the traditional Plasmodia detection method, thick blood film (TBF), and the results of alternative methods: Immunofluorescence assay (IFA) with polyclonal antibody and Quantitative Buffy Coat method (QBC)® in a well defined population groups. The analysis were done in relation to the presence or absence of malaria clinical symptoms. Also different classes of immunoglobulins anti-P.falciparum were quantified for the global analysis of the results, mainly in the discrepant results. We concluded that alternative methods are more sensitive than TBF and that the association of epidemiological, clinical and laboratory findings is necessary to define the presence of malaria.

Entamoeba histolytica: detection of coproantigens by purified antibody in the capture sandwich ELISA

A sensitive and specific Capture Sandwich ELISA (CSE) was developed using polyclonal purified rabbit antibodies against three different axenic strains of Entamoeba histolytica: CSP from Brazil and HM1 - IMSS from Mexico, for the detection of coproantigens in fecal samples. Immunoglobulin G (IgG) againstis E. histolytica was isolated from rabbits immunized with throphozoites whole extract in two stages: affinity chromatography in a column containing E. histolytica antigens bound to Sepharose 4B was followed by another chromatography in Sepharose antibodies 4B-Protein A. A Capture Sandwich ELISA using purified antibodies was able to detect 70ng of amebae protein, showing a sensitivity of 93% and specificity of 94%. The combination of microscopic examination and CSE gave a concordance and discordance of 93.25% and 6.75%, respectively. It was concluded that CSE is highly specific for the detection of coproantigens of E. histolytica in feces of infected patients, is quicker to perform, easier and more sensitive than microscopic examination.

New ultrasonic-based methods in proteomics

Dissertação apresentada para obtenção do Grau de Doutor em Bioquímica pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia.A presente dissertação foi preparada no âmbito do convénio bilateral existente entre a Universidade Nova de Lisboa e a Universidade de Vigo.

Entamoeba histolytica: fecal antigen capture immunoassay for the diagnosis of enteric amebiasis by a monoclonal antibody

Amebiasis continues to be of epidemiological importance in underdeveloped countries. Clinical diagnosis and epidemiological setting in a region are based on the fecal microscopic identification of cysts or trophozoites. This procedure requires well trained personnel, is laborious, of low sensitivity and frequently yields false-positives results. The present study was designed to develop an immuno-enzymatic fecal 96 kDa antigen capture test (COPROELISA-Eh) more sensitive and specific than microscopic diagnosis of amebiasis. Triplicates of 177 stool samples processed by the formol-ether concentration method, were defined as positive or negative by three experienced microscopic observers. Another aliquot was submitted to the antigen capture test by a monoclonal antibody against a specific membrane antigen of pathogenic strains of Entamoeba histolytica. Optical densities were interpreted as positive when they exceeded the mean value of negative samples plus two standard deviations. COPROELISA-Eh showed a 94.4% sensitivity, 98.3% specificity, 96.2% positive predictive value and 97.6% negative predictive value for the detection of E. histolytica in feces. COPROELISA-Eh is more sensitive and specific than microscopic examination, does not require specially trained personnel and allows the simultaneous processing of a large number of samples.

Domestic vectors of Chagas' disease in three rural communities of Nicaragua

A triatomine survey was conducted in three rural settlements of Nicaragua (Santa Rosa, Quebrada Honda and Poneloya) where Chagas' disease is endemic, to determine rates of house infestation, evaluate the housing condition and to asess the performance of the María sensor box in detection of domestic vectors. A total of 184 households were selected and vectors were sought by the methods of timed manual capture and by sensor boxes. The sole vectors species found in this study was Triatoma dimidiata. Of the examined bugs 50, 60 and 33%, in the respective communities, were infected with T. cruzi. The rates of house infestation as determined by manual capture and sensor boxes were respectively, 48.3% and 54.2% in Santa Rosa, 29.8% and 51.2% in Quebrada Honda and in Poneloya 3.8 and 5.9% with significant difference between the methods in Quebrada Honda. When compared with the manual capture, the Maria sensor box detected vectors in 71.4% of positive houses in two of the communities but also was able to detect bugs in 39.3% and 41.1% of houses where manual capture had been negative. Housing condition was evaluated according to three structural parameters, in this way, in the first community 79.2% of houses were classified as bad, 20.8% as regular; in the second one 42.5% were bad and 57.5% regular, whereas in the third 62.5% of the houses were regular. Rates of infestation did not differ greatly between the different housing conditions. Our results show that the sensor box is as efficient as manual capture and could be implemented in our country.

