Modelling of oil agglomeration of dolomite by the use of an artificial neural network optimized using Optimal Brain Damage algorithm

Mineral Engineering Conference

Kamiski, M.^1 ; Bastrzyk, A.^2

Faculty of Electrical Engineering, Wroclaw University of Science and Technology, Janiszewskiego 8, Wroclaw

50-377, Poland^1

Faculty of Chemistry, Wroclaw University of Science and Technology, Norwida 4/6, Wroclaw

50-373, Poland^2

关键词: Levenberg-Marquardt method;    Mixing intensity;    Off-line training;    Oil agglomeration;    Optimal brain damages;    Process recovery;    Structure complexity;    Weights calculation;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/427/1/012012/pdf
DOI  :  10.1088/1757-899X/427/1/012012

来源: IOP

【 摘 要 】

This paper investigates application of Artificial Neural Network (ANN) for dolomite oil agglomeration modelling including parameters such as surfactants concentration, oil dosage, time, pH and mixing intensity. The main algorithm implemented for weights calculation was the Levenberg-Marquardt (LM) method. Common problem during process design of neural models is suitable selection of structure complexity. It is known that several connection can influence on final results after off-line training. For improvement of this stage of preparation of the net, pruning method was implemented. Analysed algorithm was based on the main theory of Optimal Brain Damage (OBD) technique. Results present high quality of process recovery prediction. Achieved outcome also shows that reduction of the applied of the neural network can lead to higher precision of calculation.