Construção de curvas binodais para sistemas ternários, contendo biodiesel, álcool e água

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Química

Methodological adaptation for nematodes extration in forest soils of the southern Mata Atlântica

The objective of this work was to clarify whether the method to extract nematodes from European soils is suitable for forest soils and litter in the eastern of Paraná state, Brazil, and whether nematode abundance differs between sites with different ecosystems and levels of human interference. The study sites were situated in the coastal area of the Serra do Mar, near the town of Antonina, in Eastern Paraná, Brazil. Cobb's sieving and decanting method was more appropriate than ISO method, since extraction efficiency was higher and intra-sample variability was significantly lower. In order to achieve an extraction efficiency higher than 90%, Cobb's method was modified. For the extraction of nematodes from litter, the Baermann funnel, with an extraction time of 48 hours, yielded an extraction efficiency higher than 90%. Nematode abundance in litter was higher than in soil. The mean number of individuals extracted from the litter increased with the age stage of the forest sites sampled, and there was no difference in the number of individuals in the soil of the four forest sites. Mean nematode abundance in soil in banana plantations was about twice as high compared to the banana-palmito mixed stands and to the forest sites.

Haja-asutusalueiden sakokaivolietteiden kalkkistabilointi ja hyötykäyttö maataloudessa

In the sparsely populated areas of Finland there are approximately 350 000 households and 450 000 leisure time residences outside sewer networks. According to the Finnish domestic wastewater act outside sewer networks, the Finnish Government is reducing the environmental load of domestic wastewaters by the year 2017. The law is aimed at restricting the quality of sludge from domestic wastewater purification systems. The wastewater purification systems are complex systems, which often include sedimentation basins. The sedimentation basins remove most of the nutrients from the domestic wastewaters. The Finnish Government has decided that sedimentation basin sludge must be treated before reusing. One possibility is to stabilise domestic sludge with slaked lime and to reuse treated sludge in agriculture. According to this master’s thesis lime stabilisation can be done in sedimentation basins or in decanting tanks. Decanting tanks must be under 100 m3. Dosage of stabilisation is 8,5 kg/m3 of lime. If you are treading sludge that is highly hydrous, you need 13,5 kg/m3 of lime. In stabilisation lime and sludge must be thoroughly mixed. Mixed sludge must be in sedimentation basin at least two hours. If there is evidence that sludge contains salmonella or if it’s decanting tank stabilisation time is 48 hours. Sludge must be mixed at least once during the longer stabilisation time. Lime destroys Esherichia coli and enterococcus concentrations below accepted level. Lime also destroys Salmonella bacterium. After treating, sludge’s can be distributed over a field. You can safely spread lime treated domestic sludge’s about 40 m3/ha. Lime stabilisation can also be used to treat separately and collectively collected domestic wastewaters.

Utilização de argilas para purificação de biodiesel

This work describes the results of the purification of methyl biodiesel, obtained by oxidized soybean oil, using different methods. After the ester separation from the glycerin by decanting, the ester was purified each time with distillation, washing with water and adsorption with bauxite, bentonite and attapulgite. The removal of total contamination, unsaponifiable material, concentrations of free glycerin and soap were analyzed in the purified ester phase. The best result of purification was observed with the use of bentonite and bauxite, in the removal of soap and free glycerin respectively.

Efficiency of geotextile filters in dewatering wastes (sludge) from water treatment works

This paper evaluates the efficiency of geotextile filters for sludge from a compact water treatment plant (WTP). The key aspects required in the methodology of selection and designing geotextile filters for sludge from dewatering was investigated based on laboratory tests results. The analyses were supported by the measured filtrated volume of water and turbidity resulting from variable head permeability tests carried out in two geotextiles and using the conventional granular filter (sand and gravel). The results of the present study showed that more than 75% of the dewatering sludge can be filtrated with low turbidity, which permits that this water can return to the treatment plan in order to be reuse in another cycle. The reduced volume of sludge retained by the geotextile that is transferred to the drying pound increases its efficiency by reducing the drying time. The low volume of the dry waste can be removed and the geotextile can be easily cleaned or replaced when needed. These procedures significantly reduce the volume of water needed in dewatering and also avoids waste discharges in the environment.

