Klemes Assessing and Measuring Environmental Impact and Sustainability

1. Auflage 2015
ISBN: 978-0-12-802233-7
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 608 Seiten

Assessing and Measuring Environmental Impact and Sustainability answers the question 'what are the available methodologies to assess the environmental sustainability of a product, system or process?” Multiple well-known authors share their expertise in order to give a broad perspective of this issue from a chemical and environmental engineering perspective. This mathematical, quantitative book includes many case studies to assist with the practical application of environmental and sustainability methods. Readers learn how to efficiently assess and use these methods. This book summarizes all relevant environmental methodologies to assess the sustainability of a product and tools, in order to develop more green products or processes. With life cycle assessment as its main methodology, this book speaks to engineers interested in environmental impact and sustainability.
Helps engineers to assess, evaluate, and measure sustainability in industryProvides workable approaches to environmental and sustainability assessmentReaders learn tools to assess the sustainability of a process or product and to design it in an environmentally friendly way

Autoren/Hrsg.

Klemes, Jiri J

Klemes, Jiri

Inhaltsverzeichnis

1;Front Cover;1
2;Assessing and Measuring Environmental Impact and Sustainability;4
3;Copyright Page;5
4;Contents;6
5;Contributors;16
6;Preface;20
7;Acknowledgments;22
8;Introduction;24
8.1;Introduction;24
8.2;Suitability Definitions, Systems Approach to Sustainability, and Ways to Express and Measure Sustainability;26
8.3;Quantification of the Environmental Impacts: Footprints;30
8.4;Sustainable Design, Planning, and Management;35
8.5;Policies Toward the Sustainability;41
8.6;Conclusion;44
8.7;References;44
9;1 Engineering sustainability;50
9.1;Introduction;50
9.2;Extending boundaries;51
9.3;Systems analysis approach to sustainability and engineering challenges;55
9.3.1;Uncertainty analysis;56
9.3.1.1;Static uncertainties;56
9.3.1.2;Dynamic uncertainties;57
9.3.1.2.1;Ito process representation;57
9.3.1.2.2;Mental models;59
9.3.2;Decision making with uncertainty;60
9.3.2.1;Optimization approach;60
9.3.2.2;Agent-based scenario analysis;63
9.4;Case studies;64
9.5;Summary;71
9.6;References;71
10;2 Recent developments in the application of Fisher information to sustainable environmental management;74
10.1;Introduction;74
10.2;Information theory;75
10.2.1;Fisher information;75
10.2.2;Dynamic order, regime shifts, and the sustainable regimes hypothesis;77
10.3;Comparison of Computational Approaches: Assessing Sustainability in Model Systems;79
10.3.1;Representing the trajectory of a system with elementary functions;80
10.3.2;Wastewater reactor model for nitrogen removal;83
10.3.3;Shallow lake model: regime shift;86
10.3.4;Summary: comparison of methods 1 and 2;88
10.4;Application to real urban, regional, and national systems;90
10.4.1;Urban: Ohio statistical metropolitan areas;90
10.4.2;Early warning signals, regime change, and leading indicators;97
10.4.2.1;Bayes theorem;100
10.4.3;Regional: San Luis Basin, Colorado;101
10.4.4;National: United States;103
10.4.5;Exploring declines in FI as early warning signals of critical transition;105
10.5;Concluding remarks;106
10.6;Acknowledgments;108
10.7;Disclaimer;108
10.8;Appendix 2.1 Approaches to estimating FI;108
10.8.1;Method 1: Continuous form of FI as a function of the velocity and acceleration of the system’s trajectory;108
10.8.2;Method 2: Discrete form as a function of the probability density of system states;110
10.8.2.1;Binning approach;111
10.8.2.2;Algorithm;113
10.9;Appendix 2.2 FI at tipping points;114
10.10;References;118
11;3 Sustainable process index;122
11.1;Measuring ecological impact—the normative base of the SPI;122
11.2;Assigning footprints to material flows;124
11.2.1;Calculating areas for materials subject to global material cycles;125
11.2.2;Calculating areas for all other materials;126
11.3;LCA with the SPI;127
11.4;Applications of the SPI;129
