Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space Models

Sivanandam Venkatesh,Kannan Ramkumar,Rengarajan Amirtharajan

doi:10.3390/designs4030036

Abstract

Chemical process industries are running under severe constraints, and it is essential to maintain the end-product quality under disturbances. Maintaining the product quality in the cement grinding process in the presence of clinker heterogeneity is a challenging task. The model predictive controller (MPC) poses a viable solution to handle the variability. This paper addresses the design of predictive controller for the cement grinding process using the state-space model and the implementation of this industrially prevalent predictive controller in a real-time cement plant simulator. The real-time simulator provides a realistic environment for testing the controllers. Both the designed state-space predictive controller (SSMPC) in this work and the generalised predictive controller (GPC) are tested in an industrially recognized real-time simulator ECS/CEMulator available at FLSmidthPvt. Ltd., Chennai, by introducing a grindability factor from 33 to 27 (the lower the grindability factor, the harder the clinker) to the clinkers. Both the predictive controllers can maintain product quality for the hardest clinkers, whereas the existing controller maintains the product quality only up to the grindability factor of 30.

Highlights

The annual cement consumption in the world is around 1.7 billion tonnes and is increasing by 1%every year [1]
For modelling the ball mill grinding process, the suitable control strategy is to be established for this indigenous grinding unit to ensure product quality and productivity under optimised energy consumption, even in the presence of larger variations in the grindability factor of the clinker fed into the mill
The State Space Model Predictive Controller (SSMPC) designed in this work is simulated in the mill model and withproduct the andinproductivity even in theSince presence of larger variations the grindability factor of the

Summary

Introduction

The annual cement consumption in the world is around 1.7 billion tonnes and is increasing by 1%. Designed and implemented (LQ) regulator-based controllers for the cement grinding process that resulted in better performance, linear quadratic (LQ). Though predictive controllers were developed for the cement grinding process in [22,23,24] to operate the plant closer to constraints, the models considered for prediction were based on the first principles, which capture the noise dynamics badly. From the above discussions on the literature, it is clear that the models used for controlling the cement grinding circuit are based on the first principle model, which may not capture the real-time plant dynamics in the presence of external noise and disturbance. Motivated by this research gap, an attempt is made to design a predictive controller based on the state-space model of the cement grinding process. To compare the performance of GPC and SSMPC with the existing controller addressed in [26]

Cement

Predictive Controller Design

Performance of Predictive

Nominal

Conclusions