Design in the chemical industry increasingly aims not only at economic but also at environmental targets. Environmental targets are usually best quantified using the standardized, holistic method of life cycle assessment (LCA). The resulting life cycle perspective poses a major challenge to chemical engineering design because the design scope is expanded to include process, product, and supply chain. Here, we first provide a brief tutorial highlighting key elements of LCA. Methods to fill data gaps in LCA are discussed, as capturing the full life cycle is data intensive. On this basis, we review recent methods for integrating LCA into the design of chemical processes, products, and supply chains. Whereas adding LCA as a posteriori tool for decision support can be regarded as established, the integration of LCA into the design process is an active field of research. We present recent advances and derive future challenges for LCA-based design.


Article metrics loading...

Loading full text...

Full text loading...


Literature Cited

  1. 1. 
    Rahimifard S, Trollman H. 2018. UN Sustainable Development Goals: an engineering perspective. Int. J. Sustain. Eng. 11:11–3
    [Google Scholar]
  2. 2. 
    Grossmann IE. 2004. Challenges in the new millennium: product discovery and design, enterprise and supply chain optimization, global life cycle assessment. Comput. Chem. Eng. 29:129–39
    [Google Scholar]
  3. 3. 
    Martín M, Adams TA II 2019. Challenges and future directions for process and product synthesis and design. Comput. Chem. Eng. 128:421–36
    [Google Scholar]
  4. 4. 
    Grossmann IE, Harjunkoski I. 2019. Process systems engineering: academic and industrial perspectives. Comput. Chem. Eng. 126:474–84
    [Google Scholar]
  5. 5. 
    Garcia DJ, You F. 2015. Supply chain design and optimization: challenges and opportunities. Comput. Chem. Eng. 81:153–70
    [Google Scholar]
  6. 6. 
    Barbosa-Póvoa AP, Pinto JM. 2019. Process supply chains: perspectives from academia and industry. Comput. Chem. Eng. 132:106606
    [Google Scholar]
  7. 7. 
    Azapagic A, Perdan S. 2014. Sustainable chemical engineering: dealing with “wicked” sustainability problems. AIChE J 60:123998–4007
    [Google Scholar]
  8. 8. 
    Bakshi BR. 2014. Methods and tools for sustainable process design. Curr. Opin. Chem. Eng. 6:69–74
    [Google Scholar]
  9. 9. 
    Bakshi BR. 2019. Toward sustainable chemical engineering: the role of process systems engineering. Annu. Rev. Chem. Biomol. Eng. 10:265–88
    [Google Scholar]
  10. 10. 
    Brown BJ, Hanson ME, Liverman DM, Merideth RW 1987. Global sustainability: toward definition. Environ. Manag. 11:6713–19
    [Google Scholar]
  11. 11. 
    Cano-Ruiz JA, McRae GJ. 1998. Environmentally conscious chemical process design. Annu. Rev. Energy Environ. 23:1499–536
    [Google Scholar]
  12. 12. 
    Diwekar U, Shastri Y. 2011. Design for environment: a state-of-the-art review. Clean Technol. Environ. Policy 13:2227–40
    [Google Scholar]
  13. 13. 
    Adu IK, Sugiyama H, Fischer U, Hungerbühler K 2008. Comparison of methods for assessing environmental, health and safety (EHS) hazards in early phases of chemical process design. Process Saf. Environ. Prot. 86:277–93
    [Google Scholar]
  14. 14. 
    Roy N, Eljack F, Jiménez-Gutiérrez A, Zhang B, Thiruvenkataswamy P et al. 2016. A review of safety indices for process design. Curr. Opin. Chem. Eng. 14:42–48
    [Google Scholar]
  15. 15. 
    Int. Org. Stand 2006. Environmental management—life cycle assessment—principles and framework Stand. 14040, Int. Org. Stand Geneva:
  16. 16. 
    Guinée JB, Heijungs R, Huppes G, Zamagni A, Masoni P et al. 2011. Life cycle assessment: past, present, and future. Environ. Sci. Technol. 45:190–96
    [Google Scholar]
  17. 17. 
    Douglas JM. 1988. Conceptual Design of Chemical Processes New York: McGraw-Hill
  18. 18. 
    Biegler LT, Grossmann IE, Westerberg AW, Kravanja Z 1997. Systematic Methods for Chemical Process Design Phys. Chem. Eng. Sci Upper Saddle River, NJ: Prentice Hall
  19. 19. 
    Seider WD, Seader JD, Lewin DR 2009. Product and Process Design Principles: Synthesis, Analysis and Evaluation Hoboken, NJ: John Wiley & Sons Inc.
  20. 20. 
    Smith R. 2005. Chemical Process: Design and Integration Hoboken, NJ: John Wiley & Sons
  21. 21. 
    Azapagic A, Clift R. 1999. The application of life cycle assessment to process optimisation. Comput. Chem. Eng. 23:101509–26
    [Google Scholar]
  22. 22. 
    Azapagic A. 1999. Life cycle assessment and its application to process selection, design and optimisation. Chem. Eng. J. 73:11–21
    [Google Scholar]
  23. 23. 
    Perry RH, Green DW, Maloney JO 1984. Perry's Chemical Engineers’ Handbook New York: McGraw-Hill
  24. 24. 
    Grossmann IE, Drabbant R, Jain RK 1982. Incorporating toxicology in the synthesis of industrial chemical complexes. Chem. Eng. Commun. 17:1–6151–70
    [Google Scholar]
  25. 25. 
    Eur. Comm.-Joint Res. Cent., Inst. Environ. Sustain 2010. International Reference Life Cycle Data System (ILCD) Handbook: Framework and Requirements for Life Cycle Impact Assessment Models and Indicators EUR 24586 EN. Luxemburg: Publ. Off. Eur. Union. , 1st ed..