11.5;Characteristics of the SPI assessment;130
11.6;Tools Based on the SPI;132
11.7;Conclusion;133
11.8;References;133
12;4 Moving to a decision point in sustainability analyses;136
12.1;Introduction;136
12.2;Defining a system in the context of sustainability;137
12.3;Indicator-based system assessment for sustainability;138
12.4;History of sustainability analysis through the use of indicators and metrics;139
12.4.1;AIChE sustainability metrics suite/BRIDGES to sustainability metrics;140
12.4.2;IChemE sustainable development process metrics;142
12.4.3;AIChE Sustainability Index;144
12.4.4;BASF: Eco-efficiency and SEEBALANCE® analysis;147
12.5;Research methodology;149
12.5.1;Need for aggregate index PLS-VIP method;149
12.5.2;Steps in sustainability analysis using the aggregate index PLS-VIP method;151
12.5.2.1;Step 1: Ensure quality and unidirectionality of indicator data;151
12.5.2.2;Step 2: Compare relative sustainability of options: the aggregate index method for sustainability footprint;152
12.5.2.3;Step 3: Compare the ranking of indicators: PLS-VIP method;154
12.6;Case studies from engineering systems;156
12.6.1;Case Study 1: Comparison of biofuel systems;156
12.6.1.1;1a: Comparison of various feedstocks for the sustainable production of biodiesel;157
12.6.1.2;1b: Comparison of sustainability of biofuel options;160
12.6.2;Case Study 2: Environmental impact comparison of indoor and outdoor growth of algal species for biodiesel production;164
12.6.3;Case Study 3: Comparison of environmental impacts of polymers;168
12.7;Conclusions;174
12.8;Acknowledgment;176
12.9;References;176
13;5 Overview of environmental footprints;180
13.1;Glossary;180
13.2;Introduction;182
13.3;Life cycle thinking and LCA framework;183
13.4;Direct, indirect, and total effects;187
13.5;Measuring environmental sustainability;190
13.5.1;Indicators of potential environmental impacts;190
13.5.2;Eco-efficiency;192
13.5.3;Environmental footprints;192
13.5.4;Eco and total profit, eco and total NPV, and other combined criteria for measuring direct, indirect, and total effects;193
13.5.4.1;Eco cost (burdening the environment);194
13.5.4.2;Eco benefit (unburdening the environment);194
13.5.4.3;Eco profit (=eco benefit-eco cost);195
13.5.4.4;Net profit;195
13.5.4.5;Total profit;195
13.5.4.6;Eco NPV;195
13.5.4.7;Total NPV;196
13.5.5;Sustainability Indexes;196
13.5.5.1;Environmental indicators;196
13.5.5.1.1;Relative sustainability index;197
13.5.6;Multi-objective optimization;198
13.5.7;Aggregate measure of environmental sustainability;199
13.6;Key environmental footprints;200
13.6.1;Carbon footprint;208
13.6.2;Water footprint;210
13.6.3;Ecological footprint;213
13.6.4;Energy footprint;215
13.6.5;Nitrogen footprint;217
13.6.6;Phosphorus footprint;219
13.6.7;Biodiversity footprint;219
13.6.8;Land footprint;221
13.7;Other Environmental Footprints;222
13.7.1;Material footprint;222
13.7.2;Emission footprint;222
13.7.3;Human footprint;223
13.7.4;Waste footprint;223
13.7.5;Emergy footprint;223
13.7.6;Exergy footprint;224
13.7.7;Chemical footprint;225
13.7.8;Pollution footprint;225
13.7.9;Radioactive footprint;225
13.7.10;Food footprint;225
13.7.11;Nutritional footprint;226
13.8;Concluding Remarks;226
13.9;Acknowledgment;227
13.10;References;227
14;6 N footprint and the nexus between C and N footprints;244
14.1;Motivation;244
14.2;Background on Footprints;245
14.3;Scale of Footprints;248
14.3.1;Process scale;249
14.3.2;Life cycle footprints;249
14.3.3;Economic input–output footprint;250
14.3.4;Hybrid scale footprint;251
14.3.5;National scale;253
14.4;Nitrogen Accounting and Footprint;253
14.5;Eco-LCA Nitrogen Footprint;255
14.6;Eco-LCA Nitrogen Inventory and Calculations;256
14.7;Eco-LCA Nitrogen Footprint for 2002 US Economy;257
14.8;Eco-LCA Nitrogen Footprint for Products;260
14.9;Case Study: Comparison of Eco-LCA Nitrogen Footprint for Fuels;260
14.10;Nexus of Carbon and Nitrogen Footprint;263
14.11;C–N Nexus for Product Scale: Case Study of Fuels;264
14.12;Appendix 6.1;266
14.13;References;266
15;7 The water footprint of industry;270
15.1;Introduction;270
15.2;The WF Concept;271
