Digital Twins for the Circular Economy

Preut, Anna; Kopka, Jan-Philip; Clausen, Uwe

doi:10.3390/su131810467

Cited by 53 publications

(41 citation statements)

References 32 publications

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“…However, the fusion of measurement, prediction, and decision-support tools is still in its infancy. Further case-specific implementations must be reported to establish priorities and standards (Preut et al, 2021).…”

Section: Future Trendsmentioning

confidence: 99%

A primer on predictive techniques for food and bioresources transformation processes

Sicard

Barbe²,

Boutrou

et al. 2023

J Food Process Engineering

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To meet current societal demand for more sustainable transformation processes and bioresources, these processes must be optimized and new ones developed. The evolution of various systems (raw material, food, or process attributes) can be predicted to optimize the uses of biomass for better quality, safety, economic benefit, and sustainability. Predictive modeling can guide the necessary changes and influence industrials, governmental policies and consumers decision-making. However, achieving good predictive capability requires reflection on the models and model validation, which can be difficult. This review aims to help scientists begin to predict by presenting the techniques currently used in predictive science for food and related bioproducts. First, a guideline helps readers initiate a prediction process along with final tips and a warning about the risks involved, with a particular focus on the crucial validation step. Three broad categories of techniques are then presented: empirical, mechanistic, and artificial intelligence (or "data-driven"). For each category, the advantages and limitations of current techniques for prediction are explained in light of their current domains of applications, illustrated with literature studies and a detailed example. Thus this article provides engineering researchers information about predictive modeling which is a recent relevant development in optimization of both food and nonfood bioresources processes. Practical applicationsPredictive modeling is a recent development of much relevance in the optimization of both food and nonfood bioresources processes. The goal of this article is to guide those in research or industry who would like to start predicting. Therefore, the article is intended as a primer on prediction concepts and predictive techniques for food and non-food bioresources processing. Three categories of techniques commonly used in these fields are illustrated by various examples of current applications and a more detailed example helps to understand the implementation process. An increased ability of the global scientific body to predict the outcome of various decisions, often linked or sequential, will open new avenues for designing food products with circularity in mind: maintaining value and not creating waste in the process.

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Section: Future Trendsmentioning

confidence: 99%

A primer on predictive techniques for food and bioresources transformation processes

Sicard

Barbe²,

Boutrou

et al. 2023

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

“…One prerequisite for CE is to enable all stakeholders within the product life cycle to make qualified decisions based on data related to the product or asset at any stage. The advancing digitalization of the supply chain and the concept of DT can help to untie the complexity of this task [11].…”

Section: How Do Digital Twins Foster Circular Economy?mentioning

confidence: 99%

“…CE considerations start already at the design, development and production phases before bringing the product into the market [9,11,12].…”

Section: How Do Digital Twins Foster Circular Economy?mentioning

confidence: 99%

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Digital Twins for Circular Economy - Enabling Decision Support for R-Strategies

Mügge¹,

Hahn²,

Riedelsheimer³

et al. 2022

I40M

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Als Teil des digitalen Datenökosystems bieten Digitale Zwillinge (DT) für die Kreislaufwirtschaft (CE) einen vielversprechenden Ansatz für eine nachhaltigere Wertschöpfung. Durch die Analyse und Aufbereitung von produkt-, bauteil- und materialspezifischen Daten entlang des Lebenszyklus ist es möglich, aktuelle Herausforderungen wie Klimawandel und Ressourcenknappheit zu adressieren. Im deutschen Forschungsprojekt Catena-X werden auf Basis dieser unternehmensübergreifend ausgetauschten Daten und Informationen konkrete Lösungen entwickelt. In diesem Rahmen wird der „R-Strategie Assistent" vorgestellt. Dabei handelt es sich um eine Anwendung, die auf Basis von DT-Daten die beste CE-Strategie am Ende des Lebenszyklus eines Fahrzeugs ermittelt.

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“…By enhancing transparency, companies are able to de-risk their supply chain, improve critical suppliers or trigger re-design projects to reduce the social and environmental impact of their products and services. [11] There are estimates that virtual twins can help save up to 7.5 Gt of CO2 emissions by 2030. [12]…”

Section: Supply Chain Digital Twins and Circular Economy Digital Twinsmentioning

confidence: 99%

Preparing for the new Supply Chain Law – An introductory paper discussing the Opportunities and Boundaries of a digitalized Supply Chain

et al. 2022

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Sustainability aims at a sound synergy of the economic, ecological and social dimension. To promote sustainable development and guarantee a high living-standard for future generations, all three elements must be balanced. The economy impacts the other two dimensions along the value chain. Particularly critical is the impact of the supply chain as human working conditions and environmental standards must be secured for procurement of materials along complex transportation routes. To tackle this issue, France has passed a Supply Chain Law (Loi de vigilance) in 2017, Britain has passed the Modern Slavery Act which prohibits child labor in 2015 and Germany has passed a Supply Chain Act (Lieferkettengesetz) in June 2021. Furthermore, a EU-wide supply chain law on due diligence and environmental standards for supply chains is expected for 2022. A transparent digitalized version of a company´s supply chain, including its human resources and environmental impact, would facilitate the identification of several sustainability intervention points. Such transparency could lead to not only better and supply chain law-compliant regulations for fairer working conditions and improved social sustainability, but also to optimized circular economy processes. However, there is also the risk that a high level oftransparency in complex supply chains entails very high bureaucratic efforts. With this conceptual outline, we would like to contribute to the discussion of the new German legislation and present a variety of IT solutions to improve and monitor the sustainability of supply chains as well as to reduce the bureaucratic effort which comes with supply chain supervision.

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Digital Twins for the Circular Economy

Cited by 53 publications

References 32 publications

A primer on predictive techniques for food and bioresources transformation processes

A primer on predictive techniques for food and bioresources transformation processes

Digital Twins for Circular Economy - Enabling Decision Support for R-Strategies

Preparing for the new Supