Surrogate-model based MILP for the optimal design of ethylene production from shale gas

Abstract

We propose a novel algorithm for the optimal design of entire plants by refining piecewise linear surrogate models within an iterative framework. We apply this strategy to a superstructure for ethane-based ethylene production, including steam cracking and alternative technologies, and the separation, utility, carbon dioxide and hydrogen recovery systems. Multivariable piecewise linear surrogate models (SM) based on rigorous Aspen Plus models and capital cost correlations are obtained by solving Generalized Disjunctive Programming problems. Using these surrogates, a Master MILP problem is formulated to determine the optimal design. If convergence criteria are not met, SM are progressively refined in subsequent iterations. The optimal solution is the chemical looping oxidative dehydrogenation technology, whose net present value (NPV) is 12% higher than that of conventional steam cracking, while reducing the ethylene production cost by 15%. Finally, we validate the optimal design with Aspen Plus, obtaining an NPV error of less than 1%.