The study was aimed to determine the hydrodynamic of water seepage through a porous bed saturated with different amounts of high viscosity liquids. An attempt was made to describe the process of seepage through beds saturated with oils using the theory of outflow of a liquid from the tank. It was assumed that the discharge coefficient will represent changes of flow resistance during the process. It was found that the dependence of this factor on time is linear. In the second part of this work kinetics of the seepage process was investigated. Dependence of oil concentrations, eluted from the deposit with the flowing water, on time has been evaluated. Thanks to these studies it was possible to determine the effectiveness of an elution of high viscosity liquids from porous beds using water as the washing out liquid.
The objective of present work is to predict the thermal performance of wire screen porous bed solar air heater using artificial neural network (ANN) technique. This paper also describes the experimental study of porous bed solar air heaters (SAH). Analysis has been performed for two types of porous bed solar air heaters: unidirectional flow and cross flow. The actual experimental data for thermal efficiency of these solar air heaters have been used for developing ANN model and trained with Levenberg-Marquardt (LM) learning algorithm. For an optimal topology the number of neurons in hidden layer is found thirteen (LM-13).The actual experimental values of thermal efficiency of porous bed solar air heaters have been compared with the ANN predicted values. The value of coefficient of determination of proposed network is found as 0.9994 and 0.9964 for unidirectional flow and cross flow types of collector respectively at LM-13. For unidirectional flow SAH, the values of root mean square error, mean absolute error and mean relative percentage error are found to be 0.16359, 0.104235 and 0.24676 respectively, whereas, for cross flow SAH, these values are 0.27693, 0.03428, and 0.36213 respectively. It is concluded that the ANN can be used as an appropriate method for the prediction of thermal performance of porous bed solar air heaters.