The Experts below are selected from a list of 249 Experts worldwide ranked by ideXlab platform
Fardis Nakhaei - One of the best experts on this subject based on the ideXlab platform.
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Application and comparison of RNN, RBFNN and MNLR approaches on prediction of flotation column Performance
Elsevier, 2015Co-Authors: Fardis Nakhaei, Mehdi IrannajadAbstract:Evaluation of grade and recovery plays an important role in process control and plant profitability in mineral processing operations, especially flotation. The accurate measurement or estimation of these two parameters, based on the secondary variables, is a critical issue. Data-driven modeling techniques, which entail comprehensive data analysis and implementation of machine learning methods for system forecast, provide an attractive alternative. In this paper, two types of artificial neural networks (ANNs), namely radial basis function neural network (RBFNN) and layer recurrent neural network (RNN), and also a multivariate nonlinear regression (MNLR) model were employed to predict Metallurgical Performance of the flotation column. The training capacity and the accuracy of these three above mentioned types of models were compared. In order to acquire data for the simulation, a case study was conducted at Sarcheshmeh copper complex pilot plant. Based on the root mean squared error and correlation coefficient values, at training and testing stages, the RNN forecasted the Metallurgical Performance of the flotation column better than RBF and MNLR models. The RNN could predict Cu grade and recovery with correlation coefficients of 0.92 and 0.9, respectively in testing process. Keywords: Flotation column, Radial basis function, Recurrent neural network, Multivariate nonlinear regression, Metallurgical performanc
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comparison between neural networks and multiple regression methods in Metallurgical Performance modeling of flotation column
Physicochemical Problems of Mineral Processing, 2013Co-Authors: Fardis Nakhaei, Mehdi IrannajadAbstract:Artificial neural networks are relatively new computational tools which their inherent ability to learn and recognize highly non-linear and complex relationships makes them ideally suited in solving a wide range of complex real-world problems. In this research, different techniques (Linear regression, Non-linear regression, Back propagation neural network, Radial Basis Function for the estimation of Cu grade and recovery values in flotation column concentrate are studied. Modeling is performed based on 90 datasets at different operating conditions at Sarcheshmeh pilot plant, a copper concentrator in Iran, which include chemical reagents dosage, froth height, air and wash water flow rates, gas holdup and Cu grade in the rougher feed and flotation column feed, column tail and final concentrate streams. The results of models were also expressed and analyzed by intuitive graphics. The results indicated that a four-layer BP network gave the most accurate Metallurgical Performance prediction and all of the neural network models outperformed non-linear regression in the estimation process for the same set of data.
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recovery and grade prediction of pilot plant flotation column concentrate by a hybrid neural genetic algorithm
International journal of mining science and technology, 2013Co-Authors: Fardis Nakhaei, Mohammad Reza Mosavi, Abbas SamAbstract:Abstract Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process Performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for Metallurgical Performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer’s experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8-13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient ( R ) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.
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recovery and grade accurate prediction of pilot plant flotation column concentrate neural network and statistical techniques
International Journal of Mineral Processing, 2012Co-Authors: Fardis Nakhaei, Mohammad Reza Mosavi, Abbas Sam, Y VagheiAbstract:Abstract In this study, the Metallurgical Performance (grade and recovery) forecasting of pilot plant flotation column using Artificial Neural Networks (ANN) and Multivariate Non-Linear Regression (MNLR) models is investigated. Modeling is performed based on 90 datasets at different operating conditions. The values of chemical reagents dosage, froth height, air and wash water flow rates, gas holdup and Cu, Mo grades in the rougher feed and flotation column feed, column tail and final concentrate streams are used to the simulation by means of NN and MNLR. The model validation analysis demonstrates the capability of both models to predict Cu and Mo grades and recoveries for a wide range of operating conditions in pilot flotation columns. It must be noted that ANN approach offers superior predictive capability over statistical method. It was also found that the error in prediction of Metallurgical Performance using the NN model was less than the error of the regression model. The best network is proposed with multi-layer perceptron (MLP) model, sigmoid activation function and Levenberg–Marquardt learning rule with 8-12-8-2 and 8-9-12-2 architectures, in order to estimate Metallurgical Performance of Cu and Mo respectively in flotation column. The results of this study indicate that a back-propagation neural network model with Root Mean Square Errors (RMSE) of 0.68 and 0.02 for prediction of Cu and Mo grades and 0.48 and 1.16 for prediction of Cu and Mo recoveries respectively has a better Performance than the statistical method.
Abbas Sam - One of the best experts on this subject based on the ideXlab platform.