15.3;Methods to Trace Natural Resources Use and Pollution Over Supply Chains;274
15.4;Direct and Indirect WFs of Different Sectors of the Economy;278
15.4.1;The importance of water use in the primary sector;278
15.4.2;Agriculture, fishing, and forestry;281
15.4.3;Mining and quarrying;284
15.4.4;Manufacturing;286
15.4.4.1;Food and beverage products;286
15.4.4.2;Textile and apparel;286
15.4.4.3;Paper;288
15.4.4.4;Computers;290
15.4.4.5;Motor vehicles;290
15.4.5;Water supply;292
15.4.6;Construction;292
15.4.7;Transport;292
15.4.8;Wholesale, retail trade, and services;293
15.5;Water Stewardship and Transparency;295
15.6;Conclusion;299
15.7;Acknowledgment;299
15.8;References;299
16;8 Life cycle sustainability aspects of microalgal biofuels;304
16.1;Introduction;304
16.2;Background;304
16.3;Political motivation;306
16.4;LCA framework;307
16.4.1;Goal and scope definition;308
16.4.2;Life cycle inventory analysis;309
16.4.3;Life cycle impact assessment;309
16.4.4;Allocation in LCA;310
16.4.5;Interpretation;311
16.5;Sustainability metrics;312
16.5.1;Energy metrics;312
16.5.2;Carbon footprint;314
16.5.3;Water footprint (the detailed description and assessment have been provided in Chapter 7);314
16.6;Case study;316
16.6.1;Results;318
16.7;Water footprint of biofuels;320
16.8;Comparison of prior microalgal biofuel LCAs;321
16.9;Conclusions;322
16.10;References;323
17;9 Methods and tools for sustainable chemical process design;326
17.1;Nomenclature;326
17.1.1;Abbreviations;326
17.1.2;Variables;327
17.1.3;Parameters;328
17.2;Introduction;328
17.2.1;LCA: methodologies;330
17.2.2;Computer-aided tools for sustainable chemical process design;330
17.3;Sustainable process synthesis and design framework;332
17.3.1;Part 1—Problem definition;333
17.3.1.1;Step 1.1—Problem and objective function (Fobj) definition;333
17.3.1.2;Step 1.2—Literature survey for raw materials and process technologies;333
17.3.2;Part 2—Superstructure generation;335
17.3.2.1;Step 2.1—Raw material and product properties, group definition, and synthesis rules definition;336
17.3.2.2;Step 2.2—Property difference definition and selection;336
17.3.2.3;Step 2.3—Superstructure: process-step generation;336
17.3.2.4;Step 2.4—Superstructure screening;336
17.3.2.5;Step 2.5—Process technologies identification;338
17.3.2.6;Step 2.6—Process-interval superstructure generation;338
17.3.2.7;Step 2.7—Process-interval superstructure screening and generation of the “refined” superstructure;338
17.3.2.8;Step 2.8—Process modeling;339
17.3.2.9;Step 2.9—Base case(s) identification;339
17.3.3;Part 3—Process simulation, sustainability analysis, and bottleneck identification;342
17.3.3.1;Step 3.1—Base case(s) rigorous simulation;342
17.3.3.2;Step 3.2—Mass and energy indicators;342
17.3.3.3;Step 3.3—Full economic analysis;342
17.3.3.4;Step 3.4—Environmental impact analysis;343
17.3.3.5;Step 3.5—Design targets;344
17.3.4;Part 4—Generation and screening of new options;344
17.3.4.1;Step 4.1—Generation of new design alternatives for the base case(s);344
17.3.4.2;Step 4.2—Alternatives rigorous simulation;344
17.3.4.3;Step 4.3—Full sustainability analysis of the new options generated in step 4.1;345
17.3.4.4;Step 4.4—Screening and selection of the best alternative through a multicriteria multiobjective decision;346
17.3.5;Software tools available/involved through the framework;346
17.3.5.1;Superstructure information;346
17.3.5.1.1;Superstructure notation;347
17.3.5.1.2;Incremental superstructure synthesis;348
17.3.6;ProPred;348
17.3.7;SustainPro;348
17.3.8;ECON;349
17.3.9;LCSoft;349
17.3.10;ICAS;352
17.3.11;PROII;352
17.3.12;Aspen-plus;352
17.4;Case study;352
17.4.1;Part 1—Problem definition;354
17.4.2;Part 2—Superstructure generation and base case(s) identification;355
17.4.3;Part 3—Process simulation, sustainability analysis, and bottleneck identification;356
17.4.3.1;Step 3.1;356
17.4.3.2;Step 3.2;356
17.4.3.3;Step 3.3;358
17.4.3.4;Step 3.4;361
17.4.3.5;Step 3.5—Design targets;361
17.4.4;Part 4—Generation and screening of new options;362
17.4.4.1;Step 4.1—Generation of new alternatives;362
17.4.4.2;Step 4.2—Rigorous simulation of the alternatives generated;362
