in this study, activated sludge process for wastewater treatment in a refinery was investigated. for such purpose, a laboratory scale rig was built. the effect of several parameters such as temperature, residence time, effect of leca (filling-in percentage of the reactor by leca) and uv radiation on cod removal efficiency were experimentally examined. maximum cod removal efficiency was obtained...

Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, ...

In this article, the effect of operating conditions, such as temperature, Gas Hourly Space Velocity (GHSV), CH4/O2 ratio and diluents gas (mol% N2) on ethylene production by Oxidative Coupling of Methane (OCM) in a fixed bed reactor at atmospheric pressure was studied over Mn/Na2WO4/SiO2 ca...

In this paper, vapor pressure for pure compounds is estimated using the Artificial Neural Networks and a simple Group Contribution Method (ANN–GCM). For model comprehensiveness, materials were chosen from various families. Most of materials are from 12 families. Vapor pressure data of 100 compounds is used to train, validate and test the ANN-GCM model. Va...

in this study, artificial neural network was used to predict the surface tension of 20 hydrocarbon mixtures. experimental data was divided into two parts (70% for training and 30% for testing). optimal configuration of the network was obtained with minimization of prediction error on testing data. the accuracy of our proposed model was compared with four well-known empirical equations. the arti...

In this paper back-propagation artificial neural network (BPANN) with Levenberg–Marquardt algorithm is employed to predict the limiting drawing ratio (LDR) of the deep drawing process. To prepare a training set for BPANN, some finite element simulations were carried out. die and punch radius, die arc radius, friction coefficient, thickness, yield strength of sheet and strain hardening exponent ...

The Wireless Sensor Network needs to become a dynamic and adaptive network conserve energy stored in the wireless sensor node battery. This sometimes are called SON (Self Organizing Network). Several concepts have been developed such as routing, clustering, intrusion detection, other. Although several already exist, however, there is no concept for radio configuration. Therefore, authors’ contr...

BP Neural Network has a longer training time and a slow convergence. To deal with the defects of BP Neural Network a modified BP algorithm is proposed in the paper. The algorithm is applied for the control of Inverted Pendulum, a highly non linear system inherently being open loop unstable. Levenberg-Marquardt algorithm is used for the training purpose. The training samples are being collected ...

This paper presents Artificial Neural Network (ANN) implementation for Mechanical modeling of Radio Frequency Micro Electro Mechanical System (RF MEMS) lateral double beam switch. We propose an efficient approach based on ANN for analyzing the static and dynamic characteristics of RF MEMS lateral switch by calculating its characteristics parameters. ANN model were trained with five learning alg...