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- W2088491539 abstract "The Fe/SiO2 ratio in slag is one of the important control parameters for copper flash smelting process, but it is difficult to describe the complex relationship between the technological parameters and the Fe/SiO 2 ratio in slag using accurate mathematic formulae, because the copper flash smelting process is a complicated nonlinear system. An neural network model for the Fe/SiO 2 ratio in copper flash smelting slag was developed, whose net structure is 8-15-12-1, and input nodes include the oxygen volume per ton concentrate, the oxygen grade, the flux rate, the quantity of Cu, S, Fe, SiO 2 and MgO in concentrate. In order to avoid local minimum terminations when the model is trained by back propagation (BP) algorithm, a new algorithm called GA-BP is presented by using genetic algorithm (GA) to determine the initial weights and threshold values. The results show that the model can avoid local minimum terminations and accelerate convergence, and has high prediction precision and good generalization performance. The model can be used to optimize the copper flash smelting process control." @default.
- W2088491539 created "2016-06-24" @default.
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- W2088491539 date "2012-05-01" @default.
- W2088491539 modified "2023-09-27" @default.
- W2088491539 title "An Neural Network Model for the Fe/SiO<sub>2</sub> Ratio in Copper Flash Smelting Slag Using Improved Back Propagation Algorithm" @default.
- W2088491539 cites W2047420573 @default.
- W2088491539 doi "https://doi.org/10.4028/www.scientific.net/amr.524-527.1963" @default.
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