Optimal retrofitting of conventional oil refinery into sustainable bio-refinery under uncertainty

This article focuses on a novel optimization problem to retrofit a conventional fossil-based refinery into a hybrid biomass-based refinery. A mixed-integer linear programming model, which considers a 10-year-long retrofit planning along with operational constraints in each year, is formulated. The problem is extended to a multistage stochastic programming model to handle both endogenous and exogenous uncertainties, and solved through a series of two-stage stochastic programming subproblems. Furthermore, a Lagrangean decomposition algorithm is implemented to solve such a problem. By determining whether to add new units or retrofit existing units to the selected biomass-based technologies, the results provide flexible design alternatives with consideration of operational constraints for each year. The results show the advantages of the selected biomass-based technologies and enhance the performance of the final solution under uncertainty.