Resilience Dynamics in Coupled Natural-Industrial Systems: A Surrogate Modeling Approach for Assessing Climate Change Impacts on Industrial Ecosystems
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ID: 281673
2024
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Abstract
Industrial ecosystems are coupled with natural systems through utilization of
feedstocks and waste disposal. To ensure resilience in production of industrial
systems under the threat of climate change scenarios, it is necessary to
evaluate the impact of this coupling on productivity and waste generation. In
this work, we present a novel methodology for modeling and assessing the
resilience of coupled natural-industrial ecosystems under climate change
scenarios. We develop a computationally efficient framework that integrates
liquid time-constant (LTC) neural networks as surrogate models to capture
complex, nonlinear dynamics of coupled agricultural and industrial systems. The
approach is demonstrated through a case study of a soybean-based biodiesel
production network in Champaign County, Illinois. LTC models are trained to
capture dynamics of nodes and are then coupled and driven by statistically
downscaled climate projections for RCP 4.5 and 8.5 scenarios from 2006-2096.
The framework enables rapid simulation of system-wide material flow dynamics
and exploration of cascading effects from climate-induced disruptions. Results
reveal non-linear behaviors and potential tipping points in system resilience
under different climate scenarios and farm sizes. The RCP 8.5 scenario led to
earlier and more frequent production failures, increased reliance on imports
for smaller farms, and complex patterns of waste accumulation and stock levels.
The methodology provides valuable insights into system vulnerabilities and
adaptive capacities, offering decision support for enhancing the resilience and
sustainability of coupled natural-industrial ecosystems in the face of climate
change. The framework's adaptability suggests potential applications across
various industrial ecosystems and climate-sensitive sectors
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| Authors | William Farlessyost; Shweta Singh |
| Journal | arXiv |
| Year | 2024 |
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