Data-driven modeling and quality prediction of clinker production in a cement plant

The problem of deriving a model to predict the clinker quality in a cement production plant is considered. The process has a highly complex and nonlinear dynamic behavior, making physics-based first-principle modelling ineffective. A data-driven approach is thus proposed, to obtain an input-output model able to represent the overall system dynamics and to estimate the quality key performance indicators (KPIs). The approach combines a dynamic linear model, to estimate the evolution of the main process variables, with a static nonlinear regression model, to infer the clinker quality KPIs. The parameters of the dynamic model are estimated recursively on-line in a Moving Horizon Estimation fashion, to adapt to time-varying conditions such as the (unmeasured and uncertain) fuel mix. Real-world data collected on a European cement plant are used both to develop the approach and to test its effectiveness.