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 is composed of several interconnected processes that use fossil-based feedstock for producing chemicals. These processes are typically geographically clustered and often belong to different parties. Reducing the environmental impacts of the petrochemical industry is not straightforward due to, on the one hand, their reliance on fossil fuels for energy and as a feedstock and, on the other hand, the significant level of interconnected energy and material flows among processes. Current methods for analyzing changes to existing processes cannot capture the multitude and level of interactions. The goal of this paper is to create a model of a petrochemical cluster and analyze its physical characteristics and performance. This paper addresses this goal by developing an assessment method that combines process simulations, multiplex graph analysis, and key performance indicators. The method is applied to a case study based on the petrochemical cluster in the Port of Rotterdam, resulting in a uniquely highly detailed model of a petrochemical cluster. The network analysis results show that only some of the processes are very interconnected. From the performance analysis, it can be observed that the olefins process is the most carbon-intense and has high CO2 emissions. Additionally, the results showed the importance of considering existing interconnections when assessing the current performance of existing petrochemical clusters or the performance due to future changes to chemical processes. For instance, some changes would occur to an industrial cluster by introducing alternative carbon sources, such as biomass or CO2.

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.

The reliance of the petrochemical industry on fossil-based sources will need to be reduced by the introduction of Alternative carbon sources (ACS). Introducing ACS in a petrochemical cluster will require existing processes to be modified or replaced, potentially affecting other chemical processes within the cluster due to existing material and energy interconnections. Therefore, it is important to understand the current level of interconnections, functioning, and performance of the petrochemical cluster before introducing ACS. In this work, a representative cluster model based on the petrochemical cluster of the Port of Rotterdam was developed and considered as a case study. This model was analyzed using complex network analysis and environmental and technical key performance indicators. The selected key performance indicators (KPIs) provide insight into the performance of a petrochemical cluster, while the network properties give an understanding of the exchange of material and energy in an industrial cluster.