We introduce a global optimization framework for determining the minimum cost required to distill any ideal or near-ideal multicomponent mixture into its individual constituents using a sequence of columns. This new framework extends the Global Minimization Algorithm (GMA) previously introduced by Nallasivam et al. (2016); and we refer to the new framework as the Global Minimization Algorithm for Cost (GMAC). GMAC guarantees global optimality by formulating a nonlinear program (NLP) for each and every distillation configuration in the search space and solving it using global optimization solvers. The case study presented in this work not only demonstrates the need for developing such an algorithm, but also shows the flexibility and effectiveness of GMAC, which enables process engineers to design and retrofit energy efficient and cost-effective distillation configurations.
Jiang, Z., Mathew, T. J., Zhang, H., Huff, J., Nallasivam, U., Tawarmalani, M., & Agrawal, R. (2019). Global optimization of multicomponent distillation configurations: Global minimization of total cost for multicomponent mixture separations. Computers and Chemical Engineering, 126, 249–262. https://doi.org/10.1016/j.compchemeng.2019.04.009