Evaluation of Ash and Coal Response to Hybrid Polymeric Nanoparticles in Flotation Process: Data Analysis Using Self-Learning Neural Network
An artificial neural network was used to investigate the potential of using a novel polymer aid to produce clean coal concentrates in a fine clayey coal flotation process. Fine coal particles in the size range of +38-75 µm with 25.12 wt % of ash were floated in the presence of Al(OH)3-polyacrylamide nanoparticles. Five parameters; polymer dosage, pH, impeller speed, dispersant dosage and conditioning time were used as inputs in the simulation studies. Two network types (feedforward BP and cascade-forward BP) with three training algorithms (LM, BFG and GDX) and various numbers of neurons were designed and used to validate the experimentally observed qualitative and quantitative trends. The performance of each architecture design was evaluated by the correlation coefficient (R) and the mean square error (MSE). The two outputs that were used to evaluate the response of coal particles to the polymer used were the combustible recovery and the froth ash content. The cascade-forward network and Levenberg-Marquardt algorithm were selected as the optimal networks. The optimal ANN model showed a reasonable agreement in predicting the experimental data with correlation coefficient of 0.994 and 0.997 for training and testing datasets, respectively.
M. Khodakarami et al., "Evaluation of Ash and Coal Response to Hybrid Polymeric Nanoparticles in Flotation Process: Data Analysis Using Self-Learning Neural Network," International Journal of Coal Preparation and Utilization, vol. 39, no. 4, pp. 199-218, Routledge, May 2019.
The definitive version is available at https://doi.org/10.1080/19392699.2017.1308927
Mining and Nuclear Engineering
Keywords and Phrases
Aluminum compounds; Coal; Flotation; Mean square error; Nanoparticles; Neural networks; Polymers; Architecture designs; Ash removal; Clean coal; Combustible recovery; Correlation coefficient; Mean Square Error (MSE); Polymeric nanoparticles; Self-learning neural networks; Metal recovery; Al(OH)3-PAM; Artificial neural network
International Standard Serial Number (ISSN)
Article - Journal
© 2017 Routledge, All rights reserved.
01 May 2019