A systems engineering framework for the optimization of food supply chains under circular economy considerations.

Circular economy Coffee supply chain Multi-objective optimization Resource-Task-Network Superstructure optimization

Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
10 Nov 2021
Historique:
received: 11 02 2021
revised: 22 06 2021
accepted: 24 06 2021
pubmed: 31 7 2021
medline: 7 9 2021
entrez: 30 7 2021
Statut: ppublish

Résumé

The current linear "take-make-waste-extractive" model leads to the depletion of natural resources and environmental degradation. Circular Economy (CE) aims to address these impacts by building supply chains that are restorative, regenerative, and environmentally benign. This can be achieved through the re-utilization of products and materials, the extensive usage of renewable energy sources, and ultimately by closing any open material loops. Such a transition towards environmental, economic and social advancements requires analytical tools for quantitative evaluation of the alternative pathways. Here, we present a novel CE system engineering framework and decision-making tool for the modeling and optimization of food supply chains. First, the alternative pathways for the production of the desired product and the valorization of wastes and by-products are identified. Then, a Resource-Task-Network representation that captures all these pathways is utilized, based on which a mixed-integer linear programming model is developed. This approach allows the holistic modeling and optimization of the entire food supply chain, taking into account any of its special characteristics, potential constraints as well as different objectives. Considering that typically CE introduces multiple, often conflicting objectives, we deploy here a multi-objective optimization strategy for trade-off analysis. A representative case study for the supply chain of coffee is discussed, illustrating the steps and the applicability of the framework. Single and multi-objective optimization formulations under five different coffee-product demand scenarios are presented. The production of instant coffee as the only final product is shown to be the least energy and environmental efficient scenario. On the contrary, the production solely of whole beans sets a hypothetical upper bound on the optimal energy and environmental utilization. In both problems presented, the amount of energy generated is significant due to the utilization of waste generated for the production of excess energy.

Identifiants

pubmed: 34328124
pii: S0048-9697(21)03798-0
doi: 10.1016/j.scitotenv.2021.148726
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

148726

Informations de copyright

Copyright © 2021 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Stefanos G Baratsas (SG)

Artie McFerrin Department of Chemical Engineering, Texas A&M University, Jack E. Brown Chemical Engineering Building, 3122 TAMU, 100 Spence St., College Station, TX 77843, United States; Texas A&M Energy Institute, Texas A&M University, 1617 Research Pkwy, College Station, TX 77843, United States. Electronic address: sbaratsas@tamu.edu.

Efstratios N Pistikopoulos (EN)

Artie McFerrin Department of Chemical Engineering, Texas A&M University, Jack E. Brown Chemical Engineering Building, 3122 TAMU, 100 Spence St., College Station, TX 77843, United States; Texas A&M Energy Institute, Texas A&M University, 1617 Research Pkwy, College Station, TX 77843, United States. Electronic address: stratos@tamu.edu.

Styliani Avraamidou (S)

Texas A&M Energy Institute, Texas A&M University, 1617 Research Pkwy, College Station, TX 77843, United States. Electronic address: styliana@tamu.edu.

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Classifications MeSH