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Research Papers

Ecological Principles and Metrics for Improving Material Cycling Structures in Manufacturing Networks

[+] Author and Article Information
Astrid Layton

Mem. ASME
School of Mechanical Engineering,
Georgia Tech Lorraine,
57070 Metz, France
e-mail: alayton6@gatech.edu

Bert Bras

Mem. ASME
School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405
e-mail: bert.bras@me.gatech.edu

Marc Weissburg

School of Biology,
Georgia Institute of Technology,
Atlanta, GA 30332-0230
e-mail: marc.weissburg@biology.gatech.edu

1Corresponding author.

Manuscript received December 15, 2015; final manuscript received May 17, 2016; published online June 22, 2016. Editor: Y. Lawrence Yao.

J. Manuf. Sci. Eng 138(10), 101002 (Jun 22, 2016) (12 pages) Paper No: MANU-15-1664; doi: 10.1115/1.4033689 History: Received December 15, 2015; Revised May 17, 2016

A key element for achieving sustainable manufacturing systems is efficient and effective resource use. This potentially can be achieved by encouraging symbiotic thinking among multiple manufacturers and industrial actors and establish resource flow structures that are analogous to material flows in natural ecosystems. In this paper, ecological principles used by ecologists for understanding food web (FW) structures are discussed which can provide new insight for improving closed-loop manufacturing networks. Quantitative ecological metrics for measuring the performance of natural ecosystems are employed. Specifically, cyclicity, which is used by ecologists to measure the presence and strength of the internal cycling of materials and energy in a system, is discussed. To test applicability, groupings of symbiotic eco-industrial parks (EIP) were made in terms of the level of internal cycling in the network structure (high, medium, basic, and none) based on the metric cyclicity. None of the industrial systems analyzed matched the average values and amounts of cycling seen in biological ecosystems. Having detritus actors, i.e., active recyclers, is a key element for achieving more complex cycling behavior. Higher cyclicity values also correspond to higher amounts of indirect cycling and pathway proliferation rate, i.e., the rate that the number of paths increases as path length increases. In FWs, when significant cycling is present, indirect flows dominate direct flows. The application of these principles has the potential for novel insights in the context of closed-loop manufacturing systems and sustainable manufacturing.

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Figures

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Fig. 1

An example ecosystem (left), its FW (center), and its FW matrix representation (right). S1S3 represent the three species highlighted in the ecosystem and L11L33 represent the linkages between them.

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Fig. 2

Examples of the three types of internal structural cycling as represented by cyclicity (eigenvalues of [A]): (a) no cycling λmax = 0, (b) weak cycling λmax = 1, and (c) and strong cycling λmax > 1

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Fig. 3

A visualization illustrating the difference in complex cyclical interactions between a food chain and an FW in nature. Adapted from Ref. [40].

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Fig. 4

A comparison of the internal cycling of materials and energy within the Kalundborg and Pomacle–Bazancourt EIPs. Double-lined arrows represent linkages which participate in a cycle, grayed out linkages do not. Actors highlighted in bold are the acting detritus of the EIP.

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Fig. 5

The number of active detritus in an EIP plotted against network cyclicity

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Fig. 6

The Harjavalta industrial area in Finland. Double-lined linkages indicate connections which participate in a cycle, gray linkages do not. The single grayed box (city of Harjavalta) indicates an actor which exclusively participates in incoming or outgoing interactions (is only a predator or prey).

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Fig. 7

The Lower Mississippi Corridor EIP. Double-lined linkages indicate connections which participate in a cycle, while gray linkages do not. Grayed boxes indicate an actor which exclusively participates in incoming or outgoing interactions (is only a predator or predator or prey).

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Fig. 8

Path length versus number of paths of that length totaled for each of the 48 EIPs, for paths of length 1–100, plotted on a log–log scale. Inset (a) is the path length to number of paths relationship for FWs as presented by Ref. [52].

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Fig. 9

Path length versus number of paths of that length for the six highest EIPs ranked by cyclicity (class A; cyclicity > 3) for paths of length 1–100, plotted on a log–log scale

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Fig. 10

Path length versus number of paths of that length for the medium–high cyclicity EIPs (class B; 1 < cyclicity < 3) for paths of length 1–100, log–log scale

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Fig. 11

Path length versus number of paths of that length for the lowest cyclicity EIPs. Left panel shows class C EIPs (cyclicity = 1) and right panel shows the class D EIPs (cyclicity = 0) for paths of length 1–100, plotted on a log–log scale.

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