skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • Modeling material flow dynamics in coupled natural‐industrial ecosystems for resilience to climate change: A case study on a soybean‐based industrial ecosystem
Title: Modeling material flow dynamics in coupled natural‐industrial ecosystems for resilience to climate change: A case study on a soybean‐based industrial ecosystem
Abstract Industrial ecosystems are coupled with natural systems, which causes the material flow dynamics in the network to be affected by the mechanistic dynamics of each node. However, current material flow dynamics studies do not capture these mechanistic and nonlinear dynamics to evaluate material flows in networks, thus missing its role in designing resilient industrial ecosystems. In this work, we present a methodology to overcome this limitation and model material flow dynamics in a coupled natural‐industrial network by accounting for underlying nonlinear dynamics at each node. We propose a three‐step methodology: first, creating accurate surrogate models using liquid time‐constant (LTC) neural networks to capture node‐specific behavior; second, coupling these individual node models to simulate material flow dynamics in the network; and third, evaluating resilience by measuring the system's ability to maintain production levels under climate stress. Applied to a soybean‐based biodiesel production network in Champaign County, Illinois (2006–2096), our analysis reveals significant vulnerability differences between climate scenarios, with the RCP 8.5 scenario triggering production failures approximately 10 years earlier than RCP 4.5 (2016 vs. 2026), exhibiting higher failure frequency and requiring longer recovery periods. Smaller farms (450 ha) demonstrated substantially higher import dependency, while medium farms (500 ha) reached a critical bifurcation point around 2050 under RCP 8.5, indicating a systemic tipping point. These findings provide insights for policymakers and industrial managers to implement targeted interventions, supply chain diversification, and adaptive management strategies, thereby enhancing system resilience while offering industrial ecology practitioners a methodology for modeling material flow dynamics in a coupled natural‐industrial network.  more » « less
Award ID(s):
2229250
PAR ID:
10648828
Author(s) / Creator(s):
 ;  
Publisher / Repository:
Springer Science + Business Media
Date Published:
Journal Name:
Journal of Industrial Ecology
Volume:
29
Issue:
5
ISSN:
1088-1980
Format(s):
Medium: X Size: p. 1882-1896
Size(s):
p. 1882-1896
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, ACM Transactions on Embedded Computing Systems)
    null (Ed.)
    The concept of Industry 4.0 introduces the unification of industrial Internet-of-Things (IoT), cyber physical systems, and data-driven business modeling to improve production efficiency of the factories. To ensure high production efficiency, Industry 4.0 requires industrial IoT to be adaptable, scalable, real-time, and reliable. Recent successful industrial wireless standards such as WirelessHART appeared as a feasible approach for such industrial IoT. For reliable and real-time communication in highly unreliable environments, they adopt a high degree of redundancy. While a high degree of redundancy is crucial to real-time control, it causes a huge waste of energy, bandwidth, and time under a centralized approach and are therefore less suitable for scalability and handling network dynamics. To address these challenges, we propose DistributedHART—a distributed real-time scheduling system for WirelessHART networks. The essence of our approach is to adopt local (node-level) scheduling through a time window allocation among the nodes that allows each node to schedule its transmissions using a real-time scheduling policy locally and online. DistributedHART obviates the need of creating and disseminating a central global schedule in our approach, thereby significantly reducing resource usage and enhancing the scalability. To our knowledge, it is the first distributed real-time multi-channel scheduler for WirelessHART. We have implemented DistributedHART and experimented on a 130-node testbed. Our testbed experiments as well as simulations show at least 85% less energy consumption in DistributedHART compared to existing centralized approach while ensuring similar schedulability. 
    more » « less
  2. ; ; (, Sustainable Energy & Fuels)
    Industrial networks consist of multiple industrial nodes interacting with each other through material exchanges that support the overall production goal of the network. 
    more » « less
  3. ; ; ; (, Scientific Reports)
    Abstract It is essential to study the robustness and centrality of interdependent networks for building reliable interdependent systems. Here, we consider a nonlinear load-capacity cascading failure model on interdependent networks, where the initial load distribution is not random, as usually assumed, but determined by the influence of each node in the interdependent network. The node influence is measured by an automated entropy-weighted multi-attribute algorithm that takes into account both different centrality measures of nodes and the interdependence of node pairs, then averaging for not only the node itself but also its nearest neighbors and next-nearest neighbors. The resilience of interdependent networks under such a more practical and accurate setting is thoroughly investigated for various network parameters, as well as how nodes from different layers are coupled and the corresponding coupling strength. The results thereby can help better monitoring interdependent systems. 
    more » « less
  4. ; ; ; ; ; ; (, Journal of Manufacturing Science and Engineering)
    Abstract Factory in a box (FiB) is an emerging technology that meets the dynamic and diverse market demand by carrying a factory module on vehicles to perform on-site production near customers’ locations. It is suitable for meeting time-sensitive demands, such as the outbreak of disasters or epidemics/pandemics. Compared to traditional manufacturing, FiB poses a new challenge of frequently reconfiguring supply chain networks since the final production location changes as the vehicle carrying the factory travels. Supply chain network reconfiguration involves decisions regarding whether suppliers or manufacturers can be retained in the supply chain or replaced. Such a supply chain reconfiguration problem is coupled with manufacturing process planning, which assigns tasks to each manufacturer that impacts material flow in the supply chain network. Considering the supply chain reconfigurability, this article develops a new mathematical model based on nonlinear integer programming to optimize supply chain reconfiguration and assembly planning jointly. An evolutionary algorithm (EA) is developed and customized to the joint optimization of process planning and supplier/manufacturer selection. The performance of EA is verified with a nonlinear solver for a relaxed version of the problem. A case study on producing a medical product demonstrates the methodology in guiding supply chain reconfiguration and process planning as the final production site relocates in response to local demands. The methodology can be potentially generalized to supply chain and service process planning for a mobile hospital offering on-site medical services. 
    more » « less
  5. ; (, Frontiers in Water)
    The rapid growth of demand in agricultural production has created water scarcity issues worldwide. Simultaneously, climate change scenarios have projected that more frequent and severe droughts are likely to occur. Adaptive water resources management has been suggested as one strategy to better coordinate surface water and groundwater resources (i.e., conjunctive water use) to address droughts. In this study, we enhanced an aggregated water resource management tool that represents integrated agriculture, water, energy, and social systems. We applied this tool to the Yakima River Basin (YRB) in Washington State, USA. We selected four indicators of system resilience and sustainability to evaluate four adaptation methods associated with adoption behaviors in alleviating drought impacts on agriculture under RCP4.5 and RCP 8.5 climate change scenarios. We analyzed the characteristics of four adaptation methods, including greenhouses, crop planting time, irrigation technology, and managed aquifer recharge as well as alternating supply and demand dynamics to overcome drought impact. The results show that climate conditions with severe and consecutive droughts require more financial and natural resources to achieve well-implemented adaptation strategies. For long-term impact analysis, managed aquifer recharge appeared to be a cost-effective and easy-to-adopt option, whereas water entitlements are likely to get exhausted during multiple consecutive drought events. Greenhouses and water-efficient technologies are more effective in improving irrigation reliability under RCP 8.5 when widely adopted. However, implementing all adaptation methods together is the only way to alleviate most of the drought impacts projected in the future. The water resources management tool helps stakeholders and researchers gain insights in the roles of modern inventions in agricultural water cycle dynamics in the context of interactive multi-sector systems. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email