13 resultados para Insemination, Artificial

em Universidade Federal do Rio Grande do Norte(UFRN)


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LOPES, Jose Soares Batista et al. Application of multivariable control using artificial neural networks in a debutanizer distillation column.In: INTERNATIONAL CONGRESS OF MECHANICAL ENGINEERING - COBEM, 19, 5-9 nov. 2007, Brasilia. Anais... Brasilia, 2007

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MEDEIROS, Adelardo A. D. et al. SISAL - Um Sistema Supervisório para Elevação Artificial de Petróleo. In: Rio Oil and Gas Expo Conference, 2006, Rio de Janeiro, RJ. Anais... Rio de Janeiro, 2006.

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The major aim of this study was to test the hypothesis that the introduction of the Nile tilapia (Oreochromis niloticus) and the enrichment with nutrients (N and P) interact synergistically to change the structure of plankton communities, increase phytoplankton biomass and decrease water transparency of a semi-arid tropical reservoir. One field experiment was performed during five weeks in twenty enclosures (8m3) to where four treatments were randomly allocated: with tilapia addition (T), with nutrients addition (NP), with tilapia and nutrients addition (T+NP) and a control treatment with no tilapia or nutrients addition (C). A two-way repeated measures ANOVA was done to test for time (t), tilapia (T) and nutrient (NP) effects and their interaction on water transparency, total phosphorus, total nitrogen, phytoplankton and zooplankton. The results show that there was no effect of nutrient addition on these variables but significant fish effects on the biomass of total zooplankton, nauplii, rotifers, cladocerans and calanoid copepods, on the biovolume of Bacillariophyta, Zygnemaphyceae and large algae (GALD ≥ 50 μm) and on Secchi depth. In addition, we found significant interaction effects between tilapia and nutrients on Secchi depth and rotifers. Overall, tilapia decreased the biomass of most zooplankton taxa and large algae (diatoms) and decreased the water transparency while nutrient enrichment increased the biomass of zooplankton (rotifers) but only in the absence of tilapia. In conclusion, the influence of fish on the reservoir plankton community and water transparency was greater than that of nutrient loading. This finding suggests that biomanipulation should be a greater priority in the restoration of eutrophic reservoirs in tropical semi-arid regions

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The several existing methods for oil artificial lifting and the variety of automation equipment for these methods many times lead the supervisory systems to be dedicated to a unique method and/or to a unique manufacturer. To avoid this problem, it has been developed the supervisory system named SISAL, conceived to supervise wells with different lifting methods and different automation equipments. The SISAL system is working in several Brazilian states but, nowadays, it is only supervising rod pump-based wells. The objective of this work is the development of a supervision module to the plunger lift artificial lift method. The module will have the same characteristics of working with automation hardware of many manufacturers. The module will be integrated to the SISAL system, incorporating the capacity to supervise the plunger lift artificial lift method.

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From their early days, Electrical Submergible Pumping (ESP) units have excelled in lifting much greater liquid rates than most of the other types of artificial lift and developed by good performance in wells with high BSW, in onshore and offshore environments. For all artificial lift system, the lifetime and frequency of interventions are of paramount importance, given the high costs of rigs and equipment, plus the losses coming from a halt in production. In search of a better life of the system comes the need to work with the same efficiency and security within the limits of their equipment, this implies the need for periodic adjustments, monitoring and control. How is increasing the prospect of minimizing direct human actions, these adjustments should be made increasingly via automation. The automated system not only provides a longer life, but also greater control over the production of the well. The controller is the brain of most automation systems, it is inserted the logic and strategies in the work process in order to get you to work efficiently. So great is the importance of controlling for any automation system is expected that, with better understanding of ESP system and the development of research, many controllers will be proposed for this method of artificial lift. Once a controller is proposed, it must be tested and validated before they take it as efficient and functional. The use of a producing well or a test well could favor the completion of testing, but with the serious risk that flaws in the design of the controller were to cause damage to oil well equipment, many of them expensive. Given this reality, the main objective of the present work is to present an environment for evaluation of fuzzy controllers for wells equipped with ESP system, using a computer simulator representing a virtual oil well, a software design fuzzy controllers and a PLC. The use of the proposed environment will enable a reduction in time required for testing and adjustments to the controller and evaluated a rapid diagnosis of their efficiency and effectiveness. The control algorithms are implemented in both high-level language, through the controller design software, such as specific language for programming PLCs, Ladder Diagram language.

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In the artificial lift method by Electrical Submersible Pump (ESP), the energy is transmitted for the well´s deep through a flat electric handle, where it is converted into mechanical energy through an engine of sub-surface, which is connected to a centrifugal pump. This transmits energy to the fluid under the pressure form, bringing it to the surface In this method the subsurface equipment is basically divided into: pump, seal and motor. The main function of the seal is the protect the motor, avoiding the motor´s oil be contaminated by oil production and the consequent burning of it. Over time, the seal will be wearing and initiates a contamination of motor oil, causing it to lose its insulating characteristics. This work presents a design of a magnetic sensor capable of detecting contamination of insulating oil used in the artificial lift method of oil-type Electrical Submersible Pump (ESP). The objective of this sensor is to generate alarm signal just the moment when the contamination in the isolated oil is present, enabling the implementation of a predictive maintenance. The prototype was designed to work in harsh conditions to reach a depth of 2000m and temperatures up to 150°C. It was used a simulator software to defined the mechanical and electromagnetic variables. Results of field experiments were performed to validate the prototype. The final results performed in an ESP system with a 62HP motor showed a good reliability and fast response of the prototype.