Produtividade de cana-de-açúcar sob adubação com torta de filtro enriquecida com fosfato solúvel

Phosphorus is considered an essential element for plants and it is found in small amounts in Brazilian soils. The filter cake residue, composed of a mixture of bagasse and decanting sludge, has high levels of organic matter, phosphorus and calcium. The phosphorus present in the filter cake is organic, and its release, as it happens to the nitrogen, occurs gradually by mineralization and by microorganisms attack in the soil. This study aimed to evaluate sugarcane vegetative growth and yield under fertilization with filter cake enriched with soluble phosphate. The experiment was carried out in Presidente Prudente, São Paulo State, Brazil, by using a randomized complete block design, in a 5x4 factorial scheme, where the first factor consisted of filter cake doses (0 t ha-1, 0.5 t ha-1, 1.0 t ha-1, 2.0 t ha-1, and 4.0 t ha-1) and the second of phosphorus fertilizer doses (0 kg ha-1, 50 kg ha -1, 100 kg ha-1, and 200 kg ha-1 of P 2O5), with 4 repetitions, totalizing 80 plots. The experiment evaluated the tiller number, at 30, 60, 90, and 120 days after planting, oBrix, and yield. The stalk yield and tillering were influenced by the filter cake rates applied to the soil. Filter cake doses and their combination with phosphate did not change the juice quality (Brix) at harvest.

Craqueamento termocatalítico do óleo de fritura residual

Neste trabalho foi estudado o processo de craqueamento termocatalítico do óleo de fritura nas escalas de bancada e piloto, variando-se o percentual do catalisador carbonato de sódio de 5 e 10% m/m em relação a matéria prima utilizada e temperatura de 440 ºC. O objetivo foi obter misturas de hidrocarbonetos ricas na fração diesel. O óleo de fritura neutralizado e seco foi caracterizado em relação ao Índice de Acidez, Índice de saponificação, Viscosidade Cinemática, Densidade e Índice de Refração. Após o craqueamento, o produto líquido obtido foi purificado por decantação da fase aquosa e filtração simples em escala de bancada. Esse produto foi fracionado por destilação fracionada e os condensados foram coletados em um funil de decantação de acordo a faixa de destilação da gasolina (40ºC-175ºC), querosene (175ºC-235ºC), diesel leve (235°C-305ºC) e diesel pesado (305ºC-400 ºC). Foi realizada a caracterização tanto físico química quanto da composição dos produtos líquidos e suas respectivas frações. Também foi realizada a evolução do processo de craqueamento em escala piloto, acompanhando o comportamento das características físico químicas e de composição do produto formado no decorrer do processo de craqueamento. Os resultados mostraram que o catalisador carbonato de sódio forneceu produtos de baixa acidez e com boas características para uso como combustível. A variação do percentual de catalisador influencia significamente as propriedades físico químicas e composição tanto do produto quanto de suas frações. Verificou-se, ainda, que o craqueamento termocatalítico do óleo de fritura propicia a formação de hidrocarbonetos ricos na fração do diesel (19,16% diesel leve e 41,18% diesel pesado para o teste com 10% de Na₂CO₃ e de 13,53% leve e 52,73% diesel pesado para o teste com 5% de Na₂CO₃ ). Os intervalos de tempos finais do craqueamento geram um combustível com baixo teor de acidez e com propriedades físico químicas em conformidade a norma especificada para o diesel mineral.

Estudo da obtenção de biocombustíveis a partir da rota tecnológica de craqueamento utilizando carbonato de sódio e lama vermelha como catalisadores

