Name
Abstract | ||
|---|---|---|
This work proposes a particle swarm optimization (PSO) and artificial neural networks (ANN) integrated model to simulate the highly complexity and non-linear mine system, to optimize the cut-off grade and grade of crude ore. The inner layer of nesting is neural networks, which is used to compute loss rate, metal utilization rate and total cost; the outer layer is PSO algorithm, with cut-off grade and grade of crude ore as a particle, which is used to get the revenue. These two layers carry out the optimization of cut-off grade and grade of crude ore jointly. Take Daye Iron Mine as a case, the result shows that: During the period of January to November in the year 2007, the optimal cut-off grade is 17.83%, and optimal grade of crude ore is 46.4%. Comparing with the present scheme (cut-off grade is 18%, grade of crude ore is 41-42%), the optimized scheme can increase the amount of concentrate by 139200 tons, and improve the net present value by 6.698 million Yuan. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.684 | ICNC |
Keywords | Field | DocType |
neural network,pso algorithm,crude ore.,metal utilization rate,optimization,cut-off grade,crude ore,pso-ann integrated model,inner layer,crude ore grade,grade of crude ore,particle swarm optimisation,loss rate,neural networks,artificial neural networks,optimizing cut-off grade,optimal grade,mining,daye iron mine,optimal cut-off grade,artificial neural network,crude oil,neural nets,particle swarm optimization,computational modeling,iron,net present value,metals,geology | Particle swarm optimization,Mathematical optimization,Computer science,Crude oil,Cut-off,Statistics | Conference |
Volume | ISBN | Citations |
7 | 978-0-7695-3304-9 | 1 |
PageRank | References | Authors |
0.36 | 0 | 5 |