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recovery and grade prediction of pilot plant flotation column concentrate by a hybrid neural genetic algorithm
International journal of mining science and technology, 2013Co-Authors: Fardis Nakhaei, Mohammad Reza Mosavi, Abbas SamAbstract:Abstract Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process Performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for Metallurgical Performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer’s experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8-13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient ( R ) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.
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recovery and grade accurate prediction of pilot plant flotation column concentrate neural network and statistical techniques
International Journal of Mineral Processing, 2012Co-Authors: Fardis Nakhaei, Mohammad Reza Mosavi, Abbas Sam, Y VagheiAbstract:Abstract In this study, the Metallurgical Performance (grade and recovery) forecasting of pilot plant flotation column using Artificial Neural Networks (ANN) and Multivariate Non-Linear Regression (MNLR) models is investigated. Modeling is performed based on 90 datasets at different operating conditions. The values of chemical reagents dosage, froth height, air and wash water flow rates, gas holdup and Cu, Mo grades in the rougher feed and flotation column feed, column tail and final concentrate streams are used to the simulation by means of NN and MNLR. The model validation analysis demonstrates the capability of both models to predict Cu and Mo grades and recoveries for a wide range of operating conditions in pilot flotation columns. It must be noted that ANN approach offers superior predictive capability over statistical method. It was also found that the error in prediction of Metallurgical Performance using the NN model was less than the error of the regression model. The best network is proposed with multi-layer perceptron (MLP) model, sigmoid activation function and Levenberg–Marquardt learning rule with 8-12-8-2 and 8-9-12-2 architectures, in order to estimate Metallurgical Performance of Cu and Mo respectively in flotation column. The results of this study indicate that a back-propagation neural network model with Root Mean Square Errors (RMSE) of 0.68 and 0.02 for prediction of Cu and Mo grades and 0.48 and 1.16 for prediction of Cu and Mo recoveries respectively has a better Performance than the statistical method.
M Massinaei - One of the best experts on this subject based on the ideXlab platform.
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application of statistical and intelligent techniques for modeling of Metallurgical Performance of a batch flotation process
Chemical Engineering Communications, 2016Co-Authors: A Jahedsaravani, Mohammad Hamiruce Marhaban, M MassinaeiAbstract:Froth flotation is one of the most frequently used processes for separation of valuable from gangue minerals. Modeling and simulation of the flotation process is a difficult task because of nonlinear and dynamic nature of the process. In this contribution, the relationship between the process variables (i.e., gas flow rate, slurry solids%, frother/collector dosages, and pH) and the Metallurgical parameters (i.e., copper/mass/water recoveries and concentrate grade) in the batch flotation of a copper sulfide ore is discussed and modeled. Statistical (i.e., nonlinear regression) and intelligent (i.e., neural network and adaptive neuro-fuzzy) techniques are applied to model the process behavior at different conditions. The results indicate that intelligent approaches are more efficient tools for modeling of the complicated process like flotation, which are of central importance for development of the model-based control systems.
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estimation of particle size distribution on an industrial conveyor belt using image analysis and neural networks
Powder Technology, 2014Co-Authors: E Hamzeloo, M Massinaei, N MehrshadAbstract:Abstract Monitoring and controlling particle size distribution in crushing and grinding circuits are essential for improved energy efficiency and Metallurgical Performance. Machine vision is probably the most suitable approach for on-line particle size estimation because it is robust, cost-effective and non-intrusive. In the present study, size distribution of particles in crushing circuit of a copper concentrator was estimated using image processing and neural network techniques. Several images were taken from material on a conveyor belt and processed for particle identification and segmentation. A number of the most commonly used size features were extracted from the segmented images and their potential to estimate the actual particle size, represented by sieve size analysis, was evaluated. The results showed that there were substantial differences between size distributions obtained from various size measures. Maximum inscribed disk was found to be the most effective feature for particle size description. Finally, the particle size distribution of material on the conveyor belt was precisely estimated by Principal Component Analysis (PCA) and neural network techniques. The proposed soft sensors can be used for real time measurement of particle size distribution in the industrial operations instead of sophisticated and expensive instruments.
