Learning behavioral models by recurrent neural networks with discrete latent representations with application to a flexible industrial conveyor

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

“…However, in the field of modeling systems for mineral processing (grinding, classifying and concentrating), artificial neural networks are relatively recent, but their use is increasing in this type of systems due to the efficiency of the results that they can generate, avoiding the implementation of complex calculations with better performance [16], [24]. Currently, the intelligence systems by means of artificial neural networks can be summarized into four structures: i) supervised learning: the neural network learns a set of inputs and the desired outputs to solve the problem [53]- [56], ii) direct inverse learning: the neural network learns from the feedback of a system, so that, when the signal is obtained, it determines the parameters to be performed [52], [57]- [60], iii) utility backpropagation: this structure optimizes the mathematical equation that represents the system, where its main disadvantage is that it requires a model of the system to be analyzed [61]- [65] and iv) adaptive critical learning: similar to the utility backpropagation structure, but without the need for a model of the plant [66]- [68]. Although this type of structures are present and well accepted in different industrial processes, it is evident that in mineral processing applications and especially in the prediction of variables of interest such as mineral recovery, the existing studies of this type of design are based on simulations, this research being a starting point for the implementation of intelligent systems in gravimetric concentration equipment where experimental data obtained from a pilot scale jig is worked on.…”