Feed Concentration Forecasting Using Closed-Loop Input Error and Deep Learning

The slurry concentration is a crucial factor in mineral processing production, affected by both upstream and downstream systems, including closed-loop control systems. The variability in slurry concentration presents a challenge due to its complex, nonlinear nature and the difficulty in accurately modeling this dynamic system for real-time monitoring. This article introduces an innovative approach for the real-time prediction of slurry concentration. Our method comprises two key components: a mechanistic model and a nonlinear dynamic system. The mechanism model is developed using the closed-loop input error method, effectively mitigating the influence of control systems. We have rigorously demonstrated the convergence of parameters and the stability of the identification process. Further, a hybrid deep neural network, called convolutional neural network-gated recurrent unit (GRU), is proposed to tackle challenges, such as intervariable dependencies and the inherent nonlinear dynamics of the process. The network integrates an autoregressive integrated moving average (ARIMA) model for laboratory delays. A parallel GRU network captures nonlinear dynamic characteristics after the ARIMA model. This architecture ensures real-time online concentration forecasting. The effectiveness of our approach has been substantiated with actual production data from a large-scale mineral processing facility.