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

Recovering Energy at Entry of Natural Gas into Customer Premises by Employing a Counter-Flow Vortex Tube

Throttling valves are currently utilised to reduce high-pressure natural gas flowing through the distribution pipeline to the working level of customers’ equipment. This wastes valuable energy of the gas. Due to low natural gas consumption at customer premises, it is not feasible to utilise expansion machines. In this study, a new idea is proposed to take advantage of the Vortex Tube and natural gas pressure reduction. The idea is to replace the throttling valve with a Vortex Tube in the natural gas pressure reduction system and take advantage of the generated cooling capacity. An experimental investigation was made to determine the effects of the cold orifice diameter and the energy separation of the counter-flow Vortex Tube when air and natural gas are used as the fluid. The energy separation was investigated by use of the experimentally obtained data

Developing novel correlations for calculating natural gas thermodynamic properties

Natural gas is a mixture of 21 components and it is widely used in industries and homes. Knowledge of its thermodynamic properties is essential for designing appropriate processes and equipment. This paper presents simple but precise correlations of how to compute important thermodynamic properties of natural gas. As measuring natural gas composition is costly and may not be effective for real time process, the correlations are developed based on measurable real time properties. The real time properties are temperature, pressure and specific gravity of the natural gas. Calculations with these correlations are compared with measured values. The validations show that the average absolute percent deviation (AAPD) for compressibility factor calculations is 0.674%, for density is 2.55%, for Joule-Thomson coefficient is 4.16%. Furthermore, in this work, new correlations are presented for computing thermal properties of natural gas such as enthalpy, internal energy and entropy. Due to the lack of experimental data for these properties, the validation is done for pure methane. The validation shows that AAPD is 1.31%, 1.56% and 0.4% for enthalpy, internal energy and entropy respectively. The comparisons show that the correlations could predict natural gas properties with an error that is acceptable for most engineering applications