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optimisation of Metallurgical Performance of industrial flotation column using neural network and gravitational search algorithm
Canadian Metallurgical Quarterly, 2013Co-Authors: M Massinaei, H Falaghi, Hossein IzadiAbstract:AbstractExtensive advances in computational techniques allowed researchers to develop new search strategies to be used in function optimisation problems. In the present study, an optimisation algorithm, which is called gravitational search algorithm (GSA), and neural networks are integrated to optimise the Metallurgical Performance of an industrial flotation column at a copper concentrator. In this optimisation algorithm, a collection of masses interact with each other based on the Newtonian gravity and the laws of motion. Accordingly, an industrial flotation column is operated at different operating conditions (i.e. gas velocity, slurry solids %, frother dosage and froth depth) and its Metallurgical efficiency is modelled using neural networks. The developed neural network models are used as objective functions for optimisation by GSA. The obtained results indicate that industrial operations can be successfully modelled and optimiszed by applying intelligent techniques.L’avancement extensif des technique...
Mohammad Reza Mosavi - One of the best experts on this subject based on the ideXlab platform.
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Concentrate Grade Prediction in an Industrial Flotation Column Using Artificial Neural Network
Arabian Journal for Science and Engineering, 2013Co-Authors: F. Nakhaeie, Mohammad Reza MosaviAbstract:Today, column flotation has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the column flotation process is still not fully exploited. There is no prediction of process Performance for the complete use of available control capabilities. The online estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade. Therefore, advanced new methods such as Artificial Neural Network (ANN) must be employed. In this paper,a new approach has been proposed for Metallurgical Performance prediction in flotation columns using ANN. Furthermore, a case study is carried out in an industrial Metso Minerals CISA flotation column (4 m in diameter and 12 m in height) at the Sarcheshmeh Copper Concentrator Plant. The values of Cu and Mo grades in the flotation feed and final concentrate, froth height, wash water, and the air and non-floated fraction flow rates were used for the simulation by ANN. Feed-forward ANNs with 3-13-6-1 and 4-4-8-1 arrangements were used to estimating Cu and Mo grades, respectively. The correlation coefficient values for the training and testing sets for Cu and Mo grades were 0.94 and 0.93 and 0.98 and 0.97, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades with a reasonable error. Also, analysis demonstrates that prediction of grade for optimizing and controlling column flotation for a wide range of operating conditions is highly effective.
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recovery and grade prediction of pilot plant flotation column concentrate by a hybrid neural genetic algorithm
International journal of mining science and technology, 2013Co-Authors: Fardis Nakhaei, Mohammad Reza Mosavi, Abbas SamAbstract:Abstract Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process Performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for Metallurgical Performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer’s experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8-13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient ( R ) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.
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recovery and grade accurate prediction of pilot plant flotation column concentrate neural network and statistical techniques
International Journal of Mineral Processing, 2012Co-Authors: Fardis Nakhaei, Mohammad Reza Mosavi, Abbas Sam, Y VagheiAbstract:Abstract In this study, the Metallurgical Performance (grade and recovery) forecasting of pilot plant flotation column using Artificial Neural Networks (ANN) and Multivariate Non-Linear Regression (MNLR) models is investigated. Modeling is performed based on 90 datasets at different operating conditions. The values of chemical reagents dosage, froth height, air and wash water flow rates, gas holdup and Cu, Mo grades in the rougher feed and flotation column feed, column tail and final concentrate streams are used to the simulation by means of NN and MNLR. The model validation analysis demonstrates the capability of both models to predict Cu and Mo grades and recoveries for a wide range of operating conditions in pilot flotation columns. It must be noted that ANN approach offers superior predictive capability over statistical method. It was also found that the error in prediction of Metallurgical Performance using the NN model was less than the error of the regression model. The best network is proposed with multi-layer perceptron (MLP) model, sigmoid activation function and Levenberg–Marquardt learning rule with 8-12-8-2 and 8-9-12-2 architectures, in order to estimate Metallurgical Performance of Cu and Mo respectively in flotation column. The results of this study indicate that a back-propagation neural network model with Root Mean Square Errors (RMSE) of 0.68 and 0.02 for prediction of Cu and Mo grades and 0.48 and 1.16 for prediction of Cu and Mo recoveries respectively has a better Performance than the statistical method.
Saeed Farrokhpay - One of the best experts on this subject based on the ideXlab platform.
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the significance of froth stability in mineral flotation a review
Advances in Colloid and Interface Science, 2011Co-Authors: Saeed FarrokhpayAbstract:This paper presents a review of the published articles related to froth stability and its importance in mineral flotation. Froth structure and froth stability are known to play a significant role in determining the mineral grade and recovery achieved in a flotation operation. Froth stability is depending not only on the type and concentration of the frother but also on the nature and amount of the particles present in the system. To date, there is no specific criterion to quantify froth stability although a number of parameters are used as indicators of froth stability. Linking froth stability to the Metallurgical Performance is also challenged.