Operational issues in solids processing plants: Systems view

Wibowo, Christianto; Ng, Ka Ming

doi:10.1002/aic.690470112

Cited by 30 publications

(26 citation statements)

References 37 publications

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Section: Introductionmentioning

confidence: 99%

“…Turbulence is required to maintain the slurry suspended and well mixed. While operating under such conditions, phenomena such as breakage, agglomeration and segregation of particles can occur; these phenomena can be either desired or potential problems in the operation of these processes (Zwietering 1958;Wibowo & Ng 2001).One specific example is industrial crystallization, which deals with the production of solid crystals from a supersaturated liquid. The crystal suspension typically contains between 5 and 20 vol % solids with an average particle size in the range of 100 to 1000 µm, and with a solid to liquid density ratio in the range of 1 to 2.5.…”

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Fully resolved simulations of colliding monodisperse spheres in forced isotropic turbulence

et al. 2004

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Fully resolved simulations of particles suspended in a sustained turbulent flow field are presented. To solve the Navier-Stokes equations a lattice-Boltzmann scheme was used. A spectral forcing scheme is applied to maintain turbulent conditions at a Taylor microscale Reynolds number of 61. The simulations contained between 2 and 10 vol % particles with a solid to fluid density ratio between 1.15 and 1.73. A lubrication force is used to account for subgrid hydrodynamic interaction between approaching particles. Results are presented on the influence of the particle phase on the turbulence spectrum and on particle collisions. Energy spectra of the simulations show that the particles generate fluid motion at length scales of the order of the particle size. This results in a strong increase in the rate of energy dissipation at these length scales and a decrease of kinetic energy at larger length scales. Collisions due to uncorrelated particle motion are observed (primary collisions), and collision frequencies are in agreement with theory on inertial particle collisions. In addition to this, a large number of collisions at high frequencies is encountered. These secondary collisions are due to the correlated motion of particles resulting from shortrange hydrodynamic interactions and spatial correlation of the turbulent velocity field at short distances. This view is supported by the distribution of relative particle velocities, the particle velocity correlation functions and the particle radial distribution function. IntroductionIn industrial processes where solid materials are produced or handled, often dense slurries are processed under highly turbulent conditions. Turbulence is required to maintain the slurry suspended and well mixed. While operating under such conditions, phenomena such as breakage, agglomeration and segregation of particles can occur; these phenomena can be either desired or potential problems in the operation of these processes (Zwietering 1958;Wibowo & Ng 2001).One specific example is industrial crystallization, which deals with the production of solid crystals from a supersaturated liquid. The crystal suspension typically contains between 5 and 20 vol % solids with an average particle size in the range of 100 to 1000 µm, and with a solid to liquid density ratio in the range of 1 to 2.5. The Kolmogorov length scale typically is one order of magnitude smaller than the mean crystal size (Ten Cate et al. 2001). Under the action of the turbulent flow field, agglomeration (Hollander et al. 2001), abrasion, and fracture (Gahn & Mersmann 234 A. Ten Cate, J. J. Derksen, L. M. Portela and H. E. A. Van den Akker 1999) take place. These phenomena have a strong influence on the performance of the crystallization process and hence on the resulting product, and make scale-up a non-trivial exercise.In this paper we study turbulent solid-liquid suspensions under conditions that are comparable to those encountered in industrial crystallization processes (i.e. d p > η, where d p is the particle diameter and η th...

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Fully resolved simulations of colliding monodisperse spheres in forced isotropic turbulence

et al. 2004

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“…Wibowo and Ng, 2001 . Since crystals are often fed into a bulk solids processing plant to produce a solids product, it is desirable to consider the interactions among crystallization, downstream processing, and bulk-solids processing systems.…”

Section: Discussionmentioning

confidence: 95%

“…In fact, deposition is also a potential problem in many other units, Ž . such as mixers, screens, and crushers Wibowo and Ng, 2001 . Regardless of size, solid particles attach to the walls more easily in the presence of moisture.…”

Section: Systematic Procedures For Designing An Integrated Crystallizamentioning

confidence: 99%

Design of integrated crystallization systems

Wibowo

Chang

2001

AIChE Journal

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“…The applications of neural networks for chemical process modeling and MPC have also been investigated for SISO systems and iterative multistep neural network predictions in MPC based control for MIMO chemical processes [14][15][16][17][18][19]. Production of a uniform and reproducible CSD is a main difficulty in batch crystallization [20,21]. There are several approaches proposed in the literature; nevertheless, one way to improve the control of CSD is to use supersaturation control (SSC) [22][23][24], which drives the process within the metastable zone to avoid nucleation.…”

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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Operational issues in solids processing plants: Systems view

Abstract: A Jystematic approach to deal with the operational issues in solids processing

Cited by 30 publications

References 37 publications

Fully resolved simulations of colliding monodisperse spheres in forced isotropic turbulence

Fully resolved simulations of colliding monodisperse spheres in forced isotropic turbulence

Design of integrated crystallization systems

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

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