  26. 26. 
    von der Assen N, Voll P, Peters M, Bardow A 2014. Life cycle assessment of CO2 capture and utilization. A tutorial review. Chem. Soc. 43:237982–94
    [Google Scholar]
  27. 27. 
    Artz J, Müller TE, Thenert K, Kleinekorte J, Meys R et al. 2017. Sustainable conversion of carbon dioxide: an integrated review of catalysis and life cycle assessment. Chem. Rev. 118:2434–504
    [Google Scholar]
  28. 28. 
    Hauschild MZ, Huijbregts MAJ. 2015. Life Cycle Impact Assessment Dordrecht, Neth: Springer
  29. 29. 
    Frischknecht R, Jolliet O. 2016. Global Guidance for Life Cycle Impact Assessment Indicators Vol. 1 Nairobi: UN Environ. Progr.
  30. 30. 
    Eur. Comm.-Joint Res. Cent., Inst. Environ. Sustain 2011. International Reference Life Cycle Data System (ILCD) Handbook: Recommendations for Life Cycle Impact Assessment in the European Context - Based on Existing Environmental Impact Assessment Models and Factors EUR 24571 EN. Luxemburg: Publ. Off. Eur. Union. , 1st ed..
  31. 31. 
    Zimmermann A, Wunderlich J, Buchner G, Müller L, Armstrong K et al. 2018. Techno-economic assessment & life-cycle assessment guidelines for CO2 utilization Tech. Rep., Glob. CO2 Initiat., Univ. Mich Ann Arbor, MI: https://doi.org/10.3998/2027.42/145436
  32. 32. 
    Metz B, Davidson O, de Coninck H, Loos M, Meyer L 2005. IPCC Special Report on Carbon Dioxide Capture and Storage Cambridge, UK: Cambridge Univ. Press
  33. 33. 
    Heijungs R. 2014. Ten easy lessons for good communication of LCA. Int. J. Life Cycle Assess. 19:3473–76
    [Google Scholar]
  34. 34. 
    von der Assen N, Jung J, Bardow A 2013. Life cycle assessment of carbon dioxide capture and utilization: avoiding the pitfalls. Energy Environ. Sci. 6:92721–34
    [Google Scholar]
  35. 35. 
    McCulloch A, Lindley AA. 2003. From mine to refrigeration: a life cycle inventory analysis of the production of HFC-134a. Int. J. Refrig. 26:8865–72
    [Google Scholar]
  36. 36. 
    Sternberg A, Bardow A. 2015. Power-to-what?—Environmental assessment of energy storage systems. Energy Environ. Sci. 8:2389–400
    [Google Scholar]
  37. 37. 
    Jiménez-González C, Kim S, Overcash MR 2000. Methodology for developing gate-to-gate life cycle inventory information. Int. J. Life Cycle Assess. 5:3153–59
    [Google Scholar]
  38. 38. 
    Evonik Ind 2018. Finanzbericht 2018 Essen, Ger: Evonik Ind.
  39. 39. 
    Navigant 2019. Internal carbon pricing for low-carbon investment: a briefing paper on linking climate-related opportunities and risks to financing decisions for investors and banks Brief. Pap., Navigant Boulder, CO:
  40. 40. 
    Azapagic A, Pettit C, Sinclair P 2007. A life cycle methodology for mapping the flows of pollutants in the urban environment. Clean Technol. Environ. Policy 9:3199–214
    [Google Scholar]
  41. 41. 
    Udo de Haes HA, Bensahel JF, Clift R, Fuessler CR, Griesshammer R, Jensen AA 1994. Guidelines for the application of life cycle assessment in the EU ecolabelling programme Rep., Eur. Union Leiden, Neth:.
  42. 42. 
    Frischknecht R. 1998. Life cycle inventory analysis for decision-making. Scope-dependent inventory system models and context-specific joint product allocation. PhD thesis, ETH Zurich Zurich:
  43. 43. 
    Guinée JB. 2002. Handbook on life cycle assessment operational guide to the ISO standards. Int. J. Life Cycle Assess. 7:5311–13
    [Google Scholar]
  44. 44. 
    Parvatker AG, Eckelman MJ. 2018. Comparative evaluation of chemical life cycle inventory generation methods and implications for life cycle assessment results. ACS Sustain. Chem. Eng. 7:1350–67
    [Google Scholar]
  45. 45. 
    Hetherington AC, Borrion AL, Griffiths OG, McManus MC 2014. Use of LCA as a development tool within early research: challenges and issues across different sectors. Int. J. Life Cycle Assess. 19:1130–43
    [Google Scholar]
  46. 46. 
    Simon B, Bachtin K, Kiliç A, Amor B, Weil M 2016. Proposal of a framework for scale‐up life cycle inventory: a case of nanofibers for lithium iron phosphate cathode applications. Integr. Environ. Assess. Manag. 12:3465–77
    [Google Scholar]
  47. 47. 
    Piccinno F, Hischier R, Seeger S, Som C 2016. From laboratory to industrial scale: a scale-up framework for chemical processes in life cycle assessment studies. J. Clean. Prod. 135:1085–97
    [Google Scholar]
  48. 48. 
    Casamayor JL, Su D. 2012. Integration of detailed/screening LCA software-based tools into design processes. Design for Innovative Value Towards a Sustainable Society M Matsumoto, Y Umeda, K Masui, S Fukushige 609–14 Dordrecht, Neth: Springer
    [Google Scholar]
  49. 49. 
    Pascual-González J, Guillén-Gosálbez G, Mateo-Sanz JM, Jiménez-Esteller L 2016. Statistical analysis of the ecoinvent database to uncover relationships between life cycle impact assessment metrics. J. Clean. Prod. 112:359–68
    [Google Scholar]
  50. 50. 
