An Integrated Framework for Greener Solvent Selection and Solvent Recycling under Uncertainty

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ResearchWaste solvents from the chemical process industries (CPI) are not only financially costly because funds are needed for separation, removal and disposal, but what is not removed deteriorates environmental quality. Solvent recycling is costly and determining optimal separation sequences is a complex process synthesis problem. This research project is a coupled solvent selection (which involves "product design" and chemical synthesis) and solvent recycling ("process design") approach to pollution prevention and greener processes. Integrating chemical synthesis with process synthesis to improve environmental and economic performance poses a challenging mathematical problem of multiple conflicting objectives, with a combinatorial explosion of alternatives and uncertainties. This project focuses on the development of an efficient multiobjective optimization (MOP) framework under uncertainty for this simultaneous integration around the commercially available ASPEN simulator. This framework is based on algorithms for multiobjective, discrete/continuous optimization, and for uncertainty analysis. The multiobjective optimization framework will provide a set of non-dominant designs where further improvements for one objective will be at the expense of another. This is valuable in a situation where there are a large number of desirable and important objectives, which are not easily translated into dollars: for example, human health and life, environmental impacts and plant flexibility. The framework will also be useful to solve problems where objectives are difficult to quantify, and when there is a lack of data or the data is inaccurate.Broad ImpactBesides the obvious importance to the environment, the project will also benefit underrepresented groups as the PI plans to target women for participation in the project and is collaborating with the University of Puerto Rico/Mayaguez. The PI is a faculty advisor for the Society of Women Engineers (SWE) and a mentor in the Women in Science and Engineering (WISE) program.