Neste trabalho foi estudado o processo de destilação do produto líquido orgânico, obtido no craqueamento catalítico do óleo de palma (Elaeis guineensis, Jacq) bruto em escala piloto, empregando os catalisadores carbonato de sódio (Na₂CO₃) e a lama vermelha, variando o percentual de catalisador em 10% m/m e 15% m/m em relação à matéria prima utilizada, sendo fixada uma temperatura operacional de 450ºC, visando obter frações de biocombustíveis (bio-gasolina, bio-querosene e bio-óleo) semelhantes aos combustíveis derivados do petróleo. Os catalisadores foram submetidos a um pré-tratamento de desidratação durante 2 horas em uma estufa à 300ºC, posteriormente foram realizadas as análises de DRX, IR e TG. Quanto à matéria prima, foram realizadas análises físico-químicas, visando à caracterização do óleo de palma. Os produtos líquidos orgânicos (PLOs) obtidos foram submetidos a operações unitárias de separação, decantação e filtração simples em escala de bancada, para posteriormente serem realizadas análises físico-químicas e composicionais. Os PLOs foram destilados em uma coluna Vigreux de seis (06) estágios, e as frações condensadas foram coletadas de acordo com as faixas de destilação da gasolina (60ºC - 190ºC), querosene (190ºC - 235ºC) e diesel (235°C - 370ºC), para posteriormente serem caracterizadas. Verificou-se uma melhor eficiência para o catalisador carbonato de sódio a 15% m/m quanto a redução do índice de acidez, cerca de 1,7 mgKOH/g, assim como uma conversão mássica de 97% do óleo em PLO, notou-se também que, ao aumentar a quantidade de catalisador, isto favoreceu a obtenção de um produto final com uma melhor qualidade. A lama vermelha por outro lado, apresentou rendimentos de até 64% m/m e produtos com baixa acidez cerca de 62,90 mgKOH/g, comparando este resultado com dados encontrados na literatura. A partir dos resultados finais, verificou-se a eficiência dos catalisadores, no qual o catalisador carbonato de sódio forneceu produtos com baixa acidez e com boas características para uso como combustível.

Influência da adição de bioactive glass na liberação de íons do cimento de ionômero de vidro modificado por resina

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desenvolvimento e teste de um sistema para separação automática das fases glicerol-biodiesel utilizando sensor de condutividade elétrica

Pós-graduação em Engenharia Mecânica - FEIS

Produtividade de cana-de-açúcar sob adubação com torta de filtro enriquecida com fosfato solúvel

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Gas adsorption and desorption effects on cylinders and their importance for long-term gas records

It is well known that gases adsorb on many surfaces, in particular metal surfaces. There are two main forms responsible for these effects (i) physisorption and (ii) chemisorption. Physisorption is associated with lower binding energies in the order of 1–10 kJ mol−¹, compared to chemisorption which ranges from 100 to 1000 kJ mol−¹. Furthermore, chemisorption only forms monolayers, contrasting physisorption that can form multilayer adsorption. The reverse process is called desorption and follows similar mathematical laws; however, it can be influenced by hysteresis effects. In the present experiment, we investigated the adsorption/desorption phenomena on three steel and three aluminium cylinders containing compressed air in our laboratory and under controlled conditions in a climate chamber, respectively. Our observations from completely decanting one steel and two aluminium cylinders are in agreement with the pressure dependence of physisorption for CO₂, CH₄, and H₂O. The CO₂ results for both cylinder types are in excellent agreement with the pressure dependence of a monolayer adsorption model. However, mole fraction changes due to adsorption on aluminium (< 0.05 and 0 ppm for CO₂ and H₂O) were significantly lower than on steel (< 0.41 ppm and about < 2.5 ppm, respectively). The CO₂ amount adsorbed (5.8 × 1019 CO₂ molecules) corresponds to about the fivefold monolayer adsorption, indicating that the effective surface exposed for adsorption is significantly larger than the geometric surface area. Adsorption/desorption effects were minimal for CH₄ and for CO but require further attention since they were only studied on one aluminium cylinder with a very low mole fraction. In the climate chamber, the cylinders were exposed to temperatures between −10 and +50 °C to determine the corresponding temperature coefficients of adsorption. Again, we found distinctly different values for CO₂, ranging from 0.0014 to 0.0184 ppm °C−¹ for steel cylinders and −0.0002 to −0.0003 ppm °C−¹ for aluminium cylinders. The reversed temperature dependence for aluminium cylinders points to significantly lower desorption energies than for steel cylinders and due to the small values, they might at least partly be influenced by temperature, permeation from/to sealing materials, and gas-consumption-induced pressure changes. Temperature coefficients for CH₄, CO, and H₂O adsorption were, within their error bands, insignificant. These results do indicate the need for careful selection and usage of gas cylinders for high-precision calibration purposes such as requested in trace gas applications.