17.4.4.3;Step 4.3—Full sustainability analysis of the generated alternatives;362
17.4.4.4;Step 4.4—End step: screening and selection of the best option to satisfy the design problem;362
17.5;Concluding remarks;364
17.6;Appendix A;365
17.7;References;367
18;10 Life cycle assessment as a comparative analysis tool for sustainable brownfield redevelopment projects: cumulative energ...;372
18.1;Introduction and Purpose;372
18.2;Relevance;373
18.2.1;Related LCA studies;374
18.2.2;Rationale for LCA research;376
18.3;Brownfield site histories;377
18.3.1;Chicago Center for Green Technology;377
18.3.2;The Sigma site;377
18.4;Environmental assessment and remediation;379
18.4.1;Chicago Center for Green Technology;379
18.4.2;The Sigma site;380
18.5;Site Development and Building Design Features;381
18.5.1;Chicago Center for Green Technology;381
18.5.2;The Sigma site;381
18.6;LCA methodology;382
18.6.1;Scope and boundary;382
18.6.2;Tools;383
18.6.3;Data;383
18.6.3.1;Brownfield remediation;383
18.6.3.2;Site redevelopment;384
18.6.3.3;Postdevelopment building operating energy;384
18.6.3.4;Postdevelopment commuter transportation operating energy;385
18.7;LCA results;385
18.7.1;Brownfield assessment and remediation;385
18.7.2;Building rehabilitation and construction;386
18.7.3;Operating energy;389
18.7.3.1;Chicago Center for Green Technology;389
18.7.3.2;Sigma site;389
18.7.3.3;The impact of commuter transportation and electricity source;391
18.7.4;Comparison of life cycle stages;393
18.8;Discussion;396
18.8.1;Building operating energy;396
18.8.2;Transportation operating energy;397
18.9;Conclusions;397
18.10;Acknowledgments;399
18.11;Appendix A Commuter transportation survey;399
18.11.1;Personal Commuting Information;400
18.11.2;Weekly Commuting Schedule;400
18.11.3;Vehicle Information [fill out if you drive alone OR carpool for any portion of your commute];400
18.11.4;Variation in Commuting Behavior;401
18.11.5;Commuter Amenities;401
18.12;Appendix B CCGT LCA input data;403
18.13;Appendix C Sigma site LCA input data;407
18.14;References;410
19;11 The Environmental Performance Strategy Map: an integrated life cycle assessment approach to support the strategic decisi...;416
19.1;Introduction;416
19.1.1;Background;416
19.1.2;LCA: History;417
19.1.3;LCA: an overview of the general framework;419
19.1.3.1;Goal and scope definition;419
19.1.3.2;Inventory analysis;420
19.1.3.3;Impact assessment;421
19.1.3.4;Interpretation;422
19.1.4;Limitations of LCA approaches;422
19.1.5;The Human Factor: Work Environment in LCA;423
19.2;From Environmental Assessment to Strategic Environmental Maps;426
19.2.1;Introduction;426
19.2.2;What footprints?;427
19.2.2.1;Carbon footprint;428
19.2.2.2;Water footprint;428
19.2.2.3;Energy footprint;429
19.2.2.4;Emissions footprint;429
19.2.2.5;Work environment footprint;429
19.2.3;Building the map;430
19.2.4;The SEPI and policy making;432
19.2.5;Conclusions;434
19.3;The Environmental Bill of Materials and Technology Routing;434
19.3.1;How to use the Env-BOM and technology routing;437
19.3.1.1;Short process description;437
19.3.1.2;Definition of the environmental bill of materials;438
19.3.2;Conclusions;441
19.4;Uncertainty Estimation in the Definition of the EPSM;442
19.4.1;Introduction to fuzzy logic;442
19.4.2;Case study;443
19.4.3;Fuzzy inference system for EPSM calculation;443
19.4.4;Case study results;447
19.4.5;Conclusions;449
19.5;The E3-Methodology in LCA evaluation;449
19.5.1;The methodology;450
19.5.2;The E3-methodology—the steps;451
19.5.2.1;Step 1;451
19.5.2.2;Step 2;452
19.5.2.3;Step 3;452
19.5.2.4;Step 4;452
19.5.2.5;Step 5;453
19.6;Chapter Conclusions;453
19.7;References;455
20;12 Green supply chain toward sustainable industry development;458
20.1;Introduction;458
20.2;Development of GSCM;460
20.2.1;Green ERP in GSCM;460
20.2.2;LCA in GSCM;461
20.2.3;Development of optimization technique for GSCM;462
20.2.4;GT in GSCM;466
20.3;Formulation of GSC Model;468
20.3.1;Process integration approach;469
20.3.2;Superstructural approach;475
20.3.3;P-graph approach;481
20.4;Further reading;490
20.5;Conclusions;491
20.6;Acknowledgment;492
20.7;References;492
