Study on hybrid modeling approach for gold cyanidation leaching process

Gold cyanidation leaching process is a complicated chemical process. Establishing an accurate process model is of important significance for control and optimization of leaching process. By analyzing the inherent characteristics of leaching process, two types of dynamic hybrid models are designed in this paper, which consist of mass conservation equations as the dynamic mechanistic model and BP neural networks that are used to compensate for the difference between the model output and the real output or to estimate the unknown parameters. For the serial hybrid model, because of the immeasurability of kinetic reaction rates, the Tikhonov regularization method is used to estimate the kinetics reaction rates of gold and cyanide ion, which reduces the propagation of errors in the measured data effectively. The simulation results show that the hybrid models have superior predictive performance than that of the pure mechanistic model. Among the three models, the serial hybrid model has the best predictive precision. The serial hybrid model can predict the concentration of both components accurately, which is an important model basis for subsequent control and optimization of the leaching process.