    Pascual González J. 2015. Development of systematic methods for the assessment and optimization of life cycle environmental impacts PhD thesis, Univ. Rovira Virgili Tarragona, Spain:
  51. 51. 
    Kostin A, Guillén-Gosálbez G, Mele FD, Jiménez L 2012. Identifying key life cycle assessment metrics in the multiobjective design of bioethanol supply chains using a rigorous mixed-integer linear programming approach. Ind. Eng. Chem. Res. 51:145282–91
    [Google Scholar]
  52. 52. 
    Milà i Canals L, Azapagic A, Doka G, Jefferies D, King H et al. 2011. Approaches for addressing life cycle assessment data gaps for bio‐based products. J. Ind. Ecol. 15:5707–25
    [Google Scholar]
  53. 53. 
    Subramaniam B, Helling RK, Bode CJ 2016. Quantitative sustainability analysis: a powerful tool to develop resource-efficient catalytic technologies. ACS Sustain. Chem. Eng. 4:5859–65
    [Google Scholar]
  54. 54. 
    Pascual-González J, Pozo C, Guillén-Gosálbez G, Jiménez-Esteller L 2015. Combined use of MILP and multi-linear regression to simplify LCA studies. Comput. Chem. Eng. 82:34–43
    [Google Scholar]
  55. 55. 
    Calvo-Serrano R, González-Miquel M, Guillén-Gosálbez G 2019. Integrating COSMO-based σ-profiles with molecular and thermodynamic attributes to predict the life cycle environmental impact of chemicals. ACS Sustain. Chem. Eng. 7:33575–83
    [Google Scholar]
  56. 56. 
    Calvo-Serrano R, González-Miquel M, Papadokonstantakis S, Guillén-Gosálbez G 2017. Predicting the cradle-to-gate environmental impact of chemicals from molecular descriptors and thermodynamic properties via mixed-integer programming. Comput. Chem. Eng. 108:179–93
    [Google Scholar]
  57. 57. 
    Wernet G, Hellweg S, Fischer U, Papadokonstantakis S, Hungerbühler K 2008. Molecular-structure-based models of chemical inventories using neural networks. Environ. Sci. Technol. 42:176717–22
    [Google Scholar]
  58. 58. 
    Wernet G, Papadokonstantakis S, Hellweg S, Hungerbühler K 2009. Bridging data gaps in environmental assessments: modeling impacts of fine and basic chemical production. Green Chem 11:111826–31
    [Google Scholar]
  59. 59. 
    Song R, Keller AA, Suh S 2017. Rapid life-cycle impact screening using artificial neural networks. Environ. Sci. Technol. 51:1810777–85
    [Google Scholar]
  60. 60. 
    Marvuglia A, Kanevski M, Benetto E 2015. Machine learning for toxicity characterization of organic chemical emissions using USEtox database: learning the structure of the input space. Environ. Int. 83:72–85
    [Google Scholar]
  61. 61. 
    Curzons AD, Constable DJC, Mortimer DN, Cunningham VL 2001. So you think your process is green, how do you know?—Using principles of sustainability to determine what is green—a corporate perspective. Green Chem 3:11–6
    [Google Scholar]
  62. 62. 
    Karka P, Papadokonstantakis S, Kokossis A 2019. Environmental impact assessment of biomass process chains at early design stages using decision trees. Int. J. Life Cycle Assess. 24:91675–700
    [Google Scholar]
  63. 63. 
    Kim S, Overcash M. 2003. Energy in chemical manufacturing processes: gate‐to‐gate information for life cycle assessment. J. Chem. Technol. Biotechnol. 78:9995–1005
    [Google Scholar]
  64. 64. 
    Hischier R, Hellweg S, Capello C, Primas A 2005. Establishing life cycle inventories of chemicals based on differing data availability. Int. J. Life Cycle Assess. 10:159–67
    [Google Scholar]
  65. 65. 
    Righi S, Baioli F, Dal Pozzo A, Tugnoli A 2018. Integrating life cycle inventory and process design techniques for the early estimate of energy and material consumption data. Energies 11:4970
    [Google Scholar]
  66. 66. 
    Smith RL, Ruiz-Mercado GJ, Meyer DE, Gonzalez MA, Abraham JP et al. 2017. Coupling computer-aided process simulation and estimations of emissions and land use for rapid life cycle inventory modeling. ACS Sustain. Chem. Eng. 5:53786–94
    [Google Scholar]
  67. 67. 
    Cashman SA, Meyer DE, Edelen AN, Ingwersen WW, Abraham JP et al. 2016. Mining available data from the United States Environmental Protection Agency to support rapid life cycle inventory modeling of chemical manufacturing. Environ. Sci. Technol. 50:179013–25
    [Google Scholar]
  68. 68. 
    Kleinekorte J, Kröger L, Leonhard K, Bardow A 2019. A neural network-based framework to predict process-specific environmental impacts. Proceedings of the 29th European Symposium on Computer Aided Process Engineering AA Kiss, E Zondervan, R Lakerveld, L Özkan 461447–52 Amsterdam: Elsevier
    [Google Scholar]
  69. 69. 
    Martinez-Hernandez E. 2017. Trends in sustainable process design—from molecular to global scales. Curr. Opin. Chem. Eng. 17:35–41
    [Google Scholar]
  70. 70. 
    Skiborowski M. 2018. Process synthesis and design methods for process intensification. Curr. Opin. Chem. Eng. 22:216–25
    [Google Scholar]
  71. 71. 
    Jacquemin L, Pontalier P-Y, Sablayrolles C 2012. Life cycle assessment (LCA) applied to the process industry: a review. Int. J. Life Cycle Assess. 17:81028–41
    [Google Scholar]
  72. 72. 
    Burgess AA, Brennan DJ. 2001. Application of life cycle assessment to chemical processes. Chem. Eng. Sci. 56:82589–604
    [Google Scholar]
  73. 73. 
