Application Background: Petrochemical Supply Chains

• Goal /Aspiration for Project
• Evaluate potential supply-chain impacts of disruptions in the petrochemical sector  (steady-state)
• Develop a modeling, simulation, and analysis capability to assess sector vulnerabilities, its interdependencies with other critical infrastructures, its potential impacts from disruptive events (such as manmade and natural disasters), and its overall economic resilience.
• Approach/Methods/Models
• Petrochemical supply chain network - data driven
• The Loki toolkit embodies a generalized network and agent-based approach and contains a set of components that can be selected, specialized, and combined to create models of diverse systems including power systems, pipelines, social networks, and financial systems, as well as interactions across these different networks/systems. Loki has been applied to generic congestive cascade, power grids, payment systems, social simulation, and infectious diseases as well as petrochemical and natural gas systems.
• Several common measures of network topology were applied to identify the processes and products that are “important” from the standpoint of the structure of this network. 
• Definition of the edges connecting each process with its input and output materials creates a bipartite network of material-process-material-process chains.
• Basic resources such as database interaction, network construction, market exchanges, and definition of technologies as abstract chemical systems are available in the Loki toolkit.
• Status, Accomplishments and Next Steps
• Completed in 2008
• LOKI-Network algorithms and techniques were used to analyze the petrochemical subsector to predict the nonlinear impact of the loss of typical and atypical production capacities on overall systemic throughput.
• The high-level view in this idealization of chemical supply chains were also used to identify problematic areas.  For example, a network analysis reveals that propylene and styrene are connected to many other chemical products; such interconnectedness merits special attention from other higher fidelity modeling approaches.
• CASoS Goals: General Capabilities
• Data driven network representation, identification of down-chain impacts
• Capability to assess importance of a process by estimating the consequences of eliminating or curtailing that process for a network as a whole.
• CASoS Goals: Other Potential Applications
• Dynamic network impacts, market effects, transient supply chain impacts
• Acknowledgements
• This application has been funded by the Department of Homeland Security through the NISAC program and the Department of Homeland Security's Science and Technology program