Renato Lepore

Modeling and control of cement grinding processes

In this study, a nonlinear dynamic model of a cement grinding process, including a ball mill and an air separator in closed loop, is developed. This gray-box model consists of a set of algebraic and partial differential equations containing a set of unknown parameters. The selection of a model parametrization, the design of experiments, the estimation of unknown parameters from experimental data, and the model validation are discussed. Based on the resulting model, a dynamic simulator can be developed, which appears as a useful tool to analyze the process behavior and to understand the origin of instabilities observed in real-life operations. As a result, a cascaded control structure for regulating the mill flow rate, and a proportional integral controller for regulating the cement fineness are designed. Experimental data demonstrate the effectiveness of this control scheme. Alternatively, if on line measurements of the recirculated flow rate are available, a feedforward control of the feed flow rate is described, which ensures a better decoupling of mass flow rate and fineness regulation.

MODELING AND PREDICTIVE CONTROL OF CEMENT GRINDING CIRCUITS

Abstract The purpose of this paper is to show that a distributed-parameter model of a continuous ball mill can be developed by discretizing the particle size continuum into a few size intervals only. Despite this coarse discretization of the particle size distribution, the ball mill model provides a good representation of the real process, which can be combined with a classifier model to build a complete simulator of a closed-loop grinding circuit. This simplified process representation is compared with a detailed first-principle model previously developed and validated by the authors. The main advantage of the simplified model is that it can be easily incorporated in an on-line control scheme. For illustrative purposes, a NMPC scheme is implemented to regulate the product fineness when variations in the grindability of the raw material occur as a measurable disturbance. The control objective, based on a size interval content, is compatible with traditional fineness measurements.

Some observations on modeling and control of cement grinding circuits

Based on system analysis and experimental data, a dynamic model of a closed-loop cement grinding circuit, which consists of a mixed set of algebraic and partial differential equations, is developed and validated. The model equations are solved numerically using the method of lines and the resulting simulation program is used to gain some insight into the process dynamics and to design and compare control loops to achieve product specifications. The influence of the model nonlinearities, which are related to the dependency of the rates of breakage on the mill hold-up, is highlighted. In particular, this nonlinearity introduces a strong coupling between PI control loops using the fresh feed flow rate and the louver position of the classifier as manipulated variables. Several variations of this basic control scheme are thoroughly analyzed, and the necessity of an efficient mill flow rate control for the stability of the fineness control loop is demonstrated.

Nonlinear Model Predictive Control of Cement Grinding Circuits

Abstract Based on a reduced-order model of a cement grinding circuit, a non linear model predictive control strategy is developed. The first step of this NMPC study is the definition of control objectives which consider product fineness, product flow rate and/or grinding efficiency. At this stage, one of the main concerns is to relate these objectives to easily measurahle particle weight fractions. Second, NMPC is implemented so as to take the various constraints on the manipulated variables and operating conditions of the mill into account. Third, robustness with respect to model uncertainties is analyzed, and the most critical parameters are highlighted. Finally, an NMPC scheme, combining a stable inner loop for controlling the mill flow rate and a DMC-like compensation of the model mismatch, is proposed.

State and Parameter Estimation in Cement Grinding Circuits - Practical Aspects

Abstract Due to the lack of reliable and/or inexpensive hardware sensors in cement grinding, development of software sensors is particularly significant for control and monitoring purposes. In this study, a nonlinear distributed-parameter, full-horizon observer is designed, which allows the contents of the mill to be described in terms of hold-up and particle size distribution. When measurements are available at relatively high sampling rates and at, at least, two spatial locations along the mill, fast observer convergence is obtained. However, in practical situations where measurements can be collected at the mill outlet only and with a relatively low sampling rate, the observer convergence deteriorates and, as the sampling rate decreases, performance becomes similar to an asymptotic (simulation) observer. The robustness of the software sensor can be improved by on-line identification of some time-varying parameters, such as the material grindability. These several concepts are discussed and tested in simulation based on a realistic process model.

Receding-Horizon Estimation and Control of Ball Mill Circuits

This paper focuses on the design of a nonlinear model predictive control (NMPC) scheme for a cement grinding circuit, i.e., a ball mill in closed loop with an air classifier. The multivariable controller uses two mass fractions as controlled variables, and the input flow rate and the classifier selectivity as manipulated variables. As the particle size distribution inside the mill is not directly measurable, a receding-horizon observer is designed, using measurements at the mill exit only. The performance of the control scheme in the face of measurement errors and plant-model mismatches is investigated in simulation.

SCHEDULED OPTIMIZATION OF AN MMA POLYMERIZATION PROCESS

Abstract In this study, attention is focused on the design of a scheduled-optimization strategy for a batch MMA polymerization process. The objective of this strategy is to track an optimal temperature, despite uncertainties in the heat transfer and the gel ***effect. This strategy makes use of an (uncertain) physical model and on-line temperature measurements. The uncertain parameters are re-estimated on line, so as the optimal temperature trajectory. The good decoupling (in time) between the two major disturbances allows good performance to be achieved.

Software sensor design for cement grinding circuits – Practical issues

Abstract Due to the lack of reliable and/or inexpensive hardware sensors in cement grinding, development of software sensors is particularly significant for control and monitoring purposes. In this study, a nonlinear distributed-parameter, full-horizon observer is designed, which allows the contents of the mill to be described in terms of hold-up and particle size distribution. When measurements are available at relatively high sampling rates and at, at least, two spatial locations along the mill, fast observer convergence is obtained. However, in practical situations where measurements can be collected at the mill outlet only and with a relatively low sampling rate, the observer convergence deteriorates, as the sampling rate decreases, performance becomes similar to an asymptotic (simulation) observer. The robustness of the software sensor can be improved by on-line identification of some time-varying parameters, such as the material grindability. These several concepts are discussed and tested in simulation based on a realistic process model. Copyright