Optimal control of particle size in antisolvent crystallization operations Seyed Mostafa Nowee 1 , Ali Abbas 2 , Jose A. Romagnoli 3 1 School of Chemical and Biomolecular Engineering, University of Sydney, Australia. 2 School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore. 3 Department of Chemical Engineering, Louisiana State University, USA. In this paper we present a detailed antisolvent crystallization model. A population balance approach is adopted to describe the dynamic change of particle size in crystallization processes under the effect of antisolvent addition. The sodium chloride-water-ethanol is used as a model system. Maximum likelihood method is used to identify the nucleation and growth kinetic models for sodium chloride from data derived from controlled experiments. A number of growth and nucleation kinetic models are investigated in the estimation step to identify one with better prediction. The model is then validated under a new ethanol (antisolvent) addition profile showing to be in good agreement. The resulting model is then exploited in model-based optimization to readily develop optimal antisolvent feeding recipes. Two objective functions are used related to the control of particle size. Different feeding profiles are readily determined for different end-product particle size targets. The dynamic optimization results are successfully validated experimentally showing very close agreement. The approach presented here is rapid and repeatable and thus attractive for pharmaceutical and fine chemicals antisolvent crystallization operations aimed at control of particle size. 1. Introduction Antisolvent crystallization (AC) operations are employed ubiquitously in the pharmaceutical and fine chemicals industries, producing solid particulate products. The particle size of the crystallization product is an important property that is typically required to conform to quality as well as operational demands. Particle size control is the concern of this paper in which we propose and present a model-based approach to optimal AC operation. In AC a secondary solvent known as antisolvent or precipitant is added to the solution resulting in the reduction of the solubility of the solute in the original solvent and consequently supersaturation is generated. The rate of supersaturation generation in AC is highly dependent on antisolvent addition rate. Keeping the supersaturation constant during a crystallization operation has been a long-standing technique for arguably optimal operation (Mullin and Nyvelt, 1971; Jones and Mullin, 1974). Zhou et al. (2006) have carried out concentration controlled seeded AC of a pharmaceutical compound using an algebraic equation for the solubility as a function of solvent. The main objective of their feedback ICheaP-8 The 8 th International Conference on Chemical & Process Engineering ISCHIA Island Gulf of Naples 24- 27 June 2007