Caracterización química de efluentes de bodegas, Mendoza (Argentina)

La industria vitivinícola genera efluentes sólidos y líquidos en cantidad apreciable. Los sólidos son aprovechados en distintas aplicaciones e inclusive tienen valor comercial. En cambio, los líquidos pueden originar problemas cuando es necesario decidir cómo desecharlos o transformarlos en desechables. En Mendoza (Argentina), es común enviarlos después de su decantación a cauces y campos abiertos. En ambos casos aparece un serio riesgo de contaminación. Visto que generalmente se desconoce la composición de tales efluentes, este trabajo pretende caracterizarlos físico-químicamente en el período de elaboración de vinos, determinando: pH, conductividad eléctrica, DBO, DQO, cloruros, sulfatos, carbonatos y bicarbonatos, calcio, magnesio, sodio y potasio. La calidad de los efluentes varía notablemente con el agua empleada en los lavados, que aporta mayoritariamente aniones y cationes. Cuando el agua de lavado es abundante, los valores de pH, DBO y DQO de los efluentes permiten su eliminación junto con otros residuos cloacales o en campo abierto.

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web 1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs. These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools. Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ‘intelligently’ will include at least a module for POS tagging. The more an application needs to ‘understand’ the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate. However, linguistic annotation tools have still some limitations, which can be summarised as follows: 1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.). 2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts. 3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc. A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved. In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool. Therefore, it would be quite useful to find a way to (i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools; (ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate. Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned. Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section. 2. GOALS OF THE PRESENT WORK As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the triple structure recommended for annotations in these works (which is isomorphic to the triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag’s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag’s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag’s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag’s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag’s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag’s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger’s schema, a concrete instance of OntoTag’s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely • Bitext’s DataLexica (http://www.bitext.com/EN/datalexica.asp), • LACELL’s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), • Connexor’s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and • EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag’s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger’s configuration, a concrete instance of OntoTag’s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL’s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger’s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG’S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. • CONFIRMED by the development of: o OntoTag’s annotation scheme, o OntoTag’s annotation architecture, o OntoTagger’s (XML, RDF, OWL) annotation schemas, o OntoTagger’s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. • CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. • H.3 was CONFIRMED by means of the development of OntoTagger’s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor’s FDG, Bitext’s DataLexica and LACELL’s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). • CONFIRMED by means of the development of OntoTagger’s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. • CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. • REJECTED: OntoTagger’s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag’s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag’s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag’s linguistic ontologies). • On the one hand, OntoTag’s network of ontologies consists of − The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; − The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; − The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; − The OIO (OntoTag’s Integration Ontology), which  Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO;  Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. • On the other hand, OntoTag’s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: − As far as morphosyntactic annotations are concerned, OntoTag’s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; − As for syntactic annotations, OntoTag’s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; − Regarding semantic annotations, OntoTag’s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; − The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag’s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL’s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually performed by humans or else by machine learning systems. Both of them leave much to be desired: the former, with respect to their annotation rate; the latter, with respect to their (average) precision and recall. In this work, we showed how linguistic tools can be wrapped in order to annotate automatically Semantic Web pages using ontologies. This entails their fast, robust and accurate semantic annotation. As a way of example, as mentioned in Sub-goal 5.5, we developed a particular OntoTagger module for the recognition, classification and labelling of named entities, according to the MUC and ACE tagsets (Chinchor, 1997; Doddington et al., 2004). These tagsets were further specified by means of a domain ontology, namely the Cinema Named Entities Ontology (CNEO). This module was applied to the automatic annotation of ten different web pages containing cinema reviews (that is, around 5000 words). In addition, the named entities annotated with this module were also labelled as instances (or individuals) of the classes included in the CNEO and, then, were used to populate this domain ontology. • The statistical results obtained from the evaluation of this particular module of OntoTagger can be summarised as follows. On the one hand, as far as recall (R) is concerned, (R.1) the lowest value was 76,40% (for file 7); (R.2) the highest value was 97, 50% (for file 3); and (R.3) the average value was 88,73%. On the other hand, as far as the precision rate (P) is concerned, (P.1) its minimum was 93,75% (for file 4); (R.2) its maximum was 100% (for files 1, 5, 7, 8, 9, and 10); and (R.3) its average value was 98,99%. • These results, which apply to the tasks of named entity annotation and ontology population, are extraordinary good for both of them. They can be explained on the basis of the high accuracy of the annotations provided by OntoTagger at the lower levels (mainly at the morphosyntactic level). However, they should be conveniently qualified, since they might be too domain- and/or language-dependent. It should be further experimented how our approach works in a different domain or a different language, such as French, English, or German. • In any case, the results of this application of Human Language Technologies to Ontology Population (and, accordingly, to Ontological Engineering) seem very promising and encouraging in order for these two areas to collaborate and complement each other in the area of semantic annotation. Fifth, as shown in the State of the Art of this work, there are different approaches and models for the semantic annotation of texts, but all of them focus on a particular view of the semantic level. Clearly, all these approaches and models should be integrated in order to bear a coherent and joint semantic annotation level. OntoTag shows how (i) these semantic annotation layers could be integrated together; and (ii) they could be integrated with the annotations associated to other annotation levels. Sixth, we identified some recommendations, best practices and lessons learned for annotation standardisation, interoperation and merge. They show how standardisation (via ontologies, in this case) enables the combination, integration and interoperation of different linguistic tools and their annotations into a multilayered (or multileveled) linguistic annotation, which is one of the hot topics in the area of Linguistic Annotation. And last but not least, OntoTag’s annotation scheme and OntoTagger’s annotation schemas show a way to formalise and annotate coherently and uniformly the different units and features associated to the different levels and layers of linguistic annotation. This is a great scientific step ahead towards the global standardisation of this area, which is the aim of ISO/TC 37 (in particular, Subcommittee 4, dealing with the standardisation of linguistic annotations and resources).

