Avaliação de floculante natural à base de Moringa oleifera no tratamento de água produzida na indústria do petróleo: aplicação da técnica combinada floculação/flotação

The principal effluent in the oil industry is the produced water, which is commonly associated to the produced oil. It presents a pronounced volume of production and it can be reflected on the environment and society, if its discharge is unappropriated. Therefore, it is indispensable a valuable careful to establish and maintain its management. The traditional treatment of produced water, usualy includes both tecniques, flocculation and flotation. At flocculation processes, there are traditional floculant agents that aren’t well specified by tecnichal information tables and still expensive. As for the flotation process, it’s the step in which is possible to separate the suspended particles in the effluent. The dissolved air flotation (DAF) is a technique that has been consolidating economically and environmentally, presenting great reliability when compared with other processes. The DAF is presented as a process widely used in various fields of water and wastewater treatment around the globe. In this regard, this study was aimed to evaluate the potential of an alternative natural flocculant agent based on Moringa oleifera to reduce the amount of oil and grease (TOG) in produced water from the oil industry by the method of flocculation/DAF. the natural flocculant agent was evaluated by its efficacy, as well as its efficiency when compared with two commercial flocculant agents normally used by the petroleum industry. The experiments were conducted following an experimental design and the overall efficiencies for all flocculants were treated through statistical calculation based on the use of STATISTICA software version 10.0. Therefore, contour surfaces were obtained from the experimental design and were interpreted in terms of the response variable removal efficiency TOG (total oil and greases). The plan still allowed to obtain mathematical models for calculating the response variable in the studied conditions. Commercial flocculants showed similar behavior, with an average overall efficiency of 90% for oil removal, however it is the economical analysis the decisive factor to choose one of these flocculant agents to the process. The natural alternative flocculant agent based on Moringa oleifera showed lower separation efficiency than those of commercials one (average 70%), on the other hand this flocculant causes less environmental impacts and it´s less expensive

Redes sociais e a lógica do desvio comportamental em relação às normas de trabalho

This paper aim to check a hypothesis that assumes several behaviors related to social work norm´s obeying as a phenomenon that can be explained by actor´s social network structure and the rational choice processes related to the social norm inside that network, principally the payoff´s analysis received by the closest actors, or neighbors, at a social situation. Taking the sociological paradigm of rational action theory as a basis, the focus is on a debate about the logic of social norms, from Émile Durkheim´s method to Jon Elster´s theory, but also including social network analysis´s variables according to Robert Hanneman; and also Vilfredo Pareto´s constants related to human sociability, at the aim to detect elements that can help the scholars to develop an agent based model which could explain the sociological problem of deviance by a better way than the common sense´s view about morality and ethics at a social work environment

Agentes Inteligentes com Foco de Atenção Afetivo em Simulações Baseadas em Agentes

Simulations based on cognitively rich agents can become a very intensive computing task, especially when the simulated environment represents a complex system. This situation becomes worse when time constraints are present. This kind of simulations would benefit from a mechanism that improves the way agents perceive and react to changes in these types of environments. In other worlds, an approach to improve the efficiency (performance and accuracy) in the decision process of autonomous agents in a simulation would be useful. In complex environments, and full of variables, it is possible that not every information available to the agent is necessary for its decision-making process, depending indeed, on the task being performed. Then, the agent would need to filter the coming perceptions in the same as we do with our attentions focus. By using a focus of attention, only the information that really matters to the agent running context are perceived (cognitively processed), which can improve the decision making process. The architecture proposed herein presents a structure for cognitive agents divided into two parts: 1) the main part contains the reasoning / planning process, knowledge and affective state of the agent, and 2) a set of behaviors that are triggered by planning in order to achieve the agent s goals. Each of these behaviors has a runtime dynamically adjustable focus of attention, adjusted according to the variation of the agent s affective state. The focus of each behavior is divided into a qualitative focus, which is responsible for the quality of the perceived data, and a quantitative focus, which is responsible for the quantity of the perceived data. Thus, the behavior will be able to filter the information sent by the agent sensors, and build a list of perceived elements containing only the information necessary to the agent, according to the context of the behavior that is currently running. Based on the human attention focus, the agent is also dotted of a affective state. The agent s affective state is based on theories of human emotion, mood and personality. This model serves as a basis for the mechanism of continuous adjustment of the agent s attention focus, both the qualitative and the quantative focus. With this mechanism, the agent can adjust its focus of attention during the execution of the behavior, in order to become more efficient in the face of environmental changes. The proposed architecture can be used in a very flexibly way. The focus of attention can work in a fixed way (neither the qualitative focus nor the quantitaive focus one changes), as well as using different combinations for the qualitative and quantitative foci variation. The architecture was built on a platform for BDI agents, but its design allows it to be used in any other type of agents, since the implementation is made only in the perception level layer of the agent. In order to evaluate the contribution proposed in this work, an extensive series of experiments were conducted on an agent-based simulation over a fire-growing scenario. In the simulations, the agents using the architecture proposed in this work are compared with similar agents (with the same reasoning model), but able to process all the information sent by the environment. Intuitively, it is expected that the omniscient agent would be more efficient, since they can handle all the possible option before taking a decision. However, the experiments showed that attention-focus based agents can be as efficient as the omniscient ones, with the advantage of being able to solve the same problems in a significantly reduced time. Thus, the experiments indicate the efficiency of the proposed architecture

Uma análise da aplicação do modelo de Rede Neural RePART em Comitês de classificadores

RePART (Reward/Punishment ART) is a neural model that constitutes a variation of the Fuzzy Artmap model. This network was proposed in order to minimize the inherent problems in the Artmap-based model, such as the proliferation of categories and misclassification. RePART makes use of additional mechanisms, such as an instance counting parameter, a reward/punishment process and a variable vigilance parameter. The instance counting parameter, for instance, aims to minimize the misclassification problem, which is a consequence of the sensitivity to the noises, frequently presents in Artmap-based models. On the other hand, the use of the variable vigilance parameter tries to smoouth out the category proliferation problem, which is inherent of Artmap-based models, decreasing the complexity of the net. RePART was originally proposed in order to minimize the aforementioned problems and it was shown to have better performance (higer accuracy and lower complexity) than Artmap-based models. This work proposes an investigation of the performance of the RePART model in classifier ensembles. Different sizes, learning strategies and structures will be used in this investigation. As a result of this investigation, it is aimed to define the main advantages and drawbacks of this model, when used as a component in classifier ensembles. This can provide a broader foundation for the use of RePART in other pattern recognition applications

Um framework de agentes de recomendação para sistemas Web

The World Wide Web has been consolidated over the last years as a standard platform to provide software systems in the Internet. Nowadays, a great variety of user applications are available on the Web, varying from corporate applications to the banking domain, or from electronic commerce to the governmental domain. Given the quantity of information available and the quantity of users dealing with their services, many Web systems have sought to present recommendations of use as part of their functionalities, in order to let the users to have a better usage of the services available, based on their profile, history navigation and system use. In this context, this dissertation proposes the development of an agent-based framework that offers recommendations for users of Web systems. It involves the conception, design and implementation of an object-oriented framework. The framework agents can be plugged or unplugged in a non-invasive way in existing Web applications using aspect-oriented techniques. The framework is evaluated through its instantiation to three different Web systems