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Artificial Intelligence techniques are applied to improve performance of a simulated oil distillation system. The chosen system was a debutanizer column. At this process, the feed, which comes to the column, is segmented by heating. The lightest components become steams, by forming the LPG (Liquefied Petroleum Gas). The others components, C5+, continue liquid. In the composition of the LPG, ideally, we have only propane and butanes, but, in practice, there are contaminants, for example, pentanes. The objective of this work is to control pentane amount in LPG, by means of intelligent set points (SP s) determination for PID controllers that are present in original instrumentation (regulatory control) of the column. A fuzzy system will be responsible for adjusting the SP's, driven by the comparison between the molar fraction of the pentane present in the output of the plant (LPG) and the desired amount. However, the molar fraction of pentane is difficult to measure on-line, due to constraints such as: long intervals of measurement, high reliability and low cost. Therefore, an inference system was used, based on a multilayer neural network, to infer the pentane molar fraction through secondary variables of the column. Finally, the results shown that the proposed control system were able to control the value of pentane molar fraction under different operational situations

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This study shows the implementation and the embedding of an Artificial Neural Network (ANN) in hardware, or in a programmable device, as a field programmable gate array (FPGA). This work allowed the exploration of different implementations, described in VHDL, of multilayer perceptrons ANN. Due to the parallelism inherent to ANNs, there are disadvantages in software implementations due to the sequential nature of the Von Neumann architectures. As an alternative to this problem, there is a hardware implementation that allows to exploit all the parallelism implicit in this model. Currently, there is an increase in use of FPGAs as a platform to implement neural networks in hardware, exploiting the high processing power, low cost, ease of programming and ability to reconfigure the circuit, allowing the network to adapt to different applications. Given this context, the aim is to develop arrays of neural networks in hardware, a flexible architecture, in which it is possible to add or remove neurons, and mainly, modify the network topology, in order to enable a modular network of fixed-point arithmetic in a FPGA. Five synthesis of VHDL descriptions were produced: two for the neuron with one or two entrances, and three different architectures of ANN. The descriptions of the used architectures became very modular, easily allowing the increase or decrease of the number of neurons. As a result, some complete neural networks were implemented in FPGA, in fixed-point arithmetic, with a high-capacity parallel processing

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The using of supervision systems has become more and more essential in accessing, managing and obtaining data of industrial processes, because of constant and frequent developments in industrial automation. These supervisory systems (SCADA) have been widely used in many industrial environments to store process data and to control the processes in accordance with some adopted strategy. The SCADA s control hardware is the set of equipments that execute this work. The SCADA s supervision software accesses process data through the control hardware and shows them to the users. Currently, many industrial systems adopt supervision softwares developed by the same manufacturer of the control hardware. Usually, these softwares cannot be used with other equipments made by distinct manufacturers. This work proposes an approach for developing supervisory systems able to access process information through different control hardwares. An architecture for supervisory systems is first defined, in order to guarantee efficiency in communication and data exchange. Then, the architecture is applied in a supervisory system to monitor oil wells that use distinct control hardwares. The implementation was modeled and verified by using the formal method of the Petri networks. Finally, experimental results are presented to demonstrate the applicability of the proposed solution

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Artificial neural networks are usually applied to solve complex problems. In problems with more complexity, by increasing the number of layers and neurons, it is possible to achieve greater functional efficiency. Nevertheless, this leads to a greater computational effort. The response time is an important factor in the decision to use neural networks in some systems. Many argue that the computational cost is higher in the training period. However, this phase is held only once. Once the network trained, it is necessary to use the existing computational resources efficiently. In the multicore era, the problem boils down to efficient use of all available processing cores. However, it is necessary to consider the overhead of parallel computing. In this sense, this paper proposes a modular structure that proved to be more suitable for parallel implementations. It is proposed to parallelize the feedforward process of an RNA-type MLP, implemented with OpenMP on a shared memory computer architecture. The research consistes on testing and analizing execution times. Speedup, efficiency and parallel scalability are analyzed. In the proposed approach, by reducing the number of connections between remote neurons, the response time of the network decreases and, consequently, so does the total execution time. The time required for communication and synchronization is directly linked to the number of remote neurons in the network, and so it is necessary to investigate which one is the best distribution of remote connections

