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

Abstract 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.