Time filter

Source Type

Abedini S.,Imam Khomeini International University | Zarabadipour H.,International University of Qazvin
Proceedings - 2011 2nd International Conference on Control, Instrumentation and Automation, ICCIA 2011 | Year: 2012

Tuning of PID controller parameters is one of the usual tasks of the control engineers due to the wide applications of this class of controllers in industry. In this paper the Iterative Feedback Tuning (IFT) method is applied to tune the PID parameters. The main advantage of this method is that there is no need to the model of the system, so that is useful in many processes which there is no obvious model of the system. In many cases this feature can be so useful in tuning the controller parameters. The IFT is applied here to tune the PID parameters. Speed control of DC motor was employed to demonstrate the effectiveness of the method. The results is compared with other tuning methods and represented the good performance of the designed controller. As it is shown, the step response of the system controlled by PID tuned with IFT has more robustness and performs well. © 2011 IEEE.

Riahi-Madvar H.,Tarbiat Modares University | Riahi-Madvar H.,P.A. College | Ayyoubzadeh S.A.,Tarbiat Modares University | Atani M.G.,International University of Qazvin
Expert Systems with Applications | Year: 2011

Cross section geometry of stable alluvial channels usually is estimated by simple inaccurate empirical equations, because of the complexity of the phenomena and unknown physical processes of regime channels. So, the main purpose of this study is to evaluate the potential of simulating regime channel treatments using artificial neural networks (ANNs). The process of training and testing of this new model is done using a set of available published filed data (371 data numbers). Several statistical and graphical criterions are used to check the accuracy of the model in comparison with previous empirical equations. The multilayer perceptron (MLP) artificial neural network was used to construct the simulation model based on the training data using back-propagation algorithm. The results show a considerably better performance of the ANN model over the available empirical or rational equations. The constructed ANN models can almost perfectly simulate the width, depth and slope of alluvial regime channels, which clearly describes the dominant geometrical parameters of alluvial rivers. The results demonstrate that the ANN can precisely simulate the regime channel geometry, while the empirical, regression or rational equations can't do this. The presented methodology in this paper is a new approach in establishing alluvial regime channel relations and predicting cross section geometry of alluvial rivers also it can be used to design stable irrigation and water conveyance channels. © 2010 Elsevier Ltd. All rights reserved.

Vasheghani F.B.,International University of Qazvin | Rajabi F.H.,International University of Qazvin | Omidi M.H.,International University of Qazvin | Shabanian S.,International University of Qazvin
Russian Journal of Physical Chemistry A | Year: 2015

Magnetic Fe3O4/bentonite nanocomposite is synthesized by chemical co-precipitation method. Experimental data are modelled by Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich isotherms. Freundlich and Langmuir isotherm model fitted the equilibrium data for the dibenzylamine (DBA) and 2-biphenylamine (BPA) respectively, compared to the other isotherm models. The calculated thermodynamic parameters, ΔG°, ΔH°, and ΔS° showed that the DBA and BPA adsorption on bentonite nanocomposite is spontaneous and endothermic under examined conditions. Experimental data were also modeled using the adsorption kinetic models. The results show that the adsorption processes of DBA and BPA followed well the pseudo-second-order kinetics. Results indicated that Fe3O4/bentonite nanocomposite could be an alternative for more costly adsorbents used for organic toxicants removal. © 2015 Pleiades Publishing, Ltd.

Loading International University of Qazvin collaborators
Loading International University of Qazvin collaborators