Artificial Neural Networks model for predicting wall temperature of supercritical boilers

Prediction of Pressure Drop of Al2O3-Water Nanofluid in Flat Tubes Using CFD and Artificial Neural Networks

In the present study, Computational Fluid Dynamics (CFD) techniques and Artificial Neural Networks (ANN) are used to predict the pressure drop value (Δp ) of Al2O3-water nanofluid in flat tubes. Δp  is predicted taking into account five input variables: tube flattening (H), inlet volumetric flow rate (Qi  ), wall heat flux (qnw  ), nanoparticle volume fraction (Φ) and nanoparticle diameter (dp ...

Developing an Artificial Neural Networks Model for Predicting Output Energy and GHG Emission of Strawberry Production

The Prediction of Surface Tension of Ternary Mixtures at Different Temperatures Using Artificial Neural Networks

In this work, artificial neural network (ANN) has been employed to propose a practical model for predicting the surface tension of multi-component mixtures. In order to develop a reliable model based on the ANN, a comprehensive experimental data set including 15 ternary liquid mixtures at different temperatures was employed. These systems consist of 777 data points generally containing hydrocar...

PREDICTION OF COMPRESSIVE STRENGTH AND DURABILITY OF HIGH PERFORMANCE CONCRETE BY ARTIFICIAL NEURAL NETWORKS

Neural networks have recently been widely used to model some of the human activities in many areas of civil engineering applications. In the present paper, artificial neural networks (ANN) for predicting compressive strength of cubes and durability of concrete containing metakaolin with fly ash and silica fume with fly ash are developed at the age of 3, 7, 28, 56 and 90 days. For building these...

Prediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine

Two main objectives have been considered in this paper: providing a good model to predict the critical temperature and pressure of binary hydrocarbon mixtures, and comparing the efficiency of the artificial neural network algorithms and the support vector regression as two commonly used soft computing methods. In order to have a fair comparison and to achieve the highest efficiency, a comprehen...