Multiobjective nonlinear model predictive control of pharmaceutical batch crystallizers

Abstract

A rigorous multiobjective nonlinear model predictive control procedure is implemented in solving problems involving batch crystallizations. This technique does not involve the use of weighting functions and additional restrictive constraints. Three cases are considered. The first is the unseeded batch crystallization involving paracetamol, the second is the seeded batch crystallization concerning potassium nitrate while the third problem deals with a temperature controlled batch crystallizer that involves citric acid anyhydrate. The optimization language pyomo with GAMS interface is used to solve the problems.

Keywords:

• Pharmaceutical
• optimization
• crystallization
• control
• drug

Acknowledgements

Dr. Sridhar is grateful to

1. Dr. Hemalatha Killari for supplying some required data and clarifying some doubts regarding seeded crystallization

2. Dr. David Bernal and Dr. Edna Sorayya for their help on issues pertaining to PYOMO.

Disclosure statement

No potential conflict of interest was reported by the author(s).