Greener Solvent Selection, Solvent Recycling and Optimal Control for Pharmaceutical and Bio-processing Industries

This paper proposes the simultaneous integration of environmentally benign solvent selection (chemical synthesis), solvent recycling (process synthesis) and optimal control for the separation of azeotropic systems using batch distillation. The previous work performed by Kim et al. (2004) combines the chemical synthesis and process synthesis under uncertainty. For batch distillation, optimal operation is also important due to the unsteady state nature of the process and high operating costs. Optimal control allows us to optimize the column operating policy by selecting a trajectory for the reflux ratio. However, there are time-dependent uncertainties in thermodynamic models of batch distillation due to the assumption of constant relative volatility. In this paper, the uncertainties in relative volatility are modeled using Ito processes and the stochastic optimal control problem is solved by combined maximum principle and non-linear programming (NLP) techniques. Then the previous work of optimal solvent selection and recycling is coupled with optimal control. As a real world example for this integrated approach, a waste stream containing acetonitrile-water is studied. The optimal design parameters obtained by Kim et al. (2004) for this separation are used and the optimal control policy is computed first without considering uncertainties by variable transformation technique. The deterministic optimal control policy improves the product yield by 4.0% as compared to the base case. A higher recovery rate is expected when the uncertainties are incorporated into in the model.