Modeling of an industrial wet grinding operation using data-driven techniques

The data-driven modeling techniques have been applied to the industrial grinding operation of a lead–zinc ore beneficiation plant to predict the output variables, the key performance indicators (KPIs) for the circuit. Many grinding plants are not adequately equipped with measuring instruments that are used only to measure some of the output parameters leading to a major hindrance towards modeling the operation through the route of first principles. To add to this, for controlling the grinding operation, system identification of the process is a must and poses a critical problem in advanced control as the grinding process behavior is highly non-linear. This necessitates applying some advanced data-driven techniques that are capable of predicting the KPIs within some acceptable limits. A total of six important KPIs considered here are throughput, three size fractions (+150 μm, +63 μm, −38 μm), percentage solids and recirculation load. These KPIs are predicted using three manipulated variables, namely, solid ore feed rate, two water feed rates to two sumps. To capture the nonparametric model for these KPIs, the data-driven techniques used here are several versions of neural networks and wavelets. While using neural network topologies, feed-forward neural networks (FNN) and recurrent neural networks (RNN) are utilized whereas for wavelet-based networks, wavelet frames are used. A well-validated hybrid model, a combination of physical and empirical methodologies, is used to approximate the actual behavior of the plant. A set of data, generated from this hybrid model is used for training the above-mentioned networks whereas another exclusive set of data is used to validate the evolved data-driven models. Merits and demerits of each of these techniques are also presented. Implementation of these techniques based on analysis to the actual plant may influence implementation of control and optimization technologies and thereby enhancing the plant performance tremendously where lack of hardware sensors does not allow them to be a right candidate to take part in systematic exercises for plant performance improvement.