Using Baseline Methods to Identify Non-technical Losses in the Context of Smart Grids

Demand response has gained increasing importance in the context of competitive electricity markets and smart grid environments. In addition to the importance that has been given to the development of business models for integrating demand response, several methods have been developed to evaluate the consumers’ performance after the participation in a demand response event. The present paper uses those performance evaluation methods, namely customer baseline load calculation methods, to determine the expected consumption in each period of the consumer historic data. In the cases in which there is a certain difference between the actual consumption and the estimated consumption, the consumer is identified as a potential cause of non-technical losses. A case study demonstrates the application of the proposed method to real consumption data.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Consumers Performance Evaluation of the Participation in Demand Response Programs Using Baseline Methods

Demand response has gain increasing importance in the context of competitive electricity markets environment. The use of demand resources is also advantageous in the context of smart grid operation. In addition to the need of new business models for integrating demand response, adequate methods are necessary for an accurate determination of the consumers’ performance evaluation after the participation in a demand response event. The present paper makes a comparison between some of the existing baseline methods related to the consumers’ performance evaluation, comparing the results obtained with these methods and also with a method proposed by the authors of the paper. A case study demonstrates the application of the referred methods to real consumption data belonging to a consumer connected to a distribution network.

Analysis of Consumption Data to Detect Commercial Losses Using Performance Evaluation Methods in a Smart Grid

Electric power networks, namely distribution networks, have been suffering several changes during the last years due to changes in the power systems operation, towards the implementation of smart grids. Several approaches to the operation of the resources have been introduced, as the case of demand response, making use of the new capabilities of the smart grids. In the initial levels of the smart grids implementation reduced amounts of data are generated, namely consumption data. The methodology proposed in the present paper makes use of demand response consumers’ performance evaluation methods to determine the expected consumption for a given consumer. Then, potential commercial losses are identified using monthly historic consumption data. Real consumption data is used in the case study to demonstrate the application of the proposed method.

A study on parallel versus sequential relational fuzzy clustering methods

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Industry level foresight: designing foresight methods for Lithuanian energy sector

Enterprise and Work Innovation Studies,6,IET, pp.9-51

Implementação de uma Interface Homem-máquina baseada em Eletroculografia

A constante evolução da tecnologia permitiu ao ser humano a utilização de dispositivos electrónicos nas suas rotinas diárias. Estas podem ser afetadas quando os utilizadores sofrem de deficiências ou doenças que afetam as suas capacidades motoras. Com o intuito de minimizar este obstáculo surgiram as Interfaces Homem-Computador (HCI). É neste panorama que os sistemas HCI baseados em Eletroculografia (EOG) assumem um papel preponderante na melhoria da qualidade de vida destes indivíduos. A Eletroculografia é o resultado da aquisição do movimento ocular, que pode ser adquirido através de diversos métodos. Os métodos mais convencionais utilizam elétrodos de superfície para aquisição dos sinais elétricos, ou então, utilizam sistemas de gravação de vídeo, que gravam o movimento ocular. O objetivo desta tese é desenvolver um sistema HCI baseado em Eletroculografia, que adquire o sinal elétrico do movimento ocular através de elétrodos de superfície. Para tal desenvolveu-se um circuito eletrónico para a aquisição do sinal de EOG, bem como um algoritmo em Python para análise do mesmo. O circuito foi desenvolvido recorrendo a seis módulos diferentes, cada um deles com uma função específica. Para cada módulo foi necessário desenhar e implementar placas de circuito impresso, que quando conectadas entre si permitem filtrar, amplificar e digitalizar os sinais elétricos, adquiridos através de elétrodos de superfície, originados pelo movimento ocular. O algoritmo criado em Python permite analisar os dados provenientes do circuito e converte-os para coordenadas. Através destas foi possível determinar o sentido e a amplitude do movimento ocular.

Extremum estimators and stochastic optimization methods

Submitted in partial fulfillment for the Requirements for the Degree of PhD in Mathematics, in the Speciality of Statistics in the Faculdade de Ciências e Tecnologia