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Purpose <p>The transition to a circular economy requires advanced modeling frameworks capable of accurately tracking product lifecycles and material flows. Analytical approaches previously employed in this context often lack the granularity needed to account for individual product histories and dynamic decision-making. Computational methods have also been used, but they typically lack the determinism required to compute exact values.</p> Methods <p>This paper introduces the <i>Componentflow Process Network</i> (CFPN), a computational model based on Dataflow Process Networks (DPNs) for material flow analysis. CFPN models product flows using a token-based approach, enabling precise tracking of individual components throughout their lifecycles. Two case studies are presented to demonstrate the capabilities of the proposed method.</p> Results <p>CFPN successfully replicates key metrics from analytical methods, achieving exact parity in total lifetime and reentry estimates—results not attainable with current computational models. When stochastic elements such as log-normally distributed usage times are introduced, CFPN aligns closely with outputs from dynamic Material Flow Analysis (dMFA). Moreover, CFPN enables conditional branching based on the history of individual products, a feature not supported by traditional analytical approaches.</p> Conclusions <p>CFPN proves to be a versatile tool for lifecycle assessment and circular economy modeling. By enabling dynamic decision-making and component-level tracking, while also computing exact values on par with analytical methods, it represents a strong middle ground between analytical and computational approaches.</p>

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Componentflow Process Networks: A Product Reuse Model for the Circular Economy

  • 期刊论文

  • 发表日期：

    2026年3月11日

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  • Nicolas Beuve,
  • Maxime Pelcat,
  • Shuvra S. Bhattacharyya

摘要

Purpose

The transition to a circular economy requires advanced modeling frameworks capable of accurately tracking product lifecycles and material flows. Analytical approaches previously employed in this context often lack the granularity needed to account for individual product histories and dynamic decision-making. Computational methods have also been used, but they typically lack the determinism required to compute exact values.

Methods

This paper introduces the Componentflow Process Network (CFPN), a computational model based on Dataflow Process Networks (DPNs) for material flow analysis. CFPN models product flows using a token-based approach, enabling precise tracking of individual components throughout their lifecycles. Two case studies are presented to demonstrate the capabilities of the proposed method.

Results

CFPN successfully replicates key metrics from analytical methods, achieving exact parity in total lifetime and reentry estimates—results not attainable with current computational models. When stochastic elements such as log-normally distributed usage times are introduced, CFPN aligns closely with outputs from dynamic Material Flow Analysis (dMFA). Moreover, CFPN enables conditional branching based on the history of individual products, a feature not supported by traditional analytical approaches.

Conclusions

CFPN proves to be a versatile tool for lifecycle assessment and circular economy modeling. By enabling dynamic decision-making and component-level tracking, while also computing exact values on par with analytical methods, it represents a strong middle ground between analytical and computational approaches.