Comparison of subspace and prediction error methods of system identification for cement grinding process

Maintaining product quality in cement grinding process in the presence of clinker heterogeneity is a challenging task. Model predictive controllers (MPC) are argued to be one possible solution to handle the variability, and the lack of models that relates clinker heterogeneity with product quality makes the MPC design challenging. This investigation addresses the suitability of two data‐driven modelling approaches for cement grinding process‐prediction error and subspace identification methods. Data collected from cement grinding process is used to build the model of the same. The collected data is used to build different candidate state‐space models using the prediction error and subspace identification methods. The candidate models were validated using Akaike's information criterion and mean square error to study the suitability of these modelling techniques. The validation tests are used to identify the most suitable candidate models for the prediction error and subspace methods. The models developed in this investigation are inputs to design predictive controllers for cement industries and assure product quality in the presence of clinker grindability variations.