In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi

This work presents an experimental study of heat transfer and flow of distilled water and metal oxide nanofluid Fe₃O₄-distilled water at concentrations of (φ = 0.3, 0.6, 0.9 %) by volume in a horizontal pipe with constant magnetic field. All the tests are carried out with Reynolds number range (2900-9820) and uniform heat flux (11262-19562 W/m²). The results show that, the nanofluid concentration and magnetic intensity increase, the Nusselt number increases. The maximum enhancement in Nusselt number with magnetic nanofluid is (5.4 %, 26.4 %, 42.7 %) for volume concentration (0.3, 0.6, 0.9 %) respectively. The enhancement is maximized with magnetic intensity (0.1, 0.2, 0.3 tesla) respectively to (43.9, 44

The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust a

When soft tissue planning is important, usually, the Magnetic Resonance Imaging (MRI) is a medical imaging technique of selection. In this work, we show a modern method for automated diagnosis depending on a magnetic resonance images classification of the MRI. The presented technique has two main stages; features extraction and classification. We obtained the features corresponding to MRI images implementing Discrete Wavelet Transformation (DWT), inverse and forward, and textural properties, like rotation invariant texture features based on Gabor filtering, and evaluate the meaning of every

       In this paper, we focus on designing feed forward neural network (FFNN) for solving Mixed Volterra – Fredholm Integral Equations (MVFIEs) of second kind in 2–dimensions. in our method, we present a multi – layers model consisting of a hidden layer which has five hidden units (neurons) and one linear output unit. Transfer function (Log – sigmoid) and training algorithm (Levenberg – Marquardt) are used as a sigmoid activation of each unit. A comparison between the results of numerical experiment and the analytic solution of some examples has been carried out in order to justify the efficiency and the accuracy of our method.

Enhancement of heat transfer in the tube heat exchanger is studied experimentally by using discrete twisted tapes. Three different positions were selected for inserting turbulators along tube section (horizontal position by α= 0⁰, inclined position by α= 45 ⁰ and vertical position by α= 90⁰). The space between turbulators was fixed by distributing 5 pieces of these turbulators with pitch ratio    PR = (0.44). Also, the factor of constant heat flux was applied as a boundary condition around the tube test section for all experiments of this investigation, while the flow rates were selected as a variable factor (Reynolds number values vary from 5000 to 15000). The results s

Although renewable energy systems have become an interesting global issue, it is not continuous either daily or seasonally. Latent heat energy storage (LHES) is one of the suitable solutions for this problem. LHES becomes a basic element in renewable energy systems. LHES compensate for the energy lack when these systems are at low production conditions. The present work considered a shell and tube LHES for numerical investigation of the tube rotation influence on the melting process. The simulation and calculations were carried out using ANSYS Fluent software. Paraffin wax represents the phase change material (PCM) in this work, while water was selected to be the heat transfer fluid (HTF). The calculations were carried o

The concept of entransy dissipation was determined for new type of heat exchanger (shell and double concentric tubes heat exchanger). Three parameters, hot oil flow rate, temperature of inlet hot oil and pressure drop of system were investigated with this concept (entransy dissipation). The results showed that the value of entransy dissipation of oil and of system which represents the summation of entransy dissipation of both oil and water increased with increasing the flow rate of hot oil and these values were larger when cold water flow rate was doubled. Also they were increased with increasing the hot oil inlet temperature at a certain flow rate of hot oil. Furthermore, the pressure drops for hot oil in both shell side and inner tubes

Entropy generation was studied for new type of heat exchanger (shell and double concentric tubes heat exchanger). Parameters of hot oil flow rate, temperature of inlet hot oil and pressure drop were investigated with the concept of entropy generation. The results showed that the value of entropy generation increased with increasing the flow rate of hot oil and when cold water flow rate was doubled from 20 to 40 l/min, these values were larger. On the other hand, entropy generation increased with increasing the hot oil inlet temperature at a certain flow rate of hot oil. Furthermore, at a certain hot oil inlet temperature, the entropy generation increased with the pressure drop at different hot oil inlet flow rates. Final