Numerical simulation of pressure pulsation effects of a snubber in a CNG station for increasing measurement accuracy

Natural companies employ turbine flow meters to measure natural gas which delivered to Compressed Natural Gas stations. The stations utilize compressors to increase pressure. The compressor produces a flow pulsation, which affects the accuracy of the measurement. The main aim of this article is to decrease the compressor effects on measurement accuracy by utilizing a snubber between the turbine flow meter and the reciprocating compressors. For this aim, numerical modeling has been built to simulate natural gas flow through a snubber. The effects of various snubber parameters on pressure pulsation have been investigated. The parameters included snubber volume to the minimum volume ratio, the ratio of height to diameter, outlet pipe length, and the existence and non-existence a buffer. The Ansys Fluent has been used for numerical modeling with transient analysis. Results show that in H/D value of 3, the maximum reduction in the percentage of pressure pulsation drop is about 47% and increasing the outlet pipe length to the 10 times of initial length causes a decrease of about 83% in pressure pulsations. Besides, for the ratio of snubber volume to the minimum volume from 1 to 16.7, the amplitude of pressure pulsations decreases from 4.1% to 0.25%

Experimental characterization of a grid-connected hydrogen energy buffer: Hydrogen production

Energy storage becomes a necessity when a high penetration of renewable energy sources is desirable. Variability in the energy production from these types of energy sources can make the utility grid unstable, if the percentage of production is important. In order to minimize this problem, the HiDRENER project was designed to study the effect of combining different renewable energy sources with energy storage on grid stability. The system has a wind generator, a gasifying biomass power plant with syngas storage, a solar photovoltaic plant, and a hydrogen energy buffer. Controlling the entire system is very complex. This paper shows the results of the grid-connected hydrogen energy buffer characterization, considering hydrogen production in this first stage. The objective is to know the complete behavior of the system, which could help us to define the energy buffer control. This control is oriented toward consuming excess energy produced by the other sources in real time. This means that the hydrogen buffer control has to negotiate how much energy can be stored, and act on the production system. Thus, actuation variables and dynamic behavior have to be discovered.The authors thank the Ministerio de Educacion y Ciencia of Spain for the financial support of this research through Proyectos de Infraestructura Cientifico-tecnologica program.Sánchez Díaz, C.; González, D. (2013). Experimental characterization of a grid-connected hydrogen energy buffer: Hydrogen production. International Journal of Hydrogen Energy. 38(23):9741-9754. doi:10.1016/j.ijhydene.2013.05.096S97419754382

Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system