(492d) Integrated Network Optimization and Uncertainty Analysis Streamlined LCA Method for the Petrochemical Industry

The development of more sustainable products and chemicals has boosted the sustainability evaluation of production systems, ranging from single plants to entire supply chains. However, the complexity and data intensity of chemical processes represent an important burden when trying to apply methodologies such as Life Cycle Assessment (LCA). Environmental databases are generally used to solve these issues, providing results obtained from LCA studies of existing processes. The amount of chemical products and processes present in these repositories, however, is still fairly limited. Additionally, results are obtained considering only fixed mass and energy flows, which contrasts with the dynamic behavior of production processes, particularly for the chemical industry. These differences might lead to severe disagreements between the real case scenarios and environmental repositories, being necessary alternative methodologies to assist in the assessment of environmental impacts.

To this end, we present a novel methodology for the quantification of LCA impacts in the production of chemicals based on the analysis of mathematically modeled networks of chemical processes using linear programming, uncertainty analysis and impact allocation procedures. In order to test this methodology, we assembled a network of the petrochemical industry containing 178 processes and 144 chemical products, being possible to quantify the impacts allocated to different products of the network. This was done for several environmental impact categories such as GWP and ReCiPe 2008. The estimates obtained were fairly similar to those present in popular environmental repositories such as Ecoinvent.

The effect of different network parameters such as final demand or production yield were studied using a sensitivity analysis approach, obtaining the uncertainty for the allocated impact values even for chemicals not previously included in environmental databases. These results demonstrate the capabilities of network approaches to characterize chemical and petrochemical production processes, allowing dynamic and uncertainty analyses of their economic and environmental performance. The presented methodology can be used to improve the transparency and flexibility of the LCA methodology, enhancing the sustainability evaluation of new and existing processes and chemicals.