Improving estimation accuracy of metallurgical performance of industrial flotation process by using hybrid genetic algorithm – artificial neural network (GA-ANN)

• Autorzy: Allahkarami E. , Salmani Nuri O. , Abdollahzadeh A. , Rezai B. , Maghsoudi B.

Identyfikatory

DOI

10.5277/ppmp170129

• Języki publikacji: EN

Abstrakty

EN

In this study, a back propagation feed forward neural network, with two hidden layers (10:10:10:4), was applied to predict Cu grade and recovery in industrial flotation plant based on pH, chemical reagents dosage, size percentage of feed passing 75 μm, moisture content in feed, solid ratio, and grade of copper, molybdenum, and iron in feed. Modeling is performed basing on 92 data sets under different operating conditions. A back propagation training was carried out with initial weights randomly mode that may lead to trapping artificial neural network (ANN) into the local minima and converging slowly. So, the genetic algorithm (GA) is combined with ANN for improving the performance of the ANN by optimizing the initial weights of ANN. The results reveal that the GA-ANN model outperforms ANN model for predicting of the metallurgical performance. The hybrid GA-ANN based prediction method, as used in this paper, can be further employed as a reliable and accurate method, in the metallurgical performance prediction.

Słowa kluczowe

EN

artificial neural network; genetic algorithm; prediction; copper flotation

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2017
• Tom: Vol. 53, iss. 1
• Strony: 366--378
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Allahkarami E.

• Mining and Metallurgical Engineering Department, Amirkabir University of Technology, Tehran, Iran

autor

Salmani Nuri O.

• Mining and Metallurgical Engineering Department, Amirkabir University of Technology, Tehran, Iran

autor

Abdollahzadeh A.

• Mining and Metallurgical Engineering Department, Amirkabir University of Technology, Tehran, Iran
• Faculty of Engineering, University of Kashan, Kashan, Iran

autor

Rezai B.

• Mining and Metallurgical Engineering Department, Amirkabir University of Technology, Tehran, Iran

autor

Maghsoudi B.

• Sarcheshmeh Copper Concentration Plant, Kerman, Iran

Bibliografia

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