Ecologically Inspired Water Network Optimization of Steel Manufacture Using Constructed Wetlands as a Wastewater Treatment Process

被引:8
|
作者
Zhang, Kaili [1 ]
Malone, Stephen M. [2 ]
Bras, Bert [2 ]
Weissburg, Marc [3 ]
Zhao, Yuehong [1 ]
Cao, Hongbin [1 ]
机构
[1] Chinese Acad Sci, Inst Proc Engn, Beijing 100190, Peoples R China
[2] Georgia Inst Technol, George W Woodruff Sch Mech Engn, Atlanta, GA 30332 USA
[3] Georgia Inst Technol, Sch Biol Sci, Atlanta, GA 30332 USA
基金
美国国家科学基金会; 中国国家自然科学基金;
关键词
Phytoremediation; Wastewater; Steel manufacture; Optimization; ECO-INDUSTRIAL PARKS; PERFORMANCE; DESIGN; MODEL;
D O I
10.1016/j.eng.2018.07.007
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Traditional optimization models often lack a systems-level perspective at conception, which limits their effectiveness. Expanding system boundaries allow scientists and engineers to model complex interactions more accurately, leading to higher efficiency and profitability in industrial systems. Ecological systems have evolved for billions of years under conditions of material and energy shortage, and ecologists have defined analysis tools and metrics for identifying important principles. These principles may provide the framework to circumvent the limitations of traditional optimization techniques. More specifically, by recruiting functional roles that are often found in ecological systems, but are absent in industrial systems, industries can better mimic how natural systems organize themselves. The objective of this analysis is to traditionally optimize a manufacturing process by comparing the model with ecological and resource-based performance metrics in order to redesign the model with the addition of important functional roles that are found throughout nature. Industry partners provided data for this analysis, which involved building a water network for an existing steel manufacturing facility in China. The results of the traditional optimization model indicate a 23%, 29%, and 20% decline in freshwater consumption, wastewater discharge, and total annual cost, respectively. However, our ecologically inspired optimization model provides an additional 21% and 25% decline in freshwater consumption and total annual cost, respectively. Furthermore, no water is discharged. These results suggest that this unconventional approach to optimization could provide an effective technique not used by existing algorithms to solve the challenging problem of pursuing more sustainable industrial systems. (C) 2018 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:567 / 573
页数:7
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