A quantitative engineering study of ecosystem robustness using thermodynamic power cycles as case studies
Autoři:
Varuneswara Panyam aff001; Astrid Layton aff001
Působiště autorů:
J. Mike Walker ‘66 Department of Mechanical Engineering, Texas A&M University, College Station, Texas, United States of America
aff001
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0226993
Souhrn
Human networks and engineered systems are traditionally designed to maximize efficiency. Ecosystems on the other hand, achieve long-term robustness and sustainability by maintaining a unique balance between pathway efficiency and redundancy, measured in terms of the number of flow pathways available for a given unit of flow at any node in the network. Translating this flow-based ecosystem robustness into an engineering context supports the creation of new robust and sustainable design guidelines for engineered systems. Thermodynamic cycles provide good examples of human systems where simple and clearly defined modifications can be made to increase efficiency. Twenty-three variations on the Brayton and Rankine cycles are used to understand the relationship between design decisions that maximize a system’s efficient use of energy (measured by thermodynamic first law efficiency) and ecological measures of robustness and structural efficiency. The results reveal that thermodynamic efficiency and ecological pathway efficiency do not always correlate and that while on average modifications to increase energy efficiency reduce the robustness of the system, the engineering understanding of ecological network design presented here can enable decisions that are able to increase both energy efficiency and robustness.
Klíčová slova:
Ecosystems – Engineering and technology – Fluid flow – Shannon index – Sustainability science – Thermodynamics – Energy flow – Ecosystem engineering
Zdroje
1. Gallopin G. A systems approach to sustainability and sustainable development. Santiago; 2003.
2. Rosen MA. Engineering sustainability: A technical approach to sustainability. Sustainability. 2012;4(9):2270–2292. doi: 10.3390/su4092270
3. Fath BD. Quantifying economic and ecological sustainability. Ocean Coast Manag. 2015;108:13–19. doi: 10.1016/j.ocecoaman.2014.06.020
4. Kuhlman T, Farrington J. What is sustainability? Sustainability. 2010;2(11):3436–3448. doi: 10.3390/su2113436
5. Parkin S, Sommer F, Uren S. Sustainable development: understanding the concept and practical challenge. Eng Sustain. 2003;156(1):19–26.
6. León RV, Shoemaker AC, Kacker RN. Performance measures independent of adjustment: An explanation and extension of taguchi’s signal-to-noise ratios. Technometrics. 1987;29(3):253–265. doi: 10.1080/00401706.1987.10488231
7. Box G. Signal-to-Noise Ratios, Performance Criteria, and Transformations. Technometrics. 1988;30(1):1–17. doi: 10.2307/1270318
8. Maghsoodloo S. The Exact Relation of Taguchi’s Signal-to-Noise Ratio to His Quality Loss Function. Journal of Quality Technology. 1990;22(1):57–67. doi: 10.1080/00224065.1990.11979206
9. Chen W, Allen JK, Tsui KL, Mistree F. A Procedure for Robust Design: Minimizing Variations Caused by Noise Factors and Control Factors. J Mech Des. 1996;118(4):478–485. doi: 10.1115/1.2826915
10. Tsui KL. An Overview of Taguchi Method and Newly Developed Statistical Methods for Robust Design. IIE Transactions (Institute of Industrial Engineers). 2007;24(5):44–57.
11. Ulanowicz RE. The dual nature of ecosystem dynamics. Ecol Modell. 2009;220:1886–1892. doi: 10.1016/j.ecolmodel.2009.04.015
12. Ulanowicz RE, Goerner SJ, Lietaer B, Gomez R. Quantifying sustainability: Resilience, efficiency and the return of information theory. Ecol Complex. 2009;6(1):27–36. doi: 10.1016/j.ecocom.2008.10.005
