rs_aiche presentation 2013

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

Outline

Introduction

Direct compaction tablet manufacturing

process and pilot plant

Designed hybrid MPC-PID control system

Control system implementation

Closed-loop operation

Conclusions

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

Continuous tablet manufacturing process

Control variables API composition Powder level Tablet weight Tablet hardness

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

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.

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).

Advanced hybrid MPC-PID control system

Control hardware and software integrationStep 2

Step 3

Step 4

Step 1

Step 1: Monitoring the process variable: spectrumBlender Chute JDSU Micro NIR

API composition

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

(%)

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

Step 4. Overview

Step 4: User interface (DeltaV control system)

Hybrid MPC-PID

PID control scheme

NIR signalFiltered NIR signal(Control variable: API composition)

Actuator (Ratio)

Relative standard deviation (RSD)

PID

Time (min)

Va

riab

les

PID controller performance

ITAE = 14830.03

Hybrid MPC-PID: Linear model for MPC

% c

hang

e in

res

pons

e

Closed-loop performance (simulation based): Hybrid MPC-PID

Actuator

Control variable (API composition)

Time (min)

Com

posi

tion

(fra

ctio

n)

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

PID VS Hybrid MPC-PID: performance comparison

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

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

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.

rs_aiche presentation 2013

Documents

control associates

control hardware

efficient control system

control horizon u jw

actuator u

controlled variable

automatic control laboratory

model predictive control