NMPC of an industrial crystallization process using model-based observers

Damour, Cédric; Benne, Michel; Boillereaux, Lionel; Grondin-Pérez, Brigitte; Chabriat, Jean-Pierre

doi:10.1016/j.jiec.2010.07.014

Cited by 38 publications

(18 citation statements)

References 31 publications

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“…An energy balance applied to the magma in the crystallizer expresses the mass of emitted vapor: + m x x f x u (11) …”

Section: Process Modelingmentioning

confidence: 99%

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Integral feedback compensation control of sugar precipitation for Chinese medicine solution

Duan

2014

Proceedings of the 33rd Chinese Control Conference

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This paper illustrates the benefits of an integral feedback compensation control approach applied to a sugar precipitation process for Chinese medicine mixed solution. This relevant approach proposes setting point tracking for the crystal mass/concentration couple. In this purpose, a model dedicated to the stage crystallization is designed, without consideration of crystal size distribution. An integral feedback compensation strategy is constructed through identifying on-line and compensating real-time the quantities of external disturbance and parametric uncertainty, and only the upper bound of their derivative is required. The performance of the proposed control strategy, which applies to sucrose and fructose precipitation constitutes a real novelty, is tested via simulation in cases of set point tracking. The results reveal a significant validity in terms of precipitation efficiency.

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“…An energy balance applied to the magma in the crystallizer expresses the mass of emitted vapor: + m x x f x u (11) …”

Section: Process Modelingmentioning

confidence: 99%

“…However, strong nonlinearity of most chemical process shows the limitation of linear MPC, due to the limited validity of the linear model. Of late, relative algorithms are mainly iterative learning control [9,10] and nonlinear model-based predictive control [11,12].…”

Section: Introductionmentioning

confidence: 99%

Integral feedback compensation control of sugar precipitation for Chinese medicine solution

Duan

2014

Proceedings of the 33rd Chinese Control Conference

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“…However, since the batch crystallization process is a high complexity and nonlinear system, the control of this process based on the application of neural network is a significant challenge. For instance, Damour et al [20] presented the implementation of Neural Network Model Predictive Control (NNMPC) for an industrial crystallization process using model-based observers. A neural network model based on the estimates of crystal mass was used as internal model to predict the process output.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Modeling and Control for a Batch Heating/Cooling Evaporative Crystallization Process

Daosud

Thampasato

Kittisupakorn

2017

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Abstract. Crystallization processes have been widely used for separation in many fields to provide a high purity product. In this work, dynamic optimization and neural network (NN) have been applied to improve the quality of the product: citric acid. In the dynamic optimization, optimization problems maximizing both crystal yield and crystal size have been formulated. The neural networks have been developed to provide NN models to be used in the formulation of not only neural network inverse control (NNDIC) but also neural network model predictive control (NNMPC) strategies. The Levenberg Marquadt algorithm has been used to train the network and optimal neural network architectures have been determined by a mean squared error (MSE) minimization technique. In addition, a neural network model has been designed to provide estimates of the temperature and the concentration of the crystallizer. These estimates have been incorporated into the NNMPC controller. In the NNDIC controller, another neural network model has been applied to predict the set point of jacket temperature. The simulation results have shown that the obtained crystal size is increased by 19% and 30% compared to that by cooling and evaporation methods respectively and the obtained yield is increased more than 50%. The robustness of the proposed controller is investigated with respect to parameters mismatches. The results have shown that the NNMPC controller provides superior control performances in all case studies.

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“…However, it's nearly impossible to build a precise model for sugar crystallization process due to its complex physical mechanism, nonlinearity, great inertia and strong coupling links, where lots of uncertain factors are involved [3][4]. In the sugar crystallization process monitoring field, the vast majority of researchers focus on the advanced control techniques [3][4][5][6][7][8][9][10][11][12][13][14][15][16] like nonlinear control, fuzzy predictive control, neural network with PID and robust control. However, the detection sensor devices for process parameters like supersaturation, concentration, purity, crystal content and crystal size uniformity etc., are hard to be measured in the real world, which makes the application of all those advanced control techniques stay at theoretical stage but not an actual one [17].…”

Section: Introductionmentioning

confidence: 99%

Soft-sensor for Mother Liquor Purity in The Industrial Cane Sugar Crystallization Based on Extreme Learning Machine and Particle Swarm Optimization

Meng¹,

Pang²,

Chen³

et al. 2014

IJCA

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NMPC of an industrial crystallization process using model-based observers

Cited by 38 publications

References 31 publications

Integral feedback compensation control of sugar precipitation for Chinese medicine solution

Integral feedback compensation control of sugar precipitation for Chinese medicine solution

Neural Network Based Modeling and Control for a Batch Heating/Cooling Evaporative Crystallization Process

Soft-sensor for Mother Liquor Purity in The Industrial Cane Sugar Crystallization Based on Extreme Learning Machine and Particle Swarm Optimization

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