Linear Model Predictive Control Strategies Applied to a Batch Sugar Crystallizer (original) (raw)

Nonlinear model predictive control strategies applied to a fed-bath sugar crystallizer

The present work is focused on a comparative study of two nonlinear MPC (NMPC) control schemes implemented to a fed-batch sugar crystallization process -i) NMPC that does not exploit the batch nature of the process (termed as classical NMPC) and ii) the batch NMPC that takes into account the end-point control objectives. They are also compared with the classical PI controller and a linear MPC scheme. Two main scenarios are considered: a nominal case without disturbances and a case with disturbances and variations in the initial conditions. The results demonstrate that the batch NMPC outperforms the other control structures but to the expense of high computational cost.

Neural Network Model Predictive Control Applied to a Fed-Batch Sugar Crystallization

2009

This paper is focused on a comprehensive study of neural network (NN) model based predictive control (MPC), as an operation strategy for a fed-batch sugar crystallizer. The process is divided into four subsequent control loops and for each of them an individual NN-based MPC is designed. The operation is tested for a number of scenarios and is compared with alternative (linear and batch nonlinear MPC) control solutions. The results demonstrate that the NN-MPC is a promising alternative of the traditionally applied linear controllers when the process is strongly nonlinear and input-output data is the only process information available.

SISO versus MIMO model based predictive control structures: a fed-batch crystallizer case study

Resumo -Este artigo é focado em estudo comparativo de quatro estruturas de controlo preditivo baseado em modelos lineares, desenhado para o processo de cristalização por lotes. Dois esquemas de controlo do tipo uma entrada -uma saída e dois esquemas do tipo varias entradas -varias saídas são analisados em relação a qualidade do produto final. Os modelos lineares são determinados através de duas técnicas alternativas de identificação baseadas em um teste ou dois testes de recolha de dados. Os resultados do estudo mostram que as estruturas uma entrada -uma saída levam a melhor qualidade do produto final. No entanto unicamente o controlo da sobresaturação (a saída) através de taxa de vapor (a entrada) consegue satisfazer todos os objectivos do processo.

Nonlinear MPC for fed-batch multiple stages sugar crystallization

Chemical Engineering Research and Design, 2011

This paper addresses the issue of developing feasible advanced control strategies for the operation of industrial fedbatch multi-stage sugar crystallization processes. The operation of such processes poses very challenging problems mainly those inherent to its batch nature and also those due to the difficulties in measuring key process variables. Inadequate control policies lead to out-of-spec batches, with consequent losses resulting from the need of product recycling. In order to address these problems, a modification of the general Nonlinear Model Predictive Control (NMPC) is proposed in this paper, where the NMPC is executed only when the tracking error is outside a pre-specified bound. Once the error converges towards the˛-strip, the NMPC is switched off and the control action is kept constant. In order to further reduce the complexity of the control system, the proposed modification, termed Error Tolerant MPC (ETMPC), is provided with a Recurrent Neural Network (RNN) predictive model. The ETMPC + RNN control scheme was extensively tested on a crystallizer dynamic simulator, tuned with data from two industrial units, and compared with the classical NMPC and PI strategy. The results demonstrate that both NMPC and ETMPC controllers lead to improved end point process specifications, when compared with the PI controller. The explicit introduction of the error tolerance in the optimization relaxes the computational burden and can complement several other suggestions in the literature for feasible industrial real time control.

Neural Network-Based Control Strategies Applied to a Fed-Batch Crystallization Process

2007

This paper is focused on issues of process modeling and two model based control strategies of a fed-batch sugar crystallization process applying the concept of artificial neural networks (ANNs). The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. The

Hybrid model predictive control of a sugar end section

2006

This paper deals with the MPC control of an industrial hybrid process where continuous and batch units operate jointly: the crystallization section of a sugar factory. The paper describes a plant-wide predictive controller that takes into account, both, the continuous objectives and manipulated variables, as well as the ones related to the scheduling of the batch units. The MPC is formulated avoiding the use of integer variables, so that a NLP optimization technique could be applied. Simulation results of the controller operation are provided * .

Modelling and control of crystallization process

Batch crystallizers are predominantly used in chemical industries like pharmaceuticals, food industries and specialty chemicals. The nonlinear nature of the batch process leads to difficulties when the objective is to obtain a uniform Crystal Size Distribution (CSD). In this study, a linear PI controller is designed using classical controller tuning methods for controlling the crystallizer outlet temperature by manipulating the inlet jacket temperature; however, the response is not satisfactory. A simple PID controller cannot guarantee a satisfactory response that is why an optimal controller is designed to keep the concentration and temperature in a range that suits our needs. Any typical process operation has constraints on states, inputs and outputs. So, a nonlinear process needs to be operated satisfying the constraints. Hence, a nonlinear controller like Generic Model Controller (GMC) which is similar in structure to the PI controller is implemented. It minimizes the derivative of the squared error, thus improving the output response of the process. Minimization of crystal size variation is considered as an objective function in this study. Model predictive control is also designed that uses advanced optimization algorithm to minimize the error while linearizing the process. Constraints are fed into the MPC toolbox in MATLAB and Prediction, Control horizons and Performance weights are tuned using Sridhar and Cooper Method. Performances of all the three controllers (PID, GMC and MPC) are compared and it is found that MPC is the most superior one in terms of settling time and percentage overshoot.

Nonlinear control of a batch crystallizer

Chemical Engineering Communications, 2002

A nonlinear geometric feedback controller with and without state estimation (the extended continuous-discrete Kalman filter) is developed and applied to a 0.027 m 3 potash alum batch crystallizer. The manipulated variable is the temperature of the inlet cooling water supplied to the jacket of the crystallizer, and the controlled variable is the supersaturation. It is shown that the controller eliminates the large initial peak in the supersaturation (which results in excessive nucleation) and maintains the supersaturation at its set-point, provided that the manipulated variable does not reach its constraints. The controller performs well with only two measured states (the crystallizer temperature and the solute concentration) and results in larger terminal crystal mean size in comparison with natural cooling and linear cooling policies with fines dissolution.