Clean Technologies and Environmental Policy

, Volume 20, Issue 7, pp 1697–1719 | Cite as

Optimization of multi-pathway production chains and multi-criteria decision-making through sustainability evaluation: a biojet fuel production case study

  • Eduardo VyhmeisterEmail author
  • Gerardo J. Ruiz-Mercado
  • Ana I. Torres
  • John A. Posada
Original Paper


Selection of optimal technologies for novel biobased products and processes is a major challenge in process design, especially when are considered many alternatives available to transform materials into valuable products. Furthermore, such technological alternatives vary in their technical performances and cause different levels of economic and environmental impacts throughout their life cycles. Additionally, selection of optimal production pathways requires a shift from the traditional materials management practices to more sustainable practices. This contribution provides a method for optimizing multi-product network systems from a sustainability perspective by applying the GREENSCOPE framework as a sustainable objective function. A case study is presented in which the four GREENSCOPE target areas (i.e., efficiency, energy, economics, and environment) are evaluated by 21 preselected indicators as part of a multi-objective optimization problem of a biojet fuel production network. The biojet fuel production network evaluated in this study consists of four main elements: (1) feedstocks management, (2) conversion technologies, (3) co-products upgrading, and (4) auxiliary sections for in situ production of raw materials and utilities. For the sustainability objective function, the 21 indicators are analyzed considering multiple perspectives of stakeholders to study their influence on the decision-making process. It is, different sets of weighting factors are assigned to each of the four target areas. Hence, this sustainability evaluation from different stakeholders’ perspectives allows identifying optimal networks, specific target areas with great potential for improvements, and processing steps with great influence in the entire network performance. As a result, diverse optimal network arrangements were obtained according to the multiple stakeholders’ perspectives. This evidences that a win–win situation for all sustainability aspects considered can hardly be reached. Finally, this contribution demonstrated the applicability of the proposed methodology for sustainability evaluation, optimization, and decision-making in the context of a multi-product material facility by developing a multi-objective optimization model.


Biojet fuel biorefinery Multi-criteria decision-making Multi-objective optimization Multi-stakeholder analysis Sustainability assessment Materials management 

List of symbols



Multi-criteria decision-making


Life cycle assessment


Life cycle costing


Greenhouse gas


Global warming potential


Gauging reaction effectiveness for the environmental sustainability of chemistries with a multi-objective process evaluator


GREENSCOPE perspectives (efficiency, energy, environmental, and economics)


Total production cost


EPA’s toxic release inventory



Reaction mass efficiency


Mass intensity


Effective mass yield


Carbon efficiency


Renewability-material Index


Fractional water consumption


Health hazard, irritation factor

HHchronic toxicity

Health hazard, chronic toxicity factor

SHacute tox.

Safety hazard, acute toxicity


Specific toxic release


Global warming potential

WPO2 dem.

Aquatic oxygen demand potential

ms, spec.

Specific solid waste mass

Vl, spec.

Specific liquid waste volume


Specific energy intensity


Renewability energy index


Renewability-exergy index


Discounted payback period


Turnover ratio


Specific raw material cost

CE, spec.

Specific energy cost



Steam methane reforming process


Hydrothermal liquefaction process


Gasification followed by Fischer–Tropsch process



Stakeholder GREENSCOPE perspective weight (i = 1, 2, 3, 4; i.e., efficiency, energy, environmental, and economic)


Relative importance of a j index within the same GREENSCOPE perspective i


ith GREENSCOPE indicator of the efficiency perspective


ith GREENSCOPE indicator of the energy perspective


ith GREENSCOPE indicator of the environmental perspective


ith GREENSCOPE indicator of the economic perspective


ith node


Arc connecting nodes I and j. Starting in i and ending in j

\(N_{{{\text{inlet}} .st - i}}\)

Number of Inlet streams in technology i


Technology matrix


Flowrate at the j position






Total number of technology nodes


Number of units (e.g., nST number of service technology nodes)

Type of nodes


ith service technology


ith reactant


ith intermediate node


ith market service technology


ith feedstock


jth technology of the ith category of grouped technologies


Nonexistence of the jth technology of the ith category of grouped technologies

Supplementary material

10098_2018_1576_MOESM1_ESM.docx (48 kb)
Supplementary material 1 (DOCX 48 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universidad Central de ChileSantiagoChile
  2. 2.National Risk Management Research LaboratoryU.S. Environmental Protection AgencyCincinnatiUSA
  3. 3.Instituto de Ingeniería Química, Facultad de IngenieríaUniversidad de la RepúblicaMontevideoUruguay
  4. 4.Department of BiotechnologyDelft University of TechnologyDelftThe Netherlands

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