    Kralisch D, Ott D, Gericke D 2015. Rules and benefits of life cycle assessment in green chemical process and synthesis design: a tutorial review. Green Chem 17:1123–45
    [Google Scholar]
  74. 74. 
    von der Assen N, Müller LJ, Steingrube A, Voll P, Bardow A 2016. Selecting CO2 sources for CO2 utilization by environmental-merit-order curves. Environ. Sci. Technol. 50:31093–101
    [Google Scholar]
  75. 75. 
    Benavides PT, Cronauer DC, Adom F, Wang Z, Dunn JB 2017. The influence of catalysts on biofuel life cycle analysis (LCA). Sustain. Mater. Technol. 11:53–59
    [Google Scholar]
  76. 76. 
    Sternberg A, Jens CM, Bardow A 2017. Life cycle assessment of CO2-based C1-chemicals. Green Chem 19:92244–59
    [Google Scholar]
  77. 77. 
    Spierling S, Knüpffer E, Behnsen H, Mudersbach M, Krieg H et al. 2018. Bio-based plastics—a review of environmental, social and economic impact assessments. J. Clean. Prod. 185:476–91
    [Google Scholar]
  78. 78. 
    Delgove MAF, Laurent A-B, Woodley JM, De Wildeman SMA, Bernaerts KV, van der Meer Y 2019. A prospective life cycle assessment (LCA) of monomer synthesis: comparison of biocatalytic and oxidative chemistry. ChemSusChem 12:71349–60
    [Google Scholar]
  79. 79. 
    Jens CM, Müller L, Leonhard K, Bardow A 2019. To integrate or not to integrate—techno-economic and life cycle assessment of CO2 capture and conversion to methyl formate using methanol. ACS Sustain. Chem. Eng. 14:712270–80
    [Google Scholar]
  80. 80. 
    Gerssen-Gondelach SJ, Saygin D, Wicke B, Patel MK, Faaij APC 2014. Competing uses of biomass: assessment and comparison of the performance of bio-based heat, power, fuels and materials. Renew. Sustain. Energy Rev. 40:964–98
    [Google Scholar]
  81. 81. 
    Ott D, Kralisch D, Denčić I, Hessel V, Laribi Y et al. 2014. Life cycle analysis within pharmaceutical process optimization and intensification: case study of active pharmaceutical ingredient production. ChemSusChem 7:123521–33
    [Google Scholar]
  82. 82. 
    Gear M, Sadhukhan J, Thorpe R, Clift R, Seville J, Keast M 2018. A life cycle assessment data analysis toolkit for the design of novel processes—a case study for a thermal cracking process for mixed plastic waste. J. Clean. Prod. 180:735–47
    [Google Scholar]
  83. 83. 
    Salcedo R, Antipova E, Boer D, Jiménez L, Guillén-Gosálbez G 2012. Multi-objective optimization of solar Rankine cycles coupled with reverse osmosis desalination considering economic and life cycle environmental concerns. Desalination 286:358–71
    [Google Scholar]
  84. 84. 
    Gebreslassie BH, Guillén-Gosálbez G, Jiménez L, Boer D 2009. Design of environmentally conscious absorption cooling systems via multi-objective optimization and life cycle assessment. Appl. Energy 86:91712–22
    [Google Scholar]
  85. 85. 
    Gebreslassie BH, Slivinsky M, Wang B, You F 2013. Life cycle optimization for sustainable design and operations of hydrocarbon biorefinery via fast pyrolysis, hydrotreating and hydrocracking. Comput. Chem. Eng. 50:71–91
    [Google Scholar]
  86. 86. 
    Gonzalez-Garay A, Guillen-Gosalbez G. 2018. SUSCAPE: a framework for the optimal design of SUStainable ChemicAl ProcEsses incorporating data envelopment analysis. Chem. Eng. Res. Des. 137:246–64
    [Google Scholar]
  87. 87. 
    Rodríguez‐Vallejo DF, Galán‐Martín Á, Guillén‐Gosálbez G, Chachuat B 2019. Data envelopment analysis approach to targeting in sustainable chemical process design: application to liquid fuels. AIChE J 65:7e16480
    [Google Scholar]
  88. 88. 
    Helmdach D, Yaseneva P, Heer PK, Schweidtmann AM, Lapkin AA 2017. A multiobjective optimization including results of life cycle assessment in developing biorenewables-based processes. ChemSusChem 10:183632–43
    [Google Scholar]
  89. 89. 
    Goedkoop MJ, Spriensma R. 2001. The Eco-Indicator 99: A Damage Oriented Method for Life Cycle Impact Assessment—Methodology Report Amersfoort, Neth: PRé Consult. BV. , 3rd ed..
  90. 90. 
    Guillén-Gosálbez G. 2011. A novel MILP-based objective reduction method for multi-objective optimization: application to environmental problems. Comput. Chem. Eng. 35:81469–77
    [Google Scholar]
  91. 91. 
    Grossmann IE, Guillén-Gosálbez G. 2010. Scope for the application of mathematical programming techniques in the synthesis and planning of sustainable processes. Comput. Chem. Eng. 34:91365–76
    [Google Scholar]
  92. 92. 
    Chen Q, Grossmann IE. 2017. Recent developments and challenges in optimization-based process synthesis. Annu. Rev. Chem. Biomol. Eng. 8:249–83
    [Google Scholar]
  93. 93. 
    Wang B, Gebreslassie BH, You F 2013. Sustainable design and synthesis of hydrocarbon biorefinery via gasification pathway: integrated life cycle assessment and technoeconomic analysis with multiobjective superstructure optimization. Comput. Chem. Eng. 52:55–76
    [Google Scholar]
  94. 94. 
