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

Authors: Venkatesh Sivanandam; Ramkumar Kannan; Seshadhri Srinivasan; Guruprasath Muralidharan

Addresses: School of Electrical and Electronics Engineering, SASTRA University, Thanjavur, India ' School of Electrical and Electronics Engineering, SASTRA University, Thanjavur, India ' Kalasalingam University, Krishnankoil, India ' FLSmidth Pvt. Ltd., Chennai, India

Abstract: 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.

Keywords: cement grinding; system identification; state space modelling; prediction error method; PEM; subspace method; product quality; clinker heterogeneity; model predictive control; MPC; mean square error; MSE; controller design; cement industry.

DOI: 10.1504/IJSPM.2016.077319

International Journal of Simulation and Process Modelling, 2016 Vol.11 No.2, pp.97 - 107

Received: 03 Jul 2015
Accepted: 13 Oct 2015

Published online: 23 Jun 2016 *

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