Integrated Environmentally Benign Solvent Selection and Solvent Recycling Under Uncertainty

Waste solvents from chemical process industries not only reduce material economy but also deteriorate environmental quality. Solvent recycling is a major endeavor in batch as well as continuous chemical process industries. Determining optimal separation sequences is a difficult process synthesis problem. This project presents a coupled solvent selection (chemical synthesis) and solvent recycling (process synthesis) approach to pollution prevention and greener processes. The simultaneous integration of chemical synthesis and process synthesis provides better economic throughput and superior environmental quality. However, this integration poses a challenging problem of multiple conflicting objectives, combinatorial explosion of alternatives, and uncertainties. This project also focuses on the development of a new and efficient multi-objective optimization (MOP) framework under uncertainty for this simultaneous integration. This framework is based on new and efficient algorithms for multi-objective optimization and for uncertainty analysis. In this work, an industrial case study is considered. In order to increase the degrees of freedom, process synthesis alternatives are generated using combination of P-graph and Residue curve maps. We have considered uncertainties in group contribution methods at the CAMD stage, and also uncertainties in objectives like environmental impacts.