Prediction of operational parameters effect on coal flotation using artificial neural network

Abstract Artificial neural network procedures were used to predict the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density, pH, rotation rate, coal particle size, dosage of collector, frother and conditioner were used as inputs to the network. Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1 arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively as the outputs. Quite satisfactory correlations of 1 and 0.91 in training and testing stages for combustible value and of 1 and 0.95 in training and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used to determine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation process.

[1]  Peter Jackson,et al.  Introduction to expert systems , 1986 .

[2]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[3]  Xavier Turon,et al.  Predicting flotation efficiency using neural networks , 2007 .

[4]  E. C. Çilek,et al.  Application of neural networks to predict locked cycle flotation test results , 2002 .

[5]  Naresh K. Sinha,et al.  Intelligent Control Systems: Theory and Applications , 1995 .

[6]  Martin T. Hagan,et al.  Neural network design , 1995 .

[7]  R. R. Jensen,et al.  Artificial neural network-based estimation of mercury speciation in combustion flue gases , 2004 .

[8]  Zhou Hao,et al.  Optimizing pulverized coal combustion performance based on ANN and GA , 2004 .

[9]  Karl-Erik Årzén,et al.  Expert control , 1986, at - Automatisierungstechnik.

[10]  Jay Liebowitz,et al.  An introduction to expert systems , 1988 .

[11]  A. Guez,et al.  A Comparison of Three Nonlinear Controller Designs Applied to a Non-Adiabatic First-Order Exothermic Reaction in a CSTR , 1992, 1992 American Control Conference.

[12]  Shobhana Dey,et al.  Split and collectorless flotation to medium coking coal fines for multi-product zero waste concept , 2007 .

[13]  V. N. Misra,et al.  Interpretation of interaction effects and optimization of reagent dosages for fine coal flotation , 2005 .

[14]  Janet Efstathiou.  Expert systems in process control , 1989 .

[15]  R. Sripriya,et al.  Optimisation of operating variables of fine coal flotation using a combination of modified flotation parameters and statistical techniques , 2003 .

[16]  Alan Williams,et al.  The predictions of coal/char combustion rate using an artificial neural network approach , 1999 .

[17]  Zi-Xing Cai,et al.  A real-time expert control system , 1996, Artif. Intell. Eng..

[18]  Frederick Hayes-Roth,et al.  Building expert systems , 1983, Advanced book program.

[19]  Jay Liebowitz,et al.  Structuring Expert Systems: Domain, Design, and Development , 1989 .

[20]  Jay Liebowitz,et al.  Expert systems: A short introduction , 1995 .

[21]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[22]  Kefa Cen,et al.  Predicting coal ash fusion temperature with a back-propagation neural network model , 1998 .

[23]  Robert J. Mockler,et al.  An introduction to expert systems , 1992 .

[24]  Z. Aktas,et al.  THE EFFECT OF REAGENTS AND REAGENT MIXTURES ON FROTH FLOTATION OF COAL FINES , 2003 .