in this work, response surface methodology (rsm) and artificial neural network (ann) were used to predict the decolorization efficiency of reactive red 33 (rr 33) by o3/uv process in a bubble column reactor. the effects of four independent variables including time (20-60 min), superficial gas velocity (0.06-0.18 cm/s), initial concentration of dye (50-150 ppm) and ph (3-11) were investigated us...

In this work, the artificial neural networks (ANN) technology was applied to the simulation of oleuropein extraction process. For this technology, a 3-layer network structure is applied, and the operation factors such as  amount  of  flow  intensity  ratio,  temperature,  residence  time,  and  pH  are  used  as  input  variables  of  the network,  whereas  the  extraction  yield  is  considere...

infiltration rate is one of the most important soil physical parameters and is a basic input data in irrigation and drainage projects. although, a number of theoretical or experimental based equations are presented to describe this phenomenon but the evaluation of some new sciences such as artificial neural networks, for prediction of the phenomenon can be investigated. generally, the infiltrat...

Multivariable regression and artificial neural network procedures were used to modeling of the input power and gas holdup of flotation. The stepwise nonlinear equations have shown greater accuracy than linear ones where they can predict input power, and gas holdup with the correlation coefficients of 0.79 thereby 0.51 in the linear, and R2=0.88 versus 0.52 in the non linear, respectively. ...

this research intends to develop a method based on the artificial neural network (ann) to predict permanent earthquake-induced deformation of the earth dams and embankments. for this purpose, data sets of observations from 152 published case histories on the performance of the earth dams and embankments, during the past earthquakes, was used. in order to predict earthquake-induced deformation o...

it is necessary to use empirical models for estimating of instantaneous peak discharge because of deficit of gauging stations in the country. hence, at present study, two models including artificial neural networks and nonlinear multivariate regression were used to predict peak discharge in taleghan watershed. maximum daily mean discharge and corresponding daily rainfall, one day antecedent and...

evaporation is one of the most important components of hydrologic cycle.accurate estimation of this parameter is used for studies such as water balance,irrigation system design, and water resource management. in order to estimate theevaporation, direct measurement methods or physical and empirical models can beused. using direct methods require installing meteorological stations andinstruments ...

In recent years, biodiesel has been considered as a good alternative of diesel fuels. Density and viscosity are two important properties of these fuels. In this study, density and kinematic viscosity of biodiesel-diesel blends were estimated by using artificial neural network (ANN). A three-layer feed forward neural network with Levenberg-Marquard (LM) algorithm was used for learning empirical ...

An artificial neural network can be used as an intelligent controller to control non-linear, dynamic system through learning. It can easily accommodate non-linearities and time dependencies. Most common multi-layer feed-forward neural networks have the drawbacks of large number of neurons and hidden layers required to deal with complex problems and require large training time. To overcome these...

In recent years, biodiesel has been considered as a good alternative of diesel fuels. Density and viscosity are two important properties of these fuels. In this study, density and kinematic viscosity of biodiesel-diesel blends were estimated by using artificial neural network (ANN). A three-layer feed forward neural network with Levenberg-Marquard (LM) algorithm was used for learning empirical ...