    Gong J, You F. 2014. Global optimization for sustainable design and synthesis of algae processing network for CO2 mitigation and biofuel production using life cycle optimization. AIChE J 60:93195–210
    [Google Scholar]
  95. 95. 
    Demirhan CD, Tso WW, Powell JB, Pistikopoulos EN 2019. Sustainable ammonia production through process synthesis and global optimization. AIChE J 65:7e16498
    [Google Scholar]
  96. 96. 
    Gopalakrishnan V, Bakshi BR, Ziv G 2016. Assessing the capacity of local ecosystems to meet industrial demand for ecosystem services. AIChE J 62:93319–33
    [Google Scholar]
  97. 97. 
    Gopalakrishnan V, Bakshi BR. 2018. Ecosystems as unit operations for local techno-ecological synergy: integrated process design with treatment wetlands. AIChE J 64:72390–407
    [Google Scholar]
  98. 98. 
    König A, Ulonska K, Mitsos A, Viell J 2019. Optimal applications and combinations of renewable fuel production from biomass and electricity. Energy Fuels 33:21659–72
    [Google Scholar]
  99. 99. 
    Voll A, Marquardt W. 2012. Reaction network flux analysis: optimization‐based evaluation of reaction pathways for biorenewables processing. AIChE J 58:61788–801
    [Google Scholar]
  100. 100. 
    Ulonska K, Skiborowski M, Mitsos A, Viell J 2016. Early‐stage evaluation of biorefinery processing pathways using process network flux analysis. AIChE J 62:93096–108
    [Google Scholar]
  101. 101. 
    Balakrishnan M, Sacia ER, Sreekumar S, Gunbas G, Gokhale AA et al. 2015. Novel pathways for fuels and lubricants from biomass optimized using life-cycle greenhouse gas assessment. PNAS 112:257645–49
    [Google Scholar]
  102. 102. 
    Dahmen M, Marquardt W. 2017. Model-based formulation of biofuel blends by simultaneous product and pathway design. Energy Fuels 31:44096–121
    [Google Scholar]
  103. 103. 
    Caldeira C, Freire F, Olivetti EA, Kirchain R, Dias LC 2019. Analysis of cost-environmental trade-offs in biodiesel production incorporating waste feedstocks: a multi-objective programming approach. J. Clean. Prod. 216:64–73
    [Google Scholar]
  104. 104. 
    Gerber L, Gassner M, Maréchal F 2011. Systematic integration of LCA in process systems design: application to combined fuel and electricity production from lignocellulosic biomass. Comput. Chem. Eng. 35:71265–80
    [Google Scholar]
  105. 105. 
    Tock L, Maréchal F, Perrenoud M 2015. Thermo-environomic evaluation of the ammonia production. Can. J. Chem. Eng. 93:2356–62
    [Google Scholar]
  106. 106. 
    Tock L, Maréchal F. 2015. Thermo-environomic optimisation strategy for fuel decarbonisation process design and analysis. Comput. Chem. Eng. 83:110–20
    [Google Scholar]
  107. 107. 
    Nguyen T-V, Tock L, Breuhaus P, Maréchal F, Elmegaard B 2016. CO2-mitigation options for the offshore oil and gas sector. Appl. Energy 161:673–94
    [Google Scholar]
  108. 108. 
    Pavão LV, Costa CBB, Ravagnani M, Jiménez L 2017. Costs and environmental impacts multi-objective heat exchanger networks synthesis using a meta-heuristic approach. Appl. Energy 203:304–20
    [Google Scholar]
  109. 109. 
    Zhang L, Babi DK, Gani R 2016. New vistas in chemical product and process design. Annu. Rev. Chem. Biomol. Eng. 7:557–82
    [Google Scholar]
  110. 110. 
    Zhang L, Fung KY, Wibowo C, Gani R 2018. Advances in chemical product design. Rev. Chem. Eng. 34:3319–40
    [Google Scholar]
  111. 111. 
    Gani R, Ng KM. 2015. Product design—molecules, devices, functional products, and formulated products. Comput. Chem. Eng. 81:70–79
    [Google Scholar]
  112. 112. 
    Linke P, Papadopoulos A, Seferlis P 2015. Systematic methods for working fluid selection and the design, integration and control of organic rankine cycles—a review. Energies 8:64755–801
    [Google Scholar]
  113. 113. 
    Kümmerer K. 2007. Sustainable from the very beginning: rational design of molecules by life cycle engineering as an important approach for green pharmacy and green chemistry. Green Chem 9:8899–907
    [Google Scholar]
  114. 114. 
    Mehrkesh A, Karunanithi AT. 2014. New perspective on computer aided molecular design: a life cycle assessment approach. Proceedings of the 8th International Conference on Foundations of Computer-Aided Process Design - FOCAPD 2014 MR Eden, JD Siirola, GP Towler pp.369–74 Amsterdam: Elsevier
    [Google Scholar]
  115. 115. 
    Rosenbaum RK, Bachmann TM, Gold LS, Huijbregts MAJ, Jolliet O et al. 2008. USEtox—the UNEP-SETAC toxicity model: recommended characterisation factors for human toxicity and freshwater ecotoxicity in life cycle impact assessment. Int. J. Life Cycle Assess. 13:7532–46
    [Google Scholar]
  116. 116. 
    Schilling J, Tillmanns D, Lampe M, Hopp M, Gross J, Bardow A 2017. From molecules to dollars: integrating molecular design into thermo-economic process design using consistent thermodynamic modeling. Mol. Syst. Des. Eng. 2:3301–20
    [Google Scholar]
  117. 117. 
