Multiperiod Two-Stage Stochastic Programming of Supply Chain for Co-Processing System of Bio-Oil and Heavy Oil

Abstract

Bio-oil and heavy oil are co-processed in an existing fluid catalytic cracker of a refinery to reduce the biofuel cost and add biocarbon into cracked products. The supply chain design is one of the fundamental methods to further reduce the production cost of the co-processing system. In this work, a two-stage stochastic programming integrating the fluctuations of straw supply and price, algae growth rate, algae nutrients price, crude oil price, and bio-oil yield is built to design the supply chain of co-processing the system including biomass and crude oil. The sample average approximation method is adopted to solve the proposed two-stage stochastic programming. A fluid catalytic cracker with 1.2 Mt/y is used as an example to illustrate the model. The results show that optimal capacities of the algae cultivation process, fast pyrolysis and depot, integration of crude oil supply and biomass supply, and minimized total annual cost under multiperiod and uncertainties can be obtained. A comparison of the stochastic programming and the deterministic programming is also conducted and the result shows that the total annual cost decreases if uncertainties are considered. The uncertainties in the co-processing system should be considered when designing the supply chain to increase the model’s feasibility.