Prediction and Optimization of Matte Grade in ISA Furnace Based on GA-BP Neural Network

Abstract

The control of matte grade determines the production cost of the copper smelting process. In this paper, an optimal matte-grade control model is established to derive the optimal matte grade with the objective of minimizing the cost in the whole process of copper smelting. This paper also uses the prediction capability of the BP (Backpropagation) neural network to establish a BP neural network prediction model for the matte grade, considering various factors affecting matte grade (including the input copper concentrate amount and its composition content, air drumming amount, oxygen drumming amount, melting agent amount, and other process parameters). In addition, the paper also uses the optimal matte grade to optimize the dosing, air supply/oxygen supply, and oxygen supply for the ISA and other furnaces. When using BP networks only, it is a nonconvex problem with gradient descent, which tends to fall into local minima and has some bias in the prediction results. This problem can be solved by optimizing its weights and thresholds through GA (Genetic Algorithm) to find the optimal solution. The analysis results show that the average absolute error of the simulation of the BP neural network prediction model for ice copper grade after GA optimization is 0.51%, which is better than the average absolute error of 1.17% of the simulation of the single BP neural network model.

Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal

“…Gui et al [60] Pyrometallurgy • Deng et al [61] • D. Liu et al [28] • J. Liu et al [62] • Savic et al [63] • Ghea Puspita et al [64] • Cardoso et al [65] • Qian et al [66] • Cardoso et al [67] • Wang et al [68] • Yang et al [69] • Zhao et al [70] • RF: Random forest, EXS: expert system, FL: fuzzy logic, ANN: artificial neural network, CNN: convolutional neural network, MPC: model predictive control. • Olivier et al [39] • Estrada et al [29] •…”

“…Through this, high levels of mathematical correlation demonstrate the effectiveness of the model in predicting sulphur and phosphorus. Regarding the application of dynamic modelling and simulations for predicting matte grades, Zhao et al [70] found a BP neural network prediction model that, using a sample amount of 910 data points (900 for training and 10 for testing), is effective in providing guidance for controlling the copper flash smelting process with a maximum relative error of 3.3% and an average relative error of 0.54%. Yang et al [69] focus on the same process, developing and validate a dynamic model where the outcomes demonstrate that the hybrid intelligent model proposed in these publications is effective in predicting matte grade with high accuracy and reliability.…”