13. Ulanowicz RE. Growth and Development: Ecological Phenomenology. 1st ed. New York: Springer-Verlag; 1986.
14. Layton A, Bras B, Weissburg M. Ecological Principles and Metrics for Improving Material Cycling Structures in Manufacturing Networks. J Manuf Sci Eng. 2016;138(10):101002. doi: 10.1115/1.4033689
15. Layton A, Bras B, Weissburg M. Improving performance of eco-industrial parks. Int J Sustain Eng. 2017;10(4-5):250–259. doi: 10.1080/19397038.2017.1317874
16. Hardy C, Graedel TE. Industrial ecosystems as food webs. J Ind Ecol. 2002;6(1):29–38. doi: 10.1162/108819802320971623
17. Zhang Y, Yang Z, Fath BD, Li S. Ecological network analysis of an urban energy metabolic system: Model development, and a case study of four Chinese cities. Ecol Modell. 2010;221(16):1865–1879. doi: 10.1016/j.ecolmodel.2010.05.006
18. Goerner SJ, Lietaer B, Ulanowicz RE. Quantifying economic sustainability: Implications for free-enterprise theory, policy and practice. Ecol Econ. 2009. doi: 10.1016/j.ecolecon.2009.07.018
19. Huang J, Ulanowicz RE. Ecological Network Analysis for Economic Systems: Growth and Development and Implications for Sustainable Development. PLoS One. 2014;9(6):100923. doi: 10.1371/journal.pone.0100923
20. Kharrazi A, Rovenskaya E, Fath BD, Yarime M, Kraines S. Quantifying the sustainability of economic resource networks: An ecological information-based approach. Ecol Econ. 2013;90:177–186. doi: 10.1016/j.ecolecon.2013.03.018
21. Kharrazi A, Rovenskayak E, Fath BD. Network structure impacts global commodity trade growth and resilience. PLoS ONE. 2017;12(2). doi: 10.1371/journal.pone.0171184 28207790
22. Bodini A, Bondavalli C, Allesina S. Cities as ecosystems: Growth, development and implications for sustainability. Ecol Modell. 2012;245:185–198. doi: 10.1016/j.ecolmodel.2012.02.022
23. Panyam V, Huang H, Pinte B, Davis K, Layton A. Bio-Inspired Design for Robust Power Networks. In: 2019 IEEE Texas Power Energy Conf. IEEE; 2019. p. 1–6.
24. Panyam V, Huang H, Davis K, Layton A. Bio-inspired design for robust power grid networks. Appl Energy. 2019;251:113349. doi: 10.1016/j.apenergy.2019.113349
25. Panyam V, Huang H, Davis K, Layton A. An ecosystem perspective for the design of sustainable power systems. Procedia CIRP. 2019;80:269–274. doi: 10.1016/j.procir.2018.12.005
26. Panyam V, Dave T, Layton A. Understanding ecological efficiency and robustness for network design using thermodynamic power cycles. In: Proc. ASME 2018 Int. Des. Eng. Tech. Conf. Comput. Inf. Eng. Conf.; 2018. p. 1–9.
27. Carvalho M, Serra LM. Adaptation of the ascendency theory to industrial systems. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2019;41(12). doi: 10.1007/s40430-019-2051-x
28. Hammond GP. Engineering sustainability: Thermodynamics, energy systems, and the environment. Int J Energy Res. 2004;28(7):613–639. doi: 10.1002/er.988
29. Schneider ED, Kay JJ. Life as a manifestation of the second law of thermodynamics. Math Comput Model. 1994;19(6-8):25–48. doi: 10.1016/0895-7177(94)90188-0
30. Layton A, Reap J, Bras B, Weissburg M. Correlation between Thermodynamic Efficiency and Ecological Cyclicity for Thermodynamic Power Cycles. PLoS One. 2012;7(12):1–7. doi: 10.1371/journal.pone.0051841
31. Sonntag RE, Borgnakke CC, Van Wylen GJGJ, Van Wylen GJGJ. Fundamentals of thermodynamics. Wiley; 1998.
32. Borrett SR, Salas AK. Evidence for resource homogenization in 50 trophic ecosystem networks. Ecol Modell. 2010;221:1710–1716. doi: 10.1016/j.ecolmodel.2010.04.004
33. Ulanowicz RE, Holt RD, Barfield M. Limits on ecosystem trophic complexity: insights from ecological network analysis. Ecol Lett. 2014;17:127–136. doi: 10.1111/ele.12216 24382355
34. Bodini A, Cristina B. Towards a sustainable use of water resources: a whole-ecosystem approach using network analysis. Int J Environ Pollut. 2002;18(5):463–485. doi: 10.1504/IJEP.2002.002340
35. Baird D, Ulanowicz RE. The Seasonal Dynamics of The Chesapeake Bay Ecosystem. Ecol Monogr. 1989;59(4):329–364. doi: 10.2307/1943071
36. Heymans JJ, Ulanowicz RE, Bondavalli C. Network analysis of the South Florida Everglades graminoid marshes and comparison with nearby cypress ecosystems; 2002.
37. Ulanowicz RE, Abarca-Arenas LG. An informational synthesis of ecosystem structure and function. Ecol Modell. 1997;95:1–10. doi: 10.1016/S0304-3800(96)00032-4
38. Latham LG, Scully EP. Quantifying constraint to assess development in ecological networks. Ecol Modell. 2002;154(1-2):25–44. doi: 10.1016/S0304-3800(02)00032-7
Článek vyšel v časopise
PLOS One
2019 Číslo 12
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