Optimal control of a batch cooling seeded crystallizer

Hu, Qingwen; Rohani, Sohrab; Wang, D.X.; Jutan, Arthur

doi:10.1016/j.powtec.2005.04.010

Cited by 71 publications

(44 citation statements)

References 10 publications

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“…Mazzotti et al [166,167] used model-based optimization techniques to control CSD in batch cooling crystallisation of paracetamol in ethanol and also the combined cooling/antisolvent crystallisation of acetylsalicylic acid in ethanol-water mixtures. Rohani and co-workers (see for example [168][169][170][171][172][173][174]) minimized the optimization objective function with respect to a parameter vector temperature input, subject to the mass balance dynamics as well as the PB equation to obtain optimal cooling policy for product quality control. They also developed real-time model-based optimal control of anti-solvent semi-batch crystallisation processes and seeded batch crystallisation processes using real-time single and multi-objective optimization, rigid logic and fuzzy logic control methods [171][172][173][174][175].…”

Section: Optimisation and Control Of Crystal Size Distribution (Csd)mentioning

confidence: 99%

Measurement, modelling, and closed-loop control of crystal shape distribution: Literature review and future perspectives

2016

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Crystal morphology is known to be of great importance to the end-use properties of the crystal product and affect the down-stream processing such as in filtration and drying, but was previously regarded as too challenging to achieve automatic closed-loop control. As a consequence, previous work has focused on control the crystal size distribution (CSD) where the size of a crystal is often defined as the diameter of a sphere that has the same volume of the crystal. This paper reviews very promising new advances made in recent years in morphological population balance models for modelling and simulation of crystal shape distribution (CShD), measurement and estimation of crystal facet growth kinetics, as well as in 2D and 3D imaging for on-line characterisation of crystal morphology and CShD. A framework is presented integrating various components in order to achieve the ultimate objective of model-based closed-loop control of CShD. The knowledge gaps and challenges that require further research are also identified.

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Section: Optimisation and Control Of Crystal Size Distribution (Csd)mentioning

confidence: 99%

Measurement, modelling, and closed-loop control of crystal shape distribution: Literature review and future perspectives

2016

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“…Finally, m vap can be replaced in Equation (3). The kinetics parameter cryst is estimated via reverse technique thanks to seven databases supplied by the Bois-Rouge factory.…”

Section: Energy and Mass Balancementioning

confidence: 99%

“…In this aim, several advanced control algorithms (nonlinear model-based predictive control, generic model control, H  -control, etc.) were developed for both continuous and batch crystallization processes in numerous fields of application [1][2][3][4][5][6][7]. Even if modelbased strategies have proved to be efficient, in crystallization, the model design remains a crucial and difficult issue.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency Improvement of an Industrial Crystallization Process Using Linearizing Control

Damour¹,

Benne²,

Boillereaux³

et al. 2012

JCPT

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This paper illustrates the benefits of a multivariable linearizing control approach applied to an industrial crystallization process. This relevant approach is declined according to two different strategies: first, a setpoint tracking is proposed for the couple crystal mass/concentration, whereas a second way consists in tracking of crystal content and concentration. The controlled variables, unavailable on-line, are issued from an observer developed in previous works. The performance of these strategies, which application to cane sugar crystallization constitutes a real novelty, are compared with experimental data issued from a PID-controlled industrial plant. The results reveal a significant improvement of energy efficiency, leading to an economy of more than 10% of energy.

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“…Generally, the CSD is directly affected by the non-equilibrium state of the system measured via supersaturation. Since supersaturation is a function of a crystallizer temperature, the slurry temperature is often used as the manipulated variable to control the non-equilibrium driving force of the system [3,4]. However, to achieve the objective of the control, an optimal condition and effective controllers are required.…”

Section: Introductionmentioning

confidence: 99%

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

Kittisupakorn¹,

Somsong²,

Hussain³

et al. 2017

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Abstract. A main difficult task in batch crystallization is to control the size distribution of crystal products. Complexity and highly nonlinear dynamic behavior directly affect to model-based control strategies which heavily depend on the rigorous knowledge of crystallization. In this work, neural network-based model predictive control and inverse neural network control strategies are proposed and integrated with an optimization based on neural network-based hybrid model to control temperatures of a purified terephthalic acid batch crystallizer. A neural network-based hybrid model of the batch crystallizer is developed to provide nonlinear dynamic responses used in optimization algorithm for finding an optimal temperature profile related to the quality of a crystal product. Then, the obtained optimal profile is used as set points of the proposed control strategies for improving the crystal product quality. The performances and robustness of the proposed controllers are evaluated in several cases such as for set point tracking and plant/model mismatches. Simulation results show that the neural network-based model predictive control gives the best control performance among the inverse neural network control and a conventional PID controller in all cases.

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Optimal control of a batch cooling seeded crystallizer

Cited by 71 publications

References 10 publications

Measurement, modelling, and closed-loop control of crystal shape distribution: Literature review and future perspectives

Measurement, modelling, and closed-loop control of crystal shape distribution: Literature review and future perspectives

Energy Efficiency Improvement of an Industrial Crystallization Process Using Linearizing Control

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

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