21;13 Supply and demand planning and management tools toward low carbon emissions;500
21.1;Introduction;500
21.2;Carbon Pinch Analysis design method;502
21.2.1;The energy demand composite curve;503
21.2.2;The energy supply composite curve;504
21.2.3;Aggregated planning with overall emission limits (Case 1);504
21.2.4;Targeting emissions with fixed zero-carbon energy supply;506
21.3;Carbon Footprint Improvement Based on Pinch Analysis;507
21.4;Carbon emission Pinch Analysis;508
21.4.1;Step 1: Identify the carbon sources and demands;510
21.4.2;Step 2: Targeting the maximum carbon exchange;511
21.4.3;Step 3: Setting the holistic minimum carbon targets;512
21.4.3.1;The Carbon Management Hierarchy (CMH);512
21.4.3.1.1;Level 1: Direct reuse;512
21.4.3.1.2;Level 2: Source and demand manipulations;513
21.4.3.1.2.1;Source elimination and reduction;513
21.4.3.1.2.2;Demand manipulation;514
21.4.3.1.3;Level 3: Regeneration–reuse;514
21.4.3.1.4;Level 4: Carbon sequestration;514
21.4.4;Step 4: Minimum carbon network design;514
21.4.5;IP case study;515
21.4.5.1;Step 1: Data extraction;515
21.4.5.1.1;Source data extraction;515
21.4.5.1.2;Carbon demands planning;515
21.4.5.2;Step 2: Setting the holistic minimum carbon targets;517
21.4.5.2.1;CMH Level 1: Direct reuse;517
21.4.5.2.2;CMH Level 2: Source and demand manipulations;518
21.4.5.2.3;CMH Level 3: Regeneration–reuse;521
21.4.5.2.4;CMH Level 4: Carbon sequestration;522
21.4.5.3;Step 3: Carbon distribution network design;524
21.5;Conclusion;524
21.6;References;524
22;14 Setting a policy for sustainability: the importance of measurement;528
22.1;Introduction;528
22.1.1;The need for measurement;528
22.1.2;A generalized system construct;529
22.1.3;Capital assets or stocks;530
22.1.4;Requirements for a measurement framework;532
22.2;The Process Analysis Method;532
22.2.1;Preliminary scoping and essential definitions;533
22.2.1.1;Overview and background;533
22.2.1.2;Defining the system boundary;534
22.2.1.3;Defining sustainability;534
22.2.2;Sustainability framework;535
22.2.2.1;Internal impact generators;535
22.2.2.2;External impact receivers;535
22.2.2.3;Issues;535
22.2.2.4;Indicators;535
22.2.2.5;Metrics;535
22.2.3;Verification and modification;536
22.3;Some Examples of Sustainability Assessment;536
22.3.1;The oil palm case study;536
22.3.2;The car transport system;538
22.3.2.1;User decisions;539
22.3.2.2;Government policy and regulation;539
22.3.2.3;Car manufacturer design, research, and development choices;539
22.3.2.4;Car manufacturer sustainability policy and practices;539
22.3.3;Assessing the sustainability of arsenic mitigation technology for drinking water ;540
22.4;Summary and Conclusions;543
22.5;References;544
23;15 Sustainability assessments of buildings, communities, and cities;546
23.1;Introduction;546
23.2;Framework of Sustainability Assessment;549
23.3;Systems for Sustainability Assessment of Buildings;553
23.3.1;Description of the systems;555
23.3.1.1;CED systems;556
23.3.1.2;LCA systems;559
23.3.1.3;TQA systems;560
23.3.2;Comparison between systems;560
23.3.3;Characteristics of certified buildings;564
23.3.4;Limits and trends in sustainability assessment of buildings;568
23.4;Sustainability Assessment of Urban Communities;569
23.4.1;Description of the systems;569
23.4.2;Comparison between the systems;571
23.4.3;Characteristics of certified communities;574
23.4.4;Limits of sustainability assessment of communities;577
23.4.4.1;Assessment of a weak sustainability;577
23.4.4.2;Limits of static sustainability assessment;578
23.4.4.3;Limits of adaptability and stakeholders’ engagement;579
23.5;Systems for Sustainability Assessment of Cities;580
23.5.1;Description of the systems;580
23.5.2;Comparison between the systems;584
23.6;Discussion and Conclusions;587
23.7;References;588
24;Index;596

Leseproben

Chapter 1 Engineering sustainability