    Papadopoulos AI, Shavalieva G, Papadokonstantakis S, Seferlis P 2019. A framework for the integration of holistic sustainability assessment in computer-aided molecular design. Proceedings of the 29th European Symposium on Computer Aided Process Engineering AA Kiss, E Zondervan, R Lakerveld, L Özkan 13–18 Amsterdam: Elsevier
    [Google Scholar]
  118. 118. 
    von der Assen N, Lampe M, Müller L, Bardow A 2014. Life cycle assessment principles for the integrated product and process design of polymers from CO2. Proceedings of the 8th International Conference on Foundations of Computer-Aided Process Design - FOCAPD 2014 MR Eden, JD Siirola, GP Towler pp.1235–40 Amsterdam: Elsevier
    [Google Scholar]
  119. 119. 
    Calvo-Serrano R, Guillén-Gosálbez G. 2018. Streamlined life cycle assessment under uncertainty integrating a network of the petrochemical industry and optimization techniques: Ecoinvent versus mathematical modeling. ACS Sustain. Chem. Eng. 6:57109–18
    [Google Scholar]
  120. 120. 
    Hellweg S, Milà i Canals L 2014. Emerging approaches, challenges and opportunities in life cycle assessment. Science 344:61881109–13
    [Google Scholar]
  121. 121. 
    Blass V, Corbett CJ. 2018. Same supply chain, different models: integrating perspectives from life cycle assessment and supply chain management. J. Ind. Ecol. 22:118–30
    [Google Scholar]
  122. 122. 
    Guide VDR, Harrison TP, van Wassenhove LN 2003. The challenge of closed-loop supply chains. Interfaces 33:63–6
    [Google Scholar]
  123. 123. 
    Mentzer JT, DeWitt W, Keebler JS, Min S, Nix NW et al. 2001. Defining supply chain management. J. Bus. Logist. 22:21–25
    [Google Scholar]
  124. 124. 
    Mahbub N, Oyedun AO, Kumar A, Oestreich D, Arnold U, Sauer J 2017. A life cycle assessment of oxymethylene ether synthesis from biomass-derived syngas as a diesel additive. J. Clean. Prod. 165:1249–62
    [Google Scholar]
  125. 125. 
    von der Assen N, Sternberg A, Kätelhön A, Bardow A 2015. Environmental potential of carbon dioxide utilization in the polyurethane supply chain. Faraday Discuss 183:291–307
    [Google Scholar]
  126. 126. 
    Kätelhön A, Meys R, Deutz S, Suh S, Bardow A 2019. Climate change mitigation potential of carbon capture and utilization in the chemical industry. PNAS 116:2311187–94
    [Google Scholar]
  127. 127. 
    You F, Tao L, Graziano DJ, Snyder SW 2012. Optimal design of sustainable cellulosic biofuel supply chains: multiobjective optimization coupled with life cycle assessment and input‐output analysis. AIChE J 58:41157–80
    [Google Scholar]
  128. 128. 
    Yang M, Tian X, You F 2018. Manufacturing ethylene from wet shale gas and biomass: comparative technoeconomic analysis and environmental life cycle assessment. Ind. Eng. Chem. Res. 57:175980–98
    [Google Scholar]
  129. 129. 
    Stock JR. 1992. Reverse logistics White pap., Counc. Logist. Manag Lombard, IL:
  130. 130. 
    Gu F, Guo J, Zhang W, Summers PA, Hall P 2017. From waste plastics to industrial raw materials: a life cycle assessment of mechanical plastic recycling practice based on a real-world case study. Sci. Total Environ. 601–2:1192–207
    [Google Scholar]
  131. 131. 
    Laurent A, Bakas I, Clavreul J, Bernstad A, Niero M et al. 2014. Review of LCA studies of solid waste management systems—part I: lessons learned and perspectives. Waste Manag 34:3573–88
    [Google Scholar]
  132. 132. 
    Laurent A, Clavreul J, Bernstad A, Bakas I, Niero M et al. 2014. Review of LCA studies of solid waste management systems—part II: methodological guidance for a better practice. Waste Manag 34:3589–606
    [Google Scholar]
  133. 133. 
    Guide VDR, van Wassenhove LN 2009. OR FORUM—the evolution of closed-loop supply chain research. Oper. Res. 57:110–18
    [Google Scholar]
  134. 134. 
    Govindan K, Soleimani H, Kannan D 2015. Reverse logistics and closed-loop supply chain: a comprehensive review to explore the future. Eur. J. Oper. Res. 240:3603–26
    [Google Scholar]
  135. 135. 
    Hanes RJ, Bakshi BR. 2015. Process to planet: a multiscale modeling framework toward sustainable engineering. AIChE J 61:103332–52
    [Google Scholar]
  136. 136. 
    Barbosa-Póvoa AP, da Silva C, Carvalho A 2018. Opportunities and challenges in sustainable supply chain: an operations research perspective. Eur. J. Oper. Res. 268:2399–431
    [Google Scholar]
  137. 137. 
    Barbosa-Póvoa AP, Mota B, Carvalho A 2018. How to design and plan sustainable supply chains through optimization models?. Pesqui. Oper. 38:3363–88
    [Google Scholar]
  138. 138. 
    Mota B, Gomes MI, Carvalho A, Barbosa-Póvoa AP 2015. Towards supply chain sustainability: economic, environmental and social design and planning. J. Clean. Prod. 105:14–27
    [Google Scholar]
  139. 139. 
    Yue D, Pandya S, You F 2016. Integrating hybrid life cycle assessment with multiobjective optimization: a modeling framework. Environ. Sci. Technol. 50:31501–9
    [Google Scholar]
  140. 140. 
    Suh S, Huppes G. 2005. Methods for life cycle inventory of a product. J. Clean. Prod. 13:7687–97
    [Google Scholar]
  141. 141. 
    Mota B, Carvalho A, Gomes MI, Barbosa-Póvoa AP 2019. Business strategy for sustainable development: impact of life cycle inventory and life cycle impact assessment steps in supply chain design and planning. Bus. Strategy Environ. 29: https://doi.org/10.1002/bse.2352
    [Crossref] [Google Scholar]
  142. 142. 
