Predictive Control Strategy for Continuous Production Systems: A Comparative Study with Classical Control Approaches Using Simulation-Based Analysis
Abstract
:1. Introduction
- A new predictive control strategy, adapted for a continuous pharmaceutical tablet manufacturing plant using dry granulation, was developed.
- The control performance of the algorithm was tested and analyzed by simulations performed on a benchmark simulator designed based on the models that are available in the literature.
- A comparison between the results provided by the proposed predictive control strategy and those obtained using PID and LQR algorithms was performed.
2. Direct Compression vs. Dry/Wet Granulation
3. Process Description
3.1. Process Structure
3.2. Process Modelling
4. Closed-Loop Control System Framework
4.1. Control System Architecture
4.2. Model Predictive Control Strategy
5. Simulation Results
5.1. Simulation Setup
5.2. Comparison Control Strategies
5.2.1. Proportional-Integral-Derivative Controller
5.2.2. Linear Quadratic Regulator
5.3. Illustrative Results
5.4. Numerical Analysis
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
API | Active Pharmaceutical Ingredient |
CM | Continuous Manufacturing |
CPS | Cyber-Physical System |
DC | Direct Compression |
DG | Dry Granulation |
FRtool | Frequency Response tool |
GMP | Good Manufacturing Practice |
ISE | Integral-Square-Error |
IT | Information Technology |
ITAE | Integral of Time Absolute Error |
LB | Lower Bounds |
LQG | Linear Quadratic Gaussian |
LQI | Linear Quadratic Integral |
LQR | Linear Quadratic Regulator |
MIMO | Multiple-Input Multiple-Output |
MPC | Model Predictive Control |
NISE | Normalized Integral-Square-Error |
PID | Proportional-Integral-Derivative |
RC | Roller Compactor |
SISO | Single-Input Single-Output |
TP | Tablet Press |
UB | Upper Bounds |
WG | Wet Granulation |
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Property | Wet Granulation | Dry Granulation | Direct Compression |
---|---|---|---|
Compactability | Harder tablets in case of hard compactable substances. | Influenced by powder particle size and shape. | Potential problem for high loading of poorly compactable API substances. |
Flow | The granules formed are slightly more spherical than powders and have better flowability. | There may be some issues with powder flow. | Raw materials must have proper flowability and mixed with APIs, sometimes they may need lubricants before compression. |
Particle size | Greater with a longer range. | Narrower with a narrower range. | |
Content uniformity | It ensures better uniformity of content. | The resulting granulation increased confidence in uniformity. | It is at risk because it is difficult to accurately mix a small amount of API into a large volume of excipients. |
Mixing | Prevents segregation of components. | Segregation of components may occur after mixing. | A high shear can reduce particle size. |
Lubrication | Not so sensitive. | The compression step becomes easier and not sticky. | Reduces mixing time. |
Disintegration | Increased intragranular levels are required because of the negative impact of wet granulation on disintegrants. | They have an improved disintegration time because the dry binder used has a lower adhesive effect and therefore a quicker disintegration. | It allows them to disintegrate into API particles rather than granules. |
Dissolution | Providing hydrophilic properties to the surface of the granules can improve the dissolution rate. | The slowness of dissolution from granules during storage, particularly if an intragranular disintegrant is not used. | Difference in dissolution speeds up the process and allows better absorption for API tablets that are poorly soluble. |
Cost | Higher investments costs because of the time, labour, energy, and equipment. | Lower equipment costs than wet granulation. | DC has an economic advantage over granulation as it requires fewer resources. |
Sensitivity to raw material variability | Raw material wetting is influenced more by changes in raw material properties. | The properties of the raw material matter, the characteristics of API powders, and excipients are important. | Precise selection of excipients is needed as raw materials must have adequate flowability and compressibility for a successful operation. |
Stability | Not suitable for use on heat or moisture-sensitive materials. | Suitable for using on heat or moisture-sensitive materials. | |
Tableting speed | Higher | Decreased speed if the flow is low. |
Input | Value | LB | UB | Unit |
---|---|---|---|---|
Screw speed excipient feeder | 207.6 | 0 | 240 | rpm |
Screw speed API feeder | 37.4 | 0 | 240 | rpm |
Hydraulic pressure RC | 1 | 1 | 10 | MPa |
Feed speed | 2.017 | 1 | 5 | cm/s |
Angular velocity rolls RC | 5 | 1 | 10 | rpm |
Turret speed TP | 45 | 40 | 50 | rpm |
Height tablet | 0.004 | 0.0038 | 0.005 | m |
Feed volume | 9.6 × 10 | 9 × 10 | 11 × 10 | m |
Output | Value | LB | UB | Unit |
---|---|---|---|---|
Mass flow rate outlet blender | 20 | 17 | 23 | kg/h |
Concentration API | 0.15 | 0.10 | 0.20 | - |
Density outlet RC | 1.057 | 0.8 | 1.2 | g/cm |
Roller gap RC | 1.6 | 1 | 5 | mm |
Mass flow rate outlet RC | 20 | 17 | 23 | kg/h |
Mass flow rate outlet TP | 20 | 17 | 23 | kg/h |
Hardness tablet | 5.433 | 4 | 6 | MPa |
Mass tablet | 0.4566 | 0.44 | 0.47 | g |
Out1 () | Out2 () | Out7 () | Out8 () | |
---|---|---|---|---|
MPC | 3.519 | 6.263 | 2.655 | 2.361 |
LQR | 4.279 | 7.223 | 2.717 | 3.158 |
PID | 168 | 14,612 | 1021 | 175 |
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Chindrus, A.; Copot, D.; Caruntu, C.-F. Predictive Control Strategy for Continuous Production Systems: A Comparative Study with Classical Control Approaches Using Simulation-Based Analysis. Processes 2023, 11, 1258. https://doi.org/10.3390/pr11041258
Chindrus A, Copot D, Caruntu C-F. Predictive Control Strategy for Continuous Production Systems: A Comparative Study with Classical Control Approaches Using Simulation-Based Analysis. Processes. 2023; 11(4):1258. https://doi.org/10.3390/pr11041258
Chicago/Turabian StyleChindrus, Amelia, Dana Copot, and Constantin-Florin Caruntu. 2023. "Predictive Control Strategy for Continuous Production Systems: A Comparative Study with Classical Control Approaches Using Simulation-Based Analysis" Processes 11, no. 4: 1258. https://doi.org/10.3390/pr11041258