Urmila Diwekar,    Vishwamitra Research Institute, Crystal Lake, IL, USA Sustainability analysis is, by definition, multi-disciplinary. In this chapter, we present engineering sustainability by using various systems analysis approaches from different disciplines. Sustainability starts with green manufacturing and extends to industrial networks and then to the ecosystem. These systems are complex and extend boundaries of current analysis frameworks. Uncertainties exist at every stage of analysis, and dealing with uncertainty in a systematic way is necessary. Analysis approaches from optimization, social science, and financial literature are used to solve problems related to sustainability. Keywords
Sustainability; industrial ecology; green design; uncertainty; stochastic optimal control; forecasting; Ito processes Introduction
The most widely accepted definition of sustainability or sustainable development is attributed to the World Commission on Environment and Development (1987). The Commission stated in its final report that “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” This definition clearly defines sustainability on a larger timescale by alluding to the future (Cabezas, 2013). Engineers are good at decision making when they can model the phenomenon correctly. However, sustainability that starts with green manufacturing involves beginning decision making at early stages of design and considering objectives that are not well quantified. This involves multi-criteria decision making in the face of uncertainties. Industrial ecology deals with complex industrial networks and dynamic decision making. Sustainability goes beyond industrial ecology when the system boundaries extend to our planet and long-term impacts need to be considered. Dynamic decision making and adaptive management are needed to address the complete problem of sustainability. Systems analysis approaches can provide tools for addressing these problems. However, there are challenges in applying current approaches to sustainable system analysis. Forecasting is an important part of decision making because sustainability is important for the future. Given that the future brings many uncertainties, addressing these uncertainties in both quantitative and qualitative forms is important in decision making. These uncertainties could be static or dynamic. It should be remembered that sustainability is a path, and sustainable decision making involves finding this path through time. There are no absolutes in sustainability; it is always relative (Sikdar, 2014). We present the problem of sustainability, starting with sustainable manufacturing as extending boundaries of traditional decision making, followed by systems analysis approaches and challenges to solve this problem. Extending boundaries
The Burndland commission definition (World Commission on Environment and Development, 1987) of sustainability originated from the concerns that the current trends in population and economic developments are not sustainable. Coupled with adverse environmental impacts like global climate change, degradation of air, water, and land, depletion of natural resources, including freshwater minerals, loss of agricultural land because of deforestation, soil erosion, and urbanization are threatening the ecosystem (Bakshi and Fiksel, 2003). To make the planet sustainable, the efforts should be made at all levels of ecosystem. At the industrial level, green engineering means green processes, green products, green energy, and eco-friendly management by considering environmental and other societal impacts as objectives and not constraints used in traditional design. Figure 1.1 shows the integrated framework recently developed (Diwekar, 2003) to include the green engineering principles at all stages. Unlike the traditional process design in which engineers are looking for low-cost options, environmental considerations include various objectives like the long-term and short-term environmental impacts. This framework includes decisions at all levels, starting from the chemical or material selection and the process synthesis stages, to the management and planning stage, which is linked to green objectives and goals (top left corner of the Figure 1.1). It is important to start as early as possible in the design process to enhance the impact of waste minimization (Figure 1.2) (Yang and Shi, 2000).