    Mota B, Gomes MI, Carvalho A, Barbosa-Póvoa AP 2018. Sustainable supply chains: an integrated modeling approach under uncertainty. Omega 77:32–57
    [Google Scholar]
  143. 143. 
    Ekvall T, Weidema BP. 2004. System boundaries and input data in consequential life cycle inventory analysis. Int. J. Life Cycle Assess. 9:3161–71
    [Google Scholar]
  144. 144. 
    Earles JM, Halog A. 2011. Consequential life cycle assessment: a review. Int. J. Life Cycle Assess. 16:5445–53
    [Google Scholar]
  145. 145. 
    Zamagni A, Guinée J, Heijungs R, Masoni P, Raggi A 2012. Lights and shadows in consequential LCA. Int. J. Life Cycle Assess. 17:7904–18
    [Google Scholar]
  146. 146. 
    Palazzo J, Geyer R, Suh S 2020. A review of methods for characterizing the environmental consequences of actions in life cycle assessment. J. Ind. Ecol In press. https://doi.org/10.1111/jiec.12983
    [Crossref] [Google Scholar]
  147. 147. 
    Weidema BP, Frees N, Nielsen A-M 1999. Marginal production technologies for life cycle inventories. Int. J. Life Cycle Assess. 4:148–56
    [Google Scholar]
  148. 148. 
    Fraunhofer Inst. Solar Energy 2019. Monthly electricity generation in Germany in 2020 Energy Charts, Fraunhofer Inst. Solar Energy Frieburg, Ger: https://www.energy-charts.de/energy.htm
  149. 149. 
    Ger. Fed. Gov 2012. Progress Report 2012, Climate and Energy Berlin: Ger. Fed. Gov https://www.bundesregierung.de/
  150. 150. 
    Marshall A. 1890. The principles of economics Pap., Arch. Hist. Econ. Thought, McMaster Univ Hamilton, Ont: https://econpapers.repec.org/bookchap/hayhetboo/marshall1890.htm
  151. 151. 
    Spitz PH. 2003. The Chemical Industry at the Millennium. Maturity, Restructuring, and Globalization. Philadelphia: Chem. Heritage Press
  152. 152. 
    Yang Y, Heijungs R. 2018. On the use of different models for consequential life cycle assessment. Int. J. Life Cycle Assess. 23:4751–58
    [Google Scholar]
  153. 153. 
    Thonemann N, Pizzol M. 2019. Consequential life cycle assessment of carbon capture and utilization technologies within the chemical industry. Energy Environ. Sci. 12:72253–63
    [Google Scholar]
  154. 154. 
    Mathiesen BV, Münster M, Fruergaard T 2009. Uncertainties related to the identification of the marginal energy technology in consequential life cycle assessments. J. Clean. Prod. 17:151331–38
    [Google Scholar]
  155. 155. 
    Kätelhön A, Bardow A, Suh S 2016. Stochastic technology choice model for consequential life cycle assessment. Environ. Sci. Technol. 50:2312575–83
    [Google Scholar]
  156. 156. 
    Larrea‐Gallegos G, Vázquez‐Rowe I, Wiener H, Kahhat R 2019. Applying the technology choice model in consequential life cycle assessment: a case study in the Peruvian agricultural sector. J. Ind. Ecol. 23:3601–14
    [Google Scholar]
  157. 157. 
    Budzinski M, Sisca M, Thrän D 2019. Consequential LCA and LCC using linear programming: an illustrative example of biorefineries. Int. J. Life Cycle Assess. 24:122191–205
    [Google Scholar]
  158. 158. 
    Bouman M, Heijungs R, van der Voet E, van den Bergh JCJM, Huppes G 2000. Material flows and economic models: an analytical comparison of SFA, LCA and partial equilibrium models. Ecol. Econ. 32:2195–216
    [Google Scholar]
  159. 159. 
    Adams D, Alig R, McCarl BA, Murray BC 2005. FASOMGHG conceptual structure, and specification: documentation Pap., Tex. A&M, College Station, TX. https://agecon2.tamu.edu/people/faculty/mccarl-bruce/papers/1212FASOMGHG_doc.pdf
  160. 160. 
    Kløverpris J, Wenzel H, Nielsen PH 2008. Life cycle inventory modelling of land use induced by crop consumption. Int. J. Life Cycle Assess. 13:113–21
    [Google Scholar]
  161. 161. 
    Hedal Kløverpris J, Baltzer K, Nielsen PH 2010. Life cycle inventory modelling of land use induced by crop consumption. Int. J. Life Cycle Assess. 15:190–103
    [Google Scholar]
  162. 162. 
    Voll A, Sorda G, Optehostert F, Madlener R, Marquardt W 2012. Integration of market dynamics into the design of biofuel processes.. Proceedings of the 11th International Symposium on Process Systems Engineering IA Karimi, R Srinivasan pp.850–54 Amsterdam: Elsevier
    [Google Scholar]
  163. 163. 
    Gong J, You F. 2017. Consequential life cycle optimization: general conceptual framework and application to algal renewable diesel production. ACS Sustain. Chem. Eng. 5:75887–911
    [Google Scholar]
  164. 164. 
    Suh S, Yang Y. 2014. On the uncanny capabilities of consequential LCA. Int. J. Life Cycle Assess. 19:61179–84
    [Google Scholar]
  165. 165. 
    Bonabeau E. 2002. Agent-based modeling: methods and techniques for simulating human systems. PNAS 99:Suppl. 37280–87
    [Google Scholar]
  166. 166. 
    Singh A, Chu Y, You F 2014. Biorefinery supply chain network design under competitive feedstock markets: an agent-based simulation and optimization approach. Ind. Eng. Chem. Res. 53:3915111–26
    [Google Scholar]
  167. 167. 