Síntese e caracterização de biodiesel metílico e etílico a partir de blendas dos óleos de tungue e de soja

No presente trabalho foi investigada a transesterificação de blendas dos óleos de soja e de tungue com metanol ou etanol empregando catalisador alcalino (NaOH ou KOH). Foi investigado o tempo reacional, a proporção da blenda, a concentração e o tipo de catalisador, tipo de álcool e razão molar, temperatura e metodologia empregada no tratamento da reação. Nas reações com metanol obtiveram-se melhores conversões com tempo reacional de 1,5h; temperatura de 60°C; proporção blenda dos óleos de soja e de tungue de 90:10 (m/m); concentração de NaOH de 0,5% em relação a massa da blenda e razão molar metanol:blenda de 6:1. O tratamento dos ésteres metílicos produzidos na reação foi realizado por lavagem com água a 60°C após o processo de decantação das fases, metodologia C. O rendimento de ésteres metílicos foi superior a 96% e, o teor de mono-, di- e triacilglicerídeos, glicerol livre e total ficou abaixo dos limites estabelecidos pela ANP, indicando boa conversão (> 96,5%). Nas reações com etanol verificou-se que as melhores condições reacionais foram com uma concentração de catalisador de 0,8% de NaOH em relação a massa da blenda, razão molar etanol:blenda de 9:1, tempo de 1,5h e temperatura de 60°C. O tratamento dos produtos da reação foi realizado por lavagem com água a 60°C após o processo de remoção do etanol e decantação das fases, metodologia D. A concentração do catalisador foi um fator determinante na separação das fases. Uma maior concentração de catalisador favorece a saponificação, dificultando a separação das fases e afetando o rendimento do biodiesel sintetizado, tanto para o metílico quanto o etílico. O índice de acidez, tanto para o biodiesel metílico como o etílico, para qualquer proporção da blenda dos óleos de soja e tungue, ficaram dentro das normas da ANP, com valores abaixo de 0,5 mg.g-1 de KOH.