A device known as a pipeline inspection gauge (PIG) runs through oil and gas pipelines which performs various maintenance operations in the oil and gas industry. The PIG velocity, which plays a role in the efficiency of these operations, is usually determined indirectly from odometers installed in it. Although this is a relatively simple technique, the loss of contact between the odometer wheel and the pipeline results in measurement errors. To help reduce these errors, this investigation employed neural networks to estimate the speed of a prototype PIG, using the pressure difference that acts on the device inside the pipeline and its acceleration instead of using odometers. Static networks (e.g., multilayer perceptron) and recurrent networks (e.g., long short-term memory) were built, and in addition, a prototype PIG was developed with an embedded system based on Raspberry Pi 3 to collect speed, acceleration and pressure data for the model training. The implementation of the supervised neural networks used the Python library TensorFlow package. To train and evaluate the models, we used the PIG testing pipeline facilities available at the Petroleum Evaluation and Measurement Laboratory of the Federal University of Rio Grande do Norte (LAMP/UFRN). The results showed that the models were able to learn the relationship among the differential pressure, acceleration and speed of the PIG. The proposed approach can complement odometer-based systems, increasing the reliability of speed measurements.

This paper presents the implementation of a fuzzy control strategy for speed regulation of an electromagnetic frequency regulator (EFR) prototype, aiming to eliminate the dependence on knowledge of physical parameters in the most diverse operating conditions. Speed multiplication is one of the most important steps in wind power generation. Gearboxes are generally used for this purpose. However, they have a reduced lifespan and a high failure rate, and are still noise sources. The search for new ways to match the speed (and torque) between the turbine and the generator is an important research area to increase the energy, financial, and environmental efficiency of wind systems. The EFR device is an example of an alternative technology that this team of researchers has proposed. It considers the main advantages of an induction machine with the rotor in a squirrel cage positively. In the first studies, the EFR control strategy consisted of the conventional PID controllers, which have several limitations that are widely discussed in the literature. This strategy also limits the EFR’s performance, considering its entire operating range. The simulation program was developed using the Matlab/Simulink platform, while the experimental results were obtained in the laboratory emulating the EFR-based system. The EFR prototype has 2 poles, a nominal power of 2.2 kW, and a nominal frequency of 60 Hz. Experimental results were presented to validate the efficiency of the proposed control strategy.

The growing expansion of photovoltaic systems results in the need to perform maintenance routines and inspections in order to ensure that components and equipment function as expected, that is, normal productivity, operate with low risks to health, environment and safety. In this work, an analysis was carried out in the photovoltaic solar plant of UFERSA-Caraúbas, which included visual inspection and qualitative thermographic analysis, where the first one had the purpose of verifying the components and the photovoltaic arrangement itself, evidencing the factors considered abnormal; and the second, to recognize possible thermal anomalies present in the PV system and/or its components. Finally, it was possible to verify that the system in question has a considerable amount of modules with broken surface glass, in addition to the relevant presence of dirt on their surfaces. The thermographic inspection showed that the modules together with the inverters remain within the limit range determined by the manufacturers.

This paper presents the implementation of a control strategy based on fuzzy logic artificial intelligence (AI) for speed regulation of an electromagnetic frequency regulator (EFR) prototype, aiming to eliminate the dependence on knowledge of physical parameters in the most diverse operating conditions. Speed multiplication is one of the most important steps in power generation wind. Gearboxes are generally used for this purpose. However, they have a reduced lifespan and a high failure rate and are still noise sources. The search for new ways to match the speed (and torque) between the turbine and the generator is an important research area to increase the energy, financial and environmental efficiency of wind systems. The EFR device is an example of an alternative technology that this team of researchers has proposed. It counts the fact of taking advantage of the main advantages of an induction machine with the rotor in a squirrel cage positively. In the first studies, the EFR control strategy consisted of the conventional PID controllers, which has several limitations widely discussed in the literature. This strategy also limits the EFR's performance, considering its entire operating range. The simulation program was developed using the Matlab/Simulink platform, while the experimental results were obtained in the laboratory emulating the EFR-based system. The EFR prototype used has 2 poles, a nominal power of 2.2 kW, and a nominal frequency of 60 Hz. Experimental results were presented to validate the efficiency of the proposed control strategy.

Due to the world's energy consumption, the search for alternative means for the generation of electricity in a clean and sustainable way was made. It is in this scenario that photovoltaic solar energy has its prominence in the market, being a technology with low cost to invest in implementation if we compare with other generating sources, in addition providing a return on investment. This work presents a dimensioning of a photovoltaic residential project connected to the electrical network, the system consists of 24 photovoltaic panels with rated power of 9.60 (kWp). An analysis of the economic viability of the project is also made through a simulation of energy produced x consumed. With the results it was possible to ascertain the behavior of the system if it really is economically viable even with its medium-term return on investment time of approximately 60 months. Devido ao consumo mundial energético, se fez a busca por meios alternativos para a geração de energia elétrica de forma limpa e sustentável. É diante desse cenário que a energia solar fotovoltaica tem seu destaque no mercado, sendo uma tecnologia com custo baixo para investir na implementação se formos comparar com outras fontes geradoras, além disso proporcionando um retorno de investimento. Esse trabalho apresenta um dimensionamento de um projeto residencial fotovoltaico conectado à rede elétrica, o sistema é composto por 24 painéis fotovoltaicos com potência nominal de 9,60 (kWp). Também é feito uma análise da viabilidade econômica do projeto por meio de uma simulação energia produzida x consumida. Com os resultados foi possível averiguar o comportamento do sistema se realmente é viável economicamente mesmo com seu tempo de retorno de investimento a médio prazo, de aproximadamente 60 meses.

Introducción: El ácido docosahexaenoico (DHA) se deposita en regiones cerebrales e interviene en la funcionalidad neuronal. Debido a esta importante participación se considera medular en el desarrollo del sistema nervioso central. El objetivo fue analizar los resultados de estudios sobre la influencia de la suplementación con ácido docosahexaenoico en gestantes sobre los procesos cognitivos de sus hijos.Material y Métodos: Revisión sistemática de ensayos controlados aleatorizados con suplementación con DHA en embarazadas considerando los lineamientos del grupo Cochrane. Se buscaron artículos en MEDLINE, EMBASE, Scopus y la Biblioteca Cochrane desde enero del 2003 a mayo 2018. Se eligieron estudios que compararon los efectos de la suplementación en embarazadas con los del placebo respecto a evaluaciones de los procesos cognitivos. Se evaluó el riesgo de sesgo de los mismos con la herramienta de colaboración Cochrane.Resultados: Se hallaron 147 artículos científicos eligiéndose 10 que tuvieron como características metodológicas el ser de doble ciego aleatorizados. Los estudios involucraron 4506 gestantes entre las 15 y 21 semanas y 2045 niños. La suplementación se dió entre la semana quince y los tres meses después del parto. Los niños fueron evaluados desde el año y medio hasta los cuatro años. Se utilizaron seis instrumentos y dos baterías de ejecución para medir 19 procesos cognitivos. Solamente dos de los estudios reportaron diferencias significativas entre los sujetos del grupo control y experimental. Debido a la heterogeneidad entre los estudios en relación a los tamaños muestrales, las dosis y a la forma de medir la variable de interés no fue posible realizar metaanálisis. Conclusiones: Resulta insuficiente la evidencia científica para afirmar o negar un efecto positivo de la suplementación temprana con DHA en los procesos cognitivos infantiles. La recomendación del uso de suplementos con DHA durante el período gestacional para favorecerlos no se encuentra justificada.