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10/11/2005
1
AIChE Annual Meeting 6th November, 2013
San Francisco, CA, USA
ENGINEERING RESEARCH CENTER FOR
STRUCTURED ORGANIC PARTICULATE SYSTEMS
RUTGERS UNIVERSITYPURDUE UNIVERSITYNEW JERSEY INSTITUTE OF TECHNOLOGYUNIVERSITY OF PUERTO RICO AT MAYAGÜEZ
Implementation of Advanced Hybrid MPC-PID Control System Into a Continuous
Pharmaceutical Tablet Manufacturing Pilot-Plant
Ravendra Singh, Abhishek Sahay, Paul Brodbeck*, Marianthi Ierapetritou, Rohit Ramachandran
Department of Chemical & Biochemical EngineeringRutgers University, USA
*Control Associates, Allendale, NJ
Page 2
Outline
Introduction
Direct compaction tablet manufacturing
process and pilot plant
Designed hybrid MPC-PID control system
Control system implementation
Closed-loop operation
Conclusions
Page 3
Objective and Introduction
The objective is to implement an efficient control system into
continuous tablet manufacturing pilot plant
Challenges:
Powder material does not flow smoothly like fluid
Because of solid handling, the process is dead time dominant
Process variables are highly interactive
Process dynamic is poorly understood
In-line/on-line real time monitoring of process variables is difficult
The conventional process equipments are not compatible with the
control system
Page 4
Continuous tablet manufacturing process
Control variables API composition Powder level Tablet weight Tablet hardness
Page 5
Continuous direct compaction tablet manufacturing pilot-plant
Ref.: Singh, R., Boukouvala, F., Jayjock, E., Ramachandran, R. Ierapetritou, M., Muzzio, F. (2012). GMP news, European Compliance Academic (ECE), August, 2012, http://www.gmp-compliance.org.
(1) Feeders
(2) Blender
Tablet press
Page 6
Different alternatives of control strategies
Basic control strategy Easier to implement Computationally inexpensive
Advanced control strategy Computationally expensive Easier to tune Better to handle process
delay and process variable interactions
Better for multivariable system
Singh, R., Ierapetritou, M., Ramachandran, R. (2013). European Journal of Pharmaceutics and Biopharmaceutics, http://dx.doi.org/10.1016/j.ejpb.2013.02.019.
Singh, R., Ierapetritou, M., Ramachandran, R. (2012). International Journal of Pharmaceutics, 438 (1-2), 307-326.
Page 7
Model predictive control (MPC)
22 2
1 1 1 1 1 1
1 1y u un n nP M M
y set u uj j j j j j j j
i j i j i j
J w y k i y k i w u k i w u k i u
Tuning parameters1. Output weights (wy
j) 2. Rate weights ( ) 3. Input weight ( ) 4. Prediction horizon5. Control horizon
ujw
ujw
y: Controlled variableu: Actuator△u: Predicted adjustment
manipulated variable
deviations
Controlled variable deviations
controller adjustments
Singh, R., Ierapetritou, M., Ramachandran, R. (2013). European Journal of Pharmaceutics and Biopharmaceutics, http://dx.doi.org/10.1016/j.ejpb.2013.02.019.
Adapted from Bemporad, A., Morari, M. Robust Model Predictive Control: A Survey, AutomaticControl Laboratory, Swiss Federal Institute of Technology, <http://www8.cs.umu.se/research/ifor/dl/survey-robust-mpc.pdf> (10.09.12).
Page 8
Advanced hybrid MPC-PID control system
Page 9
Control hardware and software integrationStep 2
Step 3
Step 4
Step 1
Page 10
Step 1: Monitoring the process variable: spectrumBlender Chute JDSU Micro NIR
API composition
Page 11
NIR calibration model Chemometric tool: UnscramblerX (CAMO) PLS and PCA has been performed to develop the model
Time (s)
AP
I co
mp
ositi
on
(%)
Page 12
Steps 1-3. Overview
Prediction model
Input folderInput folder Output folderOutput folder
Write to OPC
DeltaV systemDeltaV system
Write to DeltaV
MATLAB OPC Tool
Read from DeltaV
JDSU micro NIR user interface
Unscrambler process pulse user interface
Page 13
Step 4. Overview
Page 14
Step 4: User interface (DeltaV control system)
Hybrid MPC-PID
Page 15
PID control scheme
NIR signalFiltered NIR signal(Control variable: API composition)
Actuator (Ratio)
Relative standard deviation (RSD)
PID
Time (min)
Va
riab
les
Page 16
PID controller performance
ITAE = 14830.03
Page 17
Hybrid MPC-PID: Linear model for MPC
% c
hang
e in
res
pons
e
Page 18
Closed-loop performance (simulation based): Hybrid MPC-PID
Actuator
Control variable (API composition)
Time (min)
Com
posi
tion
(fra
ctio
n)
Page 19
Closed-loop performance (experimental): master controller
NIR signalFiltered NIR signal(Control variable, API composition)
Actuator (Ratio)
Time (min)
Co
mp
osi
tion
(fr
act
ion)
MPC
Page 20
PID VS Hybrid MPC-PID: performance comparison
Page 21
PID VS Hybrid MPC-PID: performance comparison
Page 22
PID VS Hybrid MPC-PID: performance comparison
Criteria PID Hybrid MPC-PID
ITAE 14830.03 7757.77
RMSE 2.88% 2.12%
RSD 2.074% 0.984%
0
( ) ( )tf
i satt C t C t dt
X 100
X 100
Page 23
Summary
An advanced hybrid MPC-PID control scheme has been
designed and its performance has been evaluated using process
model
The control software and hardware has been integrated
The hybrid MPC-PID scheme has been implemented to the
direct compaction tablet manufacturing process
NIR has been integrated with the process to close the loop
The application of hybrid control system has been
demonstrated through blending process
Page 24
QUESTIONS?
Thank you!
Acknowledgements
This work is supported by the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems (ERC-SOPS), through Grant NSF-ECC 0540855.
The authors would also like to acknowledge Pieter Schmal (PSE) and ERC-SOPS colleagues for useful discussions.