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This work proposes a computer simulator for sucker rod pumped vertical wells. The simulator is able to represent the dynamic behavior of the systems and the computation of several important parameters, allowing the easy visualization of several pertinent phenomena. The use of the simulator allows the execution of several tests at lower costs and shorter times, than real wells experiments. The simulation uses a model based on the dynamic behavior of the rod string. This dynamic model is represented by a second order partial differencial equation. Through this model, several common field situations can be verified. Moreover, the simulation includes 3D animations, facilitating the physical understanding of the process, due to a better visual interpretation of the phenomena. Another important characteristic is the emulation of the main sensors used in sucker rod pumping automation. The emulation of the sensors is implemented through a microcontrolled interface between the simulator and the industrial controllers. By means of this interface, the controllers interpret the simulator as a real well. A "fault module" was included in the simulator. This module incorporates the six more important faults found in sucker rod pumping. Therefore, the analysis and verification of these problems through the simulator, allows the user to identify such situations that otherwise could be observed only in the field. The simulation of these faults receives a different treatment due to the different boundary conditions imposed to the numeric solution of the problem. Possible applications of the simulator are: the design and analysis of wells, training of technicians and engineers, execution of tests in controllers and supervisory systems, and validation of control algorithms

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Kerodon rupestris (rock cavy, mocó) is an endemic caviidae of Brazilian northeast that inhabits rocky places in the semi arid region. The aim of this study was to characterize the activity/rest rhythm of the rock cavy under 12:12 h LD cycle and continuous light. In the first stage, seven animals were submitted to two light intensities (LD; 250:0 lux and 400:0 lux; 40 days each intensity). In the second stage four males were kept for 40 days in LD (470:<1 lux), for 18 days in LL 470 lux (LL470) and for 23 days in red dim light below 1 lux (LL<1). In the third stage three males were initially kept in LD 12:12 h (450:<1 lux) and after that in LL with gradual increase in light intensity each 21 days (<1 lux LL<1; 10 lux-LL10; 160 lux LL160; 450 lux LL450). In the fourth stage it was analyzed the motor activity of 16 animals in the first 10 days in LD. Motor activity was continuously recorded by passive infrared movement sensors connected to a computer and totaled in 5 min bins. The activity showed circadian and ultradian rhythms and activity peaks at phase transitions. The activity and the rest occurred in the light as well as in the dark phase, with activity mean greater in the light phase for most of the animals. The light intensity influenced the activity/rest rhythm in the first three stages and in the first stage the activity in 400 lux increased in four animals and decreases in two. In the second stage, the tau for 3 animals in LL470 was greater than 24 h; in LL<1 it was greater than 24 h for one and lower for two. In the third stage the tau decreased with the light intensity increase for animal 8. During the first days in the experimental room, the animals did not synchronize to the LD cycle with activity and rest occurring in both phases. The results indicate that the activity/rest rhythm of Kerodon rupestris can be affected by light intensity and that the synchronization to the LD cycle results from entrainment as well as masking probably as a consequence of the action of two or more oscillators with low coupling strength

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One of the main environmental cues for the adjustment of temporal organization of the animals is the light-dark cycle (LD), which undergoes changes in phase duration throughout the seasons. Photoperiod signaling by melatonin in mammals allows behavioral changes along the year, as in the activity-rest cycle, in mood states and in cognitive performance. The aim of this study was to investigate if common marmoset (Callithrix jacchus) exhibits behavioral changes under short and long photoperiods in a 24h cycle, assessing their individual behaviors, vocal repertoire, exploratory activity (EA), recognition memory (RM) and the circadian rhythm of locomotor activity (CRA). Eight adult marmosets were exposed to a light-dark cycle of 12:12; LD 08:16; LD 12:12 and LD 16:08, sequentially, for four weeks in each condition. Locomotor activity was recorded 24h/day by passive infrared motion detectors above the individual cages. A video camera system was programmed to record each animal, twice a week, on the first two light hours. From the videos, frequency of behaviors was registered as anxiety-like, grooming, alert, hanging position, staying in nest box and feeding using continuous focal animal sampling method. Simultaneously, the calls emitted in the experimental room were recorded by a single microphone centrally located and categorized as affiliative (whirr, chirp), contact (phee), long distance (loud shrill), agonistic (twitter) and alarm (tsik, seep, see). EA was assessed on the third hour after lights onset on the last week of each condition. In a first session, marmosets were exposed to one unfamiliar object during 15 min and 24h later, on the second session, a novel object was added to evaluate RM. Results showed that long days caused a decreased of amplitude and period variance of the CRA, but not short days. Short days decreased the total daily activity and active phase duration. On long days, active phase duration increased due to an advance of activity onset in relation to symmetric days. However, not all subjects started the activity earlier on long days. The activity offset was similar to symmetric days for the majority of marmosets. Results of EA showed that RM was not affected by short or long days, and that the marmosets exhibited a decreased in duration of EA on long days. Frequency and type of calls and frequency of anxiety-like behaviors, staying in nest box and grooming were lower on the first two light hours on long days. Considering the whole active phase of marmosets as we elucidate the results of vocalizations and behaviors, it is possible that these changes in the first two light hours are due to the shifting of temporal distribution of marmoset activities, since some animals did not advance the activity onset on long days. Consequently, the marmosets mean decreased because the sampling was not possible. In conclusion, marmosets synchronized the CRA to the tested photoperiods and as the phase angle varied a lot among marmosets it is suggested that they can use different strategies. Also, long days had an effect on activity-rest cycle and exploratory behaviors