Figure 1.1 Integrated framework for green process design (Diwekar, 2003).
Figure 1.2 Opportunities of environmental impact minimization along the process life cycle. Reproduced from Yang and Shi (2000). It should be remembered that as we extend the traditional boundaries of process and plant design to include early decisions like material selections, the models and data available for the analysis are fraught with uncertainties. Therefore, to address sustainability at the plant level, we need to use multi-objective methods in the face of uncertainties. Life cycle considerations and commercial afterlife are also included in this decision making. They are shown beyond the boundaries of the framework, because life cycle analysis and afterlife analysis involve extending the boundary of the design further. Obviously, uncertainties increase as we extend the framework. This framework shows that the first step toward green engineering is to extend the traditional boundaries of design. Because sustainability is a property of the entire system, it requires boundaries of the design to be greatly expanded beyond green design, green products, and green management to industrial ecology and to the ecosystem of the entire planet (Figure 1.3) (Diwekar and Shastri, 2010).
Figure 1.3 From green design to industrial ecology to sustainability (Diwekar and Shastri, 2010). The next step toward sustainability in Figure 1.3 is industrial ecology. Industrial ecology is the study of the flows of materials and energy in industrial and consumer activities, of the effects of these flows on the environment, and of the influences of economic, political, regulatory, and social factors on the use, transformation, and disposition of resources (White, 1994). Industrial ecology applies the principles of material and energy balance traditionally used by scientists and engineers to analyze well-defined ecological systems or industrial unit operations to more complex systems involving natural and human interaction. These systems can involve activities and resource utilization over scales ranging from single industrial plants to entire sectors, regions, or economies. In so doing, the laws of conservation must consider a wide range of interacting economic, social, and environmental indicators. Figure 1.4 presents a conceptual framework for industrial ecology applied at different scales of spatial and economic organization evaluating alternative management options using different types of information, tools for analysis, and criteria for performance evaluation. As one moves from the small scale of a single-unit operation or industrial production plant to the larger scales of an integrated industrial park, community, firm, or sector, the available management options expand from simple changes in process operation and inputs to more complex resource management strategies, including integrated waste recycling and reuse options. The information changes from quantitative to order of magnitude to qualitative, and uncertainties increase. Special focus has been placed on implementing the latter via industrial symbiosis, for example, through the pioneering work of integrating several industrial and municipal facilities in Kalundborg, Denmark (Ehrenfeld and Gertler, 1997). Business case of sustainability (Beloff, 2013) often describes the triple bottom line approach, namely, economic performance, environmental performance, and social performance of industry. However, industrial ecology advocates going beyond this triple bottom line. Industrial networks comprise any number of organizations that are linked to each other through the exchange of resources. A systems approach is a necessary prerequisite to understanding interaction between organizations and its consequences for the socio-economic and biophysical system in which organizations function. The function and structure of the industrial network are influenced by competition and cooperation between organizations trying to maintain or improve their position within the network (Petrie et al., 2013). It is this dynamic behavior that makes industrial networks inherently uncertain.
Figure 1.4 Industrial ecology sources of information and evaluation criteria. Sustainability goes beyond industrial ecology, where the natural boundary for sustainability science is the entire planet Earth. Earth can be considered a closed system, at least on the timescale of human history. The difficulty is that on the planetary scale when ecosystems, societies, and economies are considered, the system becomes very large and very complex. Uncertainties become time dependent and decision forecasting is essential to study the sustainability of the planet. Cabezas (2013) defines sustainability as a path or system trajectory moving in various dimensions of ecology, society, time, economy, and energy. This is depicted...