Omar Galán

Robust H∞ control of nonlinear plants based on multi-linear models: an application to a bench-scale pH neutralization reactor

This work is aimed at developing a methodology to design controllers for nonlinear plants where desirable robustness and performance properties must be maintained across a large range of operating conditions. The approach is based on the multi-linear model representation and the H∞ control design that allows inclusion of plant nonlinearities by representing the original system as a set of local uncertain linear plants. To assess the merits of the proposed technique, experiments are performed on a bench-scale pH neutralization reactor. The results demonstrate robust performance and robust stability in the presence of disturbances and set-point variations.

Robust control of a SAG mill

Abstract It is now well accepted that process automation, closed-loop control and optimisation technologies enable higher feed rates, more consistent product qualities, lower utility usage and higher yields in a diverse range of process industries. In the minerals processing industry, as a general rule, it is advisable to consider the grinding circuit first, as good operation here is necessary for stable operation in the rest of the plant. Operating conditions in a grinding circuit are frequently such that production is significantly reduced due to the effects of process disturbances, most notably those associated with the feed ore. This paper demonstrates the design of a robust control scheme for a semi-autogenous grinding (SAG) mill. Feedback (FB), feedback plus feedforward (FF) and reduced order (i.e. less complex) robust controllers were all developed and tested—each providing tight control over the SAG mill power draw by adjusting the feed rate to the mill. This example clearly shows that using the available Matlab-based controller design package, it is possible to develop robust (i.e. in terms of strict guarantees on both controller performance and stability, despite model uncertainties) control systems for the minerals industry as readily as for other industries where advanced control is more routinely used.

On-line optimal trajectory control for a fermentation process using multi-linear models

Abstract The aim of this work is to present a technique, which is capable of dealing with discontinuous time varying systems based on the identification of local linear models at different operating conditions. The ideal process trajectory is predicted using a Model Predictive Control Scheme (MPC) in cascade with conventional PID controllers which are responsible for driving the system along at the optimal conditions. An on-line application to a laboratory scale fermenter for the production of gluconic acid is discussed to assess the reliability and performance of this strategy.

On the use of gap metric for model selection in multilinear model-based control

One way to design the control of a nonlinear system is to use a set of linear models that are close to the nonlinear system. This gives rise to a need to define the concept of closeness. Since systems can be visualized as input-output operators, a natural distance concept would be the induced operator norm. Yet, the norm cannot be generalized as a distance measure. The aim of this paper is to discuss the application of a distance measure between systems, the gap metric, in order to select a reduced set of models that contain nonredundant process information for robust stabilization of feedback systems based on multimodel controller design.

Selective Oxidation of Ethylene in an Industrial Packed-Bed Reactor: Modelling, Analysis and Optimization

This work is focused on the modelling, analysis and optimization of industrial ethylene oxide production in a packed bed reactor. The aim is to identify the critical variables that maximize the reactor productivity in an existing facility without compromising personnel safety and equipment integrity. The chemical reactions involved are highly exothermic making the internal temperature control of this unit a challenging task. Temperature excursions at dangerous levels have been experienced due to variations in composition and temperature of fresh feed to the reactor. Therefore, the prediction of dynamic temperature and composition profiles in the reactor are important for its safe operation. The model we developed incorporates catalyst deactivation and the effect of an inhibitory agent: 1,2-dichloroethene. The model predictions were found to be in good agreement with the plant data. Our model-based optimization studies show that the optimal set point for the inlet coolant temperature is suitable for preventing reactor hot spots and maximizing ethylene oxide selectivity. The heat integration aspects of the process were addressed.

A novel approach for the prediction of psd in antisolvent mediated crystallization

Abstract A phenomenological model for the description of antisolvent mediated crystal growth processes is here presented. The crystal size growth dynamics is supposed to be driven by a deterministic growth factor coupled to a stochastic component. The evolution in time of the particle size distribution is then described in terms of a Fokker-Planck equation. Validations against experimental data are presented for the NaCl-water-ethanol anti-solvent crystallization system.

On-line optimization control for a time-varying process using multiple models: A laboratory scale fermenter application

Abstract The aim of this work is to present a technique which is able to deal with discontinuous time varying systems based on the identification of local linear models at different operating conditions. The ideal process trajectory is predicted through an optimizing algorithm using a Model Predictive Control Scheme (MPC) in cascade with conventional PID controllers which are responsible for driving the system along at the optimal conditions. A laboratory scale reactor for the production of gluconic acid is studied in order to assess the reliability and performance of this control strategy.

On the Prediction and Shaping of the PSD in Crystallization Operations

Abstract The time behavior of the crystal size distribution in anti-solvent based crystal growth processes is investigated via statistical tools. The data are provided by the NaCl-water-ethanol anti-solvent crystallization system in a bench-scale fed-batch crystallization unit. It was found that the crystal population can be reasonably described in terms of a Gaussian or a Gamma distribution depending on the operating conditions.

A STOCHASTIC APPROACH TO MODEL ANTISOLVENT MEDIATED CRYSTAL GROWTH PROCESSES

A stochastic phenomenological model for the description of antisolvent crystal growth processes is here presented. The size of each crystal is supposed to be subjected to a geometric Brownian motion and its evolution in time is described in terms of a FokkerPlanck equation. A deterministic growth term is added to the model and is expressed as a simple logistic equation. A comparison with experimental data will be presented.

A Stochastic Approach for Anti-Solvent Addition Policy in Crystallization Operations: An Application to a Bench-Scale Fed-Batch Crystallizer

Abstract Abstract This work aims a stochastic approach for the calculation of robust anti-solvent addition policies for controlling the mean crystal size (MCS) in fed-batch crystallization operations. The proposed strategy is based-on a non-structured population balance where uncertainties associated with the start-up condition and random fluctuations along the fed-batch operation can be taken into account in a very natural fashion. We include and quantify the effect of the uncertainties by embedding a deterministic crystal growth model into a Fokker-Planck equation (FPE) resulting in a stochastic model for the MCS dynamics. This approach uses the Generalized Logistic equation (GLE) that has an adequate mathematical structure that suits the dynamic characteristic of the crystal growth. Thus, the numerical solution of the FPE provides the most likely MCS evolution for a given anti-solvent flow-rate. The effect of the anti-solvent is incorporated into the parameters of the FPE. The parameters of the FPE are computed as linear piece-wise interpolating functions of the anti-solvent flow-rate. The strategy uses a PID-like regulator in closed-loop fashion with the FPE to compute the anti-solvent addition flow-rates for different set-point targets in the MCS. In order to validate the stochastic model and assess the merits of the proposed strategy, the crystallization of sodium chloride in water using ethanol as anti-solvent is performed in a bench-scale fed-batch crystallizer. The implementation of the calculated anti-solvent policies resulted in a good control of the MCS despite modelling mismatch and uncertainties present during the crystallization operation.