Hydrometallurgy technology can directly deal with low grade and complex materials, improve the comprehensive utilization rate of resources, and effectively adapt to the demand of low carbon and cleaner production. A series of cascade continuous stirred tank reactors are usually applied in the gold leaching industrial process. The equations of leaching process mechanism model are mainly composed of gold conservation, cyanide ion conservation, and kinetic reaction rate equations. The derivation of the theoretical model involves many unknown parameters and some ideal assumptions, which leads to difficulty and imprecision in establishing the accurate mechanism model of the leaching process. Imprecise mechanism models limit the application of model-based control algorithms in the leaching process. Due to the constraints and limitations of the input variables in the cascade leaching process, a novel model-free adaptive control algorithm based on compact form dynamic linearization with integration (ICFDL-MFAC) control factor is first constructed. The constraints between input variables is realized by setting the initial value of the input to the pseudo-gradient and the weight of the integral coefficient. The proposed pure data-driven ICFDL-MFAC algorithm has anti-integral saturation ability and can achieve faster control rate and higher control precision. This control strategy can effectively improve the utilization efficiency of sodium cyanide and reduce environmental pollution. The consistent stability of the proposed control algorithm is also analyzed and proved. Compared with the existing model-free control algorithms, the merit and practicability of the control algorithm are verified by the practical leaching industrial process test. The proposed model-free control strategy has advantages of strong adaptive ability, robustness, and practicability. The MFAC algorithm can also be easily applied to control the multi-input multi-output of other industrial processes.

In order to improve the leaching efficiency of gold ore and reduce the environmental treatment cost of residual sodium cyanide, continuous stirred tank reactors are often connected in a cascade manner. A gold leaching system is a multiphase chemical reaction system, and its kinetic reaction mechanism is complex and affected by random factors. Using intelligent modeling technology to establish a hybrid prediction model of the leaching system, the dynamic performance of the process can be easily analyzed. According to the reaction principle and the theory of substance conservation, a mechanism model is established to reflect the main dynamic performance of the leaching system. In order to improve the global convergence of the optimization target, a particle swarm optimization (PSO) algorithm based on simulated annealing is used to optimize the adjustment parameters in the kinetic reaction velocity model. The multilayer long short-term memory (LSTM) neural network approach is used to compensate for the prediction errors caused by the unmodeled dynamics, and a hybrid model is established. The hybrid prediction model can accurately predict the leaching rate, which provides a reliable basis for guiding production, and also provides a model basis for process optimization, controller design, and operation monitoring. Finally, the superiority and practicability of the hybrid model are verified by a practical leaching industrial system test. The prediction model of key variables in the leaching process is established for the first time using the latest time series prediction technology and intelligent optimization technology. The research results of this paper can provide a good reference and guidance for other research on complex system hybrid modeling.

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