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Stojanovic B.,University of Kragujevac | Milivojevic M.,Technical and Business College | Ivanovic M.,University of Kragujevac | Milivojevic N.,Jaroslav Cerni Institute for the Development of Water Resources | Divac D.,Jaroslav Cerni Institute for the Development of Water Resources
Advances in Engineering Software | Year: 2013

Most of the existing methods for dam behavior modeling require a persistent set of input parameters. In real-world applications, failures of the measuring equipment can lead to a situation in which a selected model becomes unusable because of the volatility of the independent variables set. This paper presents an adaptive system for dam behavior modeling that is based on a multiple linear regression (MLR) model and is optimized for given conditions using genetic algorithms (GA). Throughout an evolutionary process, the system performs real-time adjustment of regressors in the MLR model according to currently active sensors. The performance of the proposed system has been evaluated in a case study of modeling the Bocac dam (at the Vrbas River located in the Republic of Srpska), whereby an MLR model of the dam displacements has been optimized for periods when the sensors were malfunctioning. Results of the analysis have shown that, under real-world circumstances, the proposed methodology outperforms traditional regression approaches. © 2013 Elsevier Ltd. All rights reserved.


Stojanovic B.,University of Kragujevac | Milivojevic M.,Technical and Business College | Milivojevic N.,Jaroslav Cerni Institute for the Development of Water Resources | Antonijevic D.,University of Kragujevac
Advances in Engineering Software | Year: 2016

Most of the existing methods for dam behavior modeling presuppose temporal immutability of the modeled structure and require a persistent set of input parameters. In real-world applications, permanent structural changes and failures of measuring equipment can lead to a situation in which a selected model becomes unusable. Hence, the development of a system capable to automatically generate the most adequate dam model for a given situation is a necessity. In this paper, we present a self-tuning system for dam behavior modeling based on artificial neural networks (ANN) optimized for given conditions using genetic algorithms (GA). Throughout an evolutionary process, the system performs near real-time adjustment of ANN architecture according to currently active sensors and a present measurement dataset. The model was validated using the Grancarevo dam case study (at the Trebisnjica river located in the Republic of Srpska), where radial displacements of a point inside the dam structure have been modeled as a function of headwater, temperature, and ageing. The performance of the system was compared to the performance of an equivalent hybrid model based on multiple linear regression (MLR) and GA. The results of the analysis have shown that the ANN/GA hybrid can give rather better accuracy compared to the MLR/GA hybrid. On the other hand, the ANN/GA has shown higher computational demands and noticeable sensitivity to the temperature phase offset present at different geographical locations. © 2016 Elsevier Ltd. All rights reserved.


Milivojevic M.,Technical and Business College | Stopic S.,RWTH Aachen | Friedrich B.,RWTH Aachen | Stojanovic B.,University of Kragujevac | Drndarevic D.,Technical and Business College
International Journal of Minerals, Metallurgy and Materials | Year: 2012

Due to the complex chemical composition of nickel ores, the requests for the decrease of production costs, and the increase of nickel extraction in the existing depletion of high-grade sulfide ores around the world, computer modeling of nickel ore leaching process became a need and a challenge. In this paper, the design of experiments (DOE) theory was used to determine the optimal experimental design plan matrix based on the D optimality criterion. In the high-pressure sulfuric acid leaching (HPSAL) process for nickel laterite in "Rudjinci" ore in Serbia, the temperature, the sulfuric acid to ore ratio, the stirring speed, and the leaching time as the predictor variables, and the degree of nickel extraction as the response have been considered. To model the process, the multiple linear regression (MLR) and response surface method (RSM), together with the two-level and four-factor full factorial central composite design (CCD) plan, were used. The proposed regression models have not been proven adequate. Therefore, the artificial neural network (ANN) approach with the same experimental plan was used in order to reduce operational costs, give a better modeling accuracy, and provide a more successful process optimization. The model is based on the multi-layer neural networks with the back-propagation (BP) learning algorithm and the bipolar sigmoid activation function. © 2012 University of Science and Technology Beijing and Springer-Verlag Berlin Heidelberg.


Milivojevic M.,Technical and Business College | Stopic R.S.,RWTH Aachen | Stojanovic B.,University of Kragujevac | Dmdarevic D.,Technical and Business College | Friedrich B.,RWTH Aachen
Metall | Year: 2013

This paper presents the developed system for determining dimensions of forming and sizing tools for the powder metallurgy self-lubricated bearings. The system is based on the application of Stepwise Forward Linear Regression. Dimensions of tools for predefined constant regime of compacting and sintering are modeled. The study investigated the influence of a set of seven input factors to the four output variables. RMSE was used as a measure of accuracy for the modeled dimensions of tools. Using this system, a software that can be a strong support to engineers who design powder metallurgy (PM) technology, has been developed. The accuracy of modeled dimensions of tools has been compared to accuracy obtained by artificial neural networks (ANN). Given results show that Stepwise Forward Linear Regressions can be successfully used for designing tools for self-lubricated bearings.

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