    Bichraoui-Draper N, Xu M, Miller SA, Guillaume B 2015. Agent-based life cycle assessment for switchgrass-based bioenergy systems. Resour. Conserv. Recycl. 103:171–78
    [Google Scholar]
  168. 168. 
    Eur. Comm.-Joint Res. Cent., Inst. Environ. Sustain 2010. International Reference Life Cycle Data System (ILCD) Handbook—General Guide for Life Cycle Assessment—Detailed Guidance EUR 24708 EN Luxembourg: Publ. Off. Eur. Union. , 1st ed..
  169. 169. 
    Sugiyama H, Fischer U, Hungerbühler K 2008. Decision framework for chemical process design including different stages of environmental, health, and safety assessment. AICHE J 54:41037–53
    [Google Scholar]
  170. 170. 
    Patel AD, Meesters K, den Uil H, de Jong E, Blok K, Patel MK 2012. Sustainability assessment of novel chemical processes at early stage: application to bio-based processes. Energy Environ. Sci. 5:8430–44
    [Google Scholar]
  171. 171. 
    Goedkoop M, Heijungs R, Huijbregts M, De Schryver A, Struijs J, Van Zelm R 2009. ReCiPe 2008: a life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level Rep., Minist. Volkshuisv. Ruimt. Ordening Milieu, Neth .
  172. 172. 
    Huijbregts MA, Norris G, Bretz R, Ciroth A, Maurice B et al. 2001. Framework for modelling data uncertainty in life cycle inventories. Int. J. Life Cycle Assess. 6:3127–32
    [Google Scholar]
  173. 173. 
    Huijbregts MAJ. 1998. A general framework for the analysis of uncertainty and variability in life cycle assessment. Int. J. Life Cycle Assess. 3:5273–80
    [Google Scholar]
  174. 174. 
    Björklund AE. 2002. Survey of approaches to improve reliability in LCA. Int. J. Life Cycle Assess. 7:264
    [Google Scholar]
  175. 175. 
    Lloyd SM, Ries R. 2007. Characterizing, propagating, and analyzing uncertainty in life‐cycle assessment: a survey of quantitative approaches. J. Ind. Ecol. 11:1161–79
    [Google Scholar]
  176. 176. 
    Groen EA, Heijungs R, Bokkers EA, De Boer IJ 2014. Methods for uncertainty propagation in life cycle assessment. Environ. Model. Softw. 62:316–25
    [Google Scholar]
  177. 177. 
    Heijungs R. 2010. Sensitivity coefficients for matrix-based LCA. Int. J. Life Cycle Assess. 15:5511–20
    [Google Scholar]
  178. 178. 
    Jung J, von der Assen N, Bardow A 2014. Sensitivity coefficient-based uncertainty analysis for multi-functionality in LCA. Int. J. Life Cycle Assess. 19:3661–76
    [Google Scholar]
  179. 179. 
    Sonnemann GW, Schuhmacher M, Castells F 2003. Uncertainty assessment by a Monte Carlo simulation in a life cycle inventory of electricity produced by a waste incinerator. J. Clean. Prod. 11:3279–92
    [Google Scholar]
  180. 180. 
    Heijungs R, Lenzen M. 2014. Error propagation methods for LCA—a comparison. Int. J. Life Cycle Assess. 19:71445–61
    [Google Scholar]
  181. 181. 
    Groen EA, Bokkers EA, Heijungs R, de Boer IJ 2017. Methods for global sensitivity analysis in life cycle assessment. Int. J. Life Cycle Assess. 22:71125–37
    [Google Scholar]
  182. 182. 
    Lacirignola M, Blanc P, Girard R, Perez-Lopez P, Blanc I 2017. LCA of emerging technologies: addressing high uncertainty on inputs' variability when performing global sensitivity analysis. Sci. Total Environ. 578:268–80
    [Google Scholar]
  183. 183. 
    Stadler K, Wood R, Bulavskaya T, Södersten CJ, Simas M et al. 2018. EXIOBASE 3: developing a time series of detailed environmentally extended multi‐regional input‐output tables. J. Ind. Ecol. 22:3502–15
    [Google Scholar]
  184. 184. 
    Perkins J, Suh S. 2019. Uncertainty implications of hybrid approach in LCA: precision versus accuracy. Environ. Sci. Technol. 53:73681–88
    [Google Scholar]
  185. 185. 
    PlasticsEurope 2014. Eco-profiles Resour., PlasticsEurope. https://www.plasticseurope.org/en/resources/eco-profiles
  186. 186. 
    Wei W, Larrey-Lassalle P, Faure T, Dumoulin N, Roux P, Mathias JD 2016. Using the reliability theory for assessing the decision confidence probability for comparative life cycle assessments. Environ. Sci. Technol. 50:52272–80
    [Google Scholar]
  187. 187. 
    Beltran AM, Prado V, Font Vivanco D, Henriksson PJ, Guinée JB, Heijungs R 2018. Quantified uncertainties in comparative life cycle assessment: What can be concluded. ? Environ. Sci. Technol. 52:42152–61
    [Google Scholar]
  188. 188. 
    Guillén‐Gosálbez G, Grossmann IE. 2009. Optimal design and planning of sustainable chemical supply chains under uncertainty. AIChE J 55:199–121
    [Google Scholar]
  189. 189. 
    Gavankar S, Anderson S, Keller AA 2015. Critical components of uncertainty communication in life cycle assessments of emerging technologies: nanotechnology as a case study. J. Ind. Ecol. 19:3468–79
    [Google Scholar]
  190. 190. 
    Igos E, Benetto E, Meyer R, Baustert P, Othoniel B 2019. How to treat uncertainties in life cycle assessment studies?. Int. J. Life Cycle Assess. 24:4794–807
    [Google Scholar]

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error