The petrochemical industry must transition its material and energy sources from fossil-based sources to more sustainable alternatives. While decarbonizing the energy source is challenging, defossilization of the material feedstock is significantly more difficult. In this work, we present a superstructure-based, multi-period, multi-objective optimization framework to address this problem. This framework focuses on minimizing the use of fossil carbon and modifications to petrochemical clusters while explicitly controlling the order of appearance of new processes. The combination of process options becoming available to the solution space over time and the cluster being locked in a path-dependent transition allows the framework to capture realistic transformation pathways. We demonstrate the framework with a small-scale case study of 10 fossil-based and 6 alternative processes. The results demonstrate the ability of the framework to select optimal defossillization pathways while simultaneously considering the impacts on mass and energy flows across the cluster.

The petrochemical industry needs to reduce the use of fossil fuel as carbon feedstock to reduce its CO2 emissions. Several alternative carbon sources (ACSs), such as biomass, CO2 and plastic waste are being proposed to replace fossil carbon. As each of these ACS process routes has its tradeoffs, it is essential to identify the defossilization pathways that will have the most significant impact. In this work, a superstructure-based optimization approach is presented that can be used to assess defossilization pathways in existing petrochemical clusters. The small case study shows that CO₂ is a promising ACS to replace fossil fuel as the main carbon source but requires a large amount of green hydrogen and significant modifications to the existing cluster.