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An integrated dynamic modelling and simulation system for analysis of particulate processes
European Symposium on Computer Aided Process Engineering - 12 J. Grievink and J. van Schijndel (Editors) ® 2002 Elsevier Science B.V. All rights reserved.
427
An integrated dynamic modelling and simulation system for analysis of particulate processes Nicoleta Balliu, Ian Cameron and Robert Newell* CAPE Centre, Department of Chemical Engineering University of Queensland, Brisbane, Queensland, Australia 4072 *Daesim Technologies Pty. Ltd., GPO Box 819, Brisbane, Qld 4001
Abstract Particulate materials and their processing operations are an extremely important part of the worldwide manufacturing industry. In particular, granulation is a complex particle size enlargement process widely used in pharmaceutical, agricultural and fertilizer industries. The operation of continuous granulation plants can be very difficult. Problems with the design, control and operation of these systems can often be addressed through the use of computer aided modelling and simulation environments. This work describes the development of a particulate library of dynamic models for particle processing which incorporate full particle distribution models. The major contribution of this work is to provide comprehensive dynamic modelling and simulation tools which capture the complexities of these systems in order to investigate the dynamic behaviour of a wide range of particulate processes. In particular, the development of a family of granulator models is presented which allows the user to tailor the models to specific applications through the selections of key mechanisms.
1. Introduction Particle technology is a fertile area for research and is of great importance in a wide range of industries from pharmaceuticals to minerals, food and petrochemicals. Various phenomena involving particle processing are still unclear and many design procedures are based more on past experiences. Therefore, a good knowledge of the mechanisms in particle processing is useful in product development, waste minimization and quality control. Many practitioners are focused on better approaches to process analysis based on mathematical modelling and computer simulations. There are already well-established tools for steady state process design and investigations (e.g. Aspen Plus) (Aspentech, 2001). However, to understand complex dynamic systems, tools incorporating dynamic models that reflect the complex dynamics of the system are needed. Moreover, in the area of particle processing, there is a need for a comprehensive modelling and simulation environment, which allows process engineers to investigate the overall dynamic behaviour of a wide range of particulate systems from pharmaceuticals to fertilizers.
428 The objective of this work is to create and implement an integrated dynamic modelling and simulation system for analysis of major particle processing operations which include granulation, drying, particle reduction, particle separation and control structures. Models can be structured in a library and configured into a complex flow sheet using an object-based graphical configurator as done in Daesim Dynamics (Newell and Cameron, 2000). By choosing objects from component libraries we can simulate the time-dependent behaviour of systems whose underlying mechanisms can be defined by differential and algebraic equations (DAEs). The integrated dynamic tool presents a combined package for understanding various dynamic systems that lead to improved product quality, economics and finally a betterdesigned process for optimal behaviour. A case study for a granulation process based on the model library is presented in this paper. Due to the complexity of granulation processes we set out more fully the approach adopted in modelling these systems.
2. Description of the granulation circuit In this section we describe the behaviour of a simulated granulation circuit for which the particulate library can be used. A particulate stream or slurry is piped into the granulation drum and binder can be sprayed on the feed and recycle. Granule growth occurs along the drum. Granules leaves the granulator into the dryer. After drying solids are screened to separate the product size. Over-size granules are crushed and recycled together with under-sized granules. Figure 1 shows a simple granulation circuit. i<^'frniffir?[ni Fie
EdM
l-JDixl Trands
Sequences
SoM
Optimise
:: L^ "S^L" JL} V'/i /
Figure 1: Schematic Diagram of the Granulation Circuit
3. Description of particulate library structure The library structure is represented as flow sheet made up by using classes, which define a particular unit process, blocks, and connecting links. Models are composed of block and link objects. Block objects are model components whose behaviour is
429 described by DAEs with parameters. Link objects connecting blocks are simply algebraic variables. Blocks and links codes are written in a version of structured text language. LIBRARY CLASSES Member
Model class Particle size enlargement
Particle drying Particle size separation Particle size reduction Control & Instrumentation
> > > • • > > • • > > >
Granulator High-shear mixer Fluid bed Simple dryer Complex dryer Screens Cyclone Ball mill Hammer mill Valve Pl-ControUer Variable-Sensor
The most important link is the particulate stream which contains solid particles and fluid. The stream is defined in a matrix form, according to the number of particles size ranges - from 1 to M and the number of components present, 1 to NC. A temperature is also assigned to the particulate stream. The above constitute the key models for simulating most particle processing flow sheets and in particular a generalized granulation circuit.
4. Modelling Hierarchies Using the Particulate Library An important aspect of the particulate library is that it can be used to represent the model on various levels of granularity. As seen in Figure 3, at Level 1 we can represent the overall granulation circuit, with all the units involved. Simulations can be performed by choosing different units from the library and combine them together for particular applications. Sensitivity analyses of the influences on the granulation process of system variables can be easily evaluated and observed through simulations. At Level 2 we can decompose the system (Hangos and Cameron, 2001) to represent only the granulator, studying the dynamics of the whole unit. The granulator model can include mechanisms such as nucleation, layering, agglomeration and breakage or only nucleation and agglomeration or agglomeration and breakage, according to which assumptions were made. At Level 3 of decomposition , the model of granulation is defined in the library in such a way that we can perform simulations by switching between different mechanisms presented in the model. The same granulation model can be used in several interconnected granulator blocks - GRANULATOR 1, GRANULATOR 2, GRANULATOR 3 - and in each of them we can switch on/off different mechanisms. By employing this structure we are able to separate the various mechanisms/regimes present in the granulator. The concept of design and control of regime separated
430 granulation processes, based on granulation mechanisms has been applied by Wildeboer et al. (Wildeboer et al., 2001). The low level building blocks contain information regarding each mechanism or process present in the granulator. At this level we can define and characterize in detail nucleation, layering, agglomeration or breakage mechanisms as well as the chemical reactions in the granulator. Level 1
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5. Model hierarchy application and key simulation studies The above hierarchical system can be used to perform simulations for specific cases of interest. The influence of some parameters and mechanisms for Level 2 and Level 4 model representation are presented below. 5.1 Influence of increasing the amount of binder in the granulator inlet flow We perform simulations by taking onto account the agglomeration mechanism into granulator. In figure 3a) we represent the product rates for different size distributions until the system reaches steady state. After a certain time, when the steady state is attained we increase the fluid content in the granulator. As seen in Figure 3b) the process responds fast and a deviation of the trends as we increase the inlet fluid flow can be noticed. Moreover, the strong correlation between the granule moisture and agglomeration process is also evidenced by the decrease in the proportion of smaller particles - size 003, size 006 and size 008 and the increase of larger particles - size 010, size Oil and size 012. These sizes referred to mean particle diameter of 0.4mm up to 3.17mm. 1
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Figure 3: The influence of increasing the inlet flow moisture 5.1 Influence of granulator mechanisms In the agglomeration model, granule growth by agglomeration is modelled with a sequential two stage kernel, a size independent one when the rate of collisions is assumed to be independent of particle size and a size dependent one, depends on particle size (Adetayo, 1993). This determines the shape of the granule size distribution. The variation of product rates with time for different size fractions assuming a sizeindependent kernel is presented in Figure 4a while the variation of the product rates taking into account a size-dependent one is presented in Figure 4b. In the case of the size-dependent kernel, the granule size distribution widens as a result of the production of large granules without any significant change to the small granule end of the distribution. There is in the model a possibility of choosing between the sizeindependent kernel and the size-dependent kernel.
432
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Figure 4: The influence of kernel structure in the agglomeration mechanism These results can be used as the basis of key design changes in processing equipment to improve productivity. Control studies, structural optimization, optimal start-up, shutdown and grade changing can also be tested on such dynamic models.
6. Conclusions and recommendations The particulate library presented in this chapter gives the user the choice of many different combinations of operations and mechanisms for various particulate processing flowsheets. Thus, the particulate library can be used to improve industrial operations following model validation and parameter estimation from experimental data. A sensitivity analysis can also be performed using the library models to determine the extent of dominance of various system parameters in any given industrial operation. Simulation results can be used as the basis for key design changes in the processing equipment to improve productivity. The library continues to be expanded to ensure that it meets the needs of industries where particulate systems are an important aspect of the operations. The authors acknowledge part funding from Australian Research Council grant ARC 980026919.
7, References Aspentech, 2001, http://www.aspentech.com. Newell, R.B. and I.T. Cameron, 2000, Daesim Modelling and Simulation: User's guide, Daesim Technologies Pty. Ltd., http://www.daesim.com. Hangos K. and I.T. Cameron, 2001, Process Modelling and Model Analysis, Academic Press, ISBN 0-12-156931-4. Wildeboer, W.J., J. Litster and I.T. Cameron, 2001, Design of Regime Separated Continuous Granulators, University of Queensland P.P.S.D.C internal report. Adetayo A. A., 1993, Modelling and Simulation of a Granulation Circuit, PhD Thesis, University of Queensland, Australia.
427
An integrated dynamic modelling and simulation system for analysis of particulate processes Nicoleta Balliu, Ian Cameron and Robert Newell* CAPE Centre, Department of Chemical Engineering University of Queensland, Brisbane, Queensland, Australia 4072 *Daesim Technologies Pty. Ltd., GPO Box 819, Brisbane, Qld 4001
Abstract Particulate materials and their processing operations are an extremely important part of the worldwide manufacturing industry. In particular, granulation is a complex particle size enlargement process widely used in pharmaceutical, agricultural and fertilizer industries. The operation of continuous granulation plants can be very difficult. Problems with the design, control and operation of these systems can often be addressed through the use of computer aided modelling and simulation environments. This work describes the development of a particulate library of dynamic models for particle processing which incorporate full particle distribution models. The major contribution of this work is to provide comprehensive dynamic modelling and simulation tools which capture the complexities of these systems in order to investigate the dynamic behaviour of a wide range of particulate processes. In particular, the development of a family of granulator models is presented which allows the user to tailor the models to specific applications through the selections of key mechanisms.
1. Introduction Particle technology is a fertile area for research and is of great importance in a wide range of industries from pharmaceuticals to minerals, food and petrochemicals. Various phenomena involving particle processing are still unclear and many design procedures are based more on past experiences. Therefore, a good knowledge of the mechanisms in particle processing is useful in product development, waste minimization and quality control. Many practitioners are focused on better approaches to process analysis based on mathematical modelling and computer simulations. There are already well-established tools for steady state process design and investigations (e.g. Aspen Plus) (Aspentech, 2001). However, to understand complex dynamic systems, tools incorporating dynamic models that reflect the complex dynamics of the system are needed. Moreover, in the area of particle processing, there is a need for a comprehensive modelling and simulation environment, which allows process engineers to investigate the overall dynamic behaviour of a wide range of particulate systems from pharmaceuticals to fertilizers.
428 The objective of this work is to create and implement an integrated dynamic modelling and simulation system for analysis of major particle processing operations which include granulation, drying, particle reduction, particle separation and control structures. Models can be structured in a library and configured into a complex flow sheet using an object-based graphical configurator as done in Daesim Dynamics (Newell and Cameron, 2000). By choosing objects from component libraries we can simulate the time-dependent behaviour of systems whose underlying mechanisms can be defined by differential and algebraic equations (DAEs). The integrated dynamic tool presents a combined package for understanding various dynamic systems that lead to improved product quality, economics and finally a betterdesigned process for optimal behaviour. A case study for a granulation process based on the model library is presented in this paper. Due to the complexity of granulation processes we set out more fully the approach adopted in modelling these systems.
2. Description of the granulation circuit In this section we describe the behaviour of a simulated granulation circuit for which the particulate library can be used. A particulate stream or slurry is piped into the granulation drum and binder can be sprayed on the feed and recycle. Granule growth occurs along the drum. Granules leaves the granulator into the dryer. After drying solids are screened to separate the product size. Over-size granules are crushed and recycled together with under-sized granules. Figure 1 shows a simple granulation circuit. i<^'frniffir?[ni Fie
EdM
l-JDixl Trands
Sequences
SoM
Optimise
:: L^ "S^L" JL} V'/i /
Figure 1: Schematic Diagram of the Granulation Circuit
3. Description of particulate library structure The library structure is represented as flow sheet made up by using classes, which define a particular unit process, blocks, and connecting links. Models are composed of block and link objects. Block objects are model components whose behaviour is
429 described by DAEs with parameters. Link objects connecting blocks are simply algebraic variables. Blocks and links codes are written in a version of structured text language. LIBRARY CLASSES Member
Model class Particle size enlargement
Particle drying Particle size separation Particle size reduction Control & Instrumentation
> > > • • > > • • > > >
Granulator High-shear mixer Fluid bed Simple dryer Complex dryer Screens Cyclone Ball mill Hammer mill Valve Pl-ControUer Variable-Sensor
The most important link is the particulate stream which contains solid particles and fluid. The stream is defined in a matrix form, according to the number of particles size ranges - from 1 to M and the number of components present, 1 to NC. A temperature is also assigned to the particulate stream. The above constitute the key models for simulating most particle processing flow sheets and in particular a generalized granulation circuit.
4. Modelling Hierarchies Using the Particulate Library An important aspect of the particulate library is that it can be used to represent the model on various levels of granularity. As seen in Figure 3, at Level 1 we can represent the overall granulation circuit, with all the units involved. Simulations can be performed by choosing different units from the library and combine them together for particular applications. Sensitivity analyses of the influences on the granulation process of system variables can be easily evaluated and observed through simulations. At Level 2 we can decompose the system (Hangos and Cameron, 2001) to represent only the granulator, studying the dynamics of the whole unit. The granulator model can include mechanisms such as nucleation, layering, agglomeration and breakage or only nucleation and agglomeration or agglomeration and breakage, according to which assumptions were made. At Level 3 of decomposition , the model of granulation is defined in the library in such a way that we can perform simulations by switching between different mechanisms presented in the model. The same granulation model can be used in several interconnected granulator blocks - GRANULATOR 1, GRANULATOR 2, GRANULATOR 3 - and in each of them we can switch on/off different mechanisms. By employing this structure we are able to separate the various mechanisms/regimes present in the granulator. The concept of design and control of regime separated
430 granulation processes, based on granulation mechanisms has been applied by Wildeboer et al. (Wildeboer et al., 2001). The low level building blocks contain information regarding each mechanism or process present in the granulator. At this level we can define and characterize in detail nucleation, layering, agglomeration or breakage mechanisms as well as the chemical reactions in the granulator. Level 1
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i Reaction
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Figure 2: Hierarchical model representation
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£
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431
5. Model hierarchy application and key simulation studies The above hierarchical system can be used to perform simulations for specific cases of interest. The influence of some parameters and mechanisms for Level 2 and Level 4 model representation are presented below. 5.1 Influence of increasing the amount of binder in the granulator inlet flow We perform simulations by taking onto account the agglomeration mechanism into granulator. In figure 3a) we represent the product rates for different size distributions until the system reaches steady state. After a certain time, when the steady state is attained we increase the fluid content in the granulator. As seen in Figure 3b) the process responds fast and a deviation of the trends as we increase the inlet fluid flow can be noticed. Moreover, the strong correlation between the granule moisture and agglomeration process is also evidenced by the decrease in the proportion of smaller particles - size 003, size 006 and size 008 and the increase of larger particles - size 010, size Oil and size 012. These sizes referred to mean particle diameter of 0.4mm up to 3.17mm. 1
C/3
C/3
N
C/3
O
CJ =3 T3 O
O OH
OH
.
mem
11
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/ moisture
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1\
/
I
/ ^ -<:. — / ——_____ ^--""^ _ l _ j |
I , .''"'
Time(0 to 4000) [s]
Time(0 to 4000) [s] a)
/ s i z e 010 / s i z e 006 / s i z e 011 / s i z e 003 / s i z e 012
b)
Figure 3: The influence of increasing the inlet flow moisture 5.1 Influence of granulator mechanisms In the agglomeration model, granule growth by agglomeration is modelled with a sequential two stage kernel, a size independent one when the rate of collisions is assumed to be independent of particle size and a size dependent one, depends on particle size (Adetayo, 1993). This determines the shape of the granule size distribution. The variation of product rates with time for different size fractions assuming a sizeindependent kernel is presented in Figure 4a while the variation of the product rates taking into account a size-dependent one is presented in Figure 4b. In the case of the size-dependent kernel, the granule size distribution widens as a result of the production of large granules without any significant change to the small granule end of the distribution. There is in the model a possibility of choosing between the sizeindependent kernel and the size-dependent kernel.
432
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mm
size 007
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Ou
Time(0 to 2000) [s]
Time(0 to 2000) [s] a)
b)
Figure 4: The influence of kernel structure in the agglomeration mechanism These results can be used as the basis of key design changes in processing equipment to improve productivity. Control studies, structural optimization, optimal start-up, shutdown and grade changing can also be tested on such dynamic models.
6. Conclusions and recommendations The particulate library presented in this chapter gives the user the choice of many different combinations of operations and mechanisms for various particulate processing flowsheets. Thus, the particulate library can be used to improve industrial operations following model validation and parameter estimation from experimental data. A sensitivity analysis can also be performed using the library models to determine the extent of dominance of various system parameters in any given industrial operation. Simulation results can be used as the basis for key design changes in the processing equipment to improve productivity. The library continues to be expanded to ensure that it meets the needs of industries where particulate systems are an important aspect of the operations. The authors acknowledge part funding from Australian Research Council grant ARC 980026919.
7, References Aspentech, 2001, http://www.aspentech.com. Newell, R.B. and I.T. Cameron, 2000, Daesim Modelling and Simulation: User's guide, Daesim Technologies Pty. Ltd., http://www.daesim.com. Hangos K. and I.T. Cameron, 2001, Process Modelling and Model Analysis, Academic Press, ISBN 0-12-156931-4. Wildeboer, W.J., J. Litster and I.T. Cameron, 2001, Design of Regime Separated Continuous Granulators, University of Queensland P.P.S.D.C internal report. Adetayo A. A., 1993, Modelling and Simulation of a Granulation Circuit, PhD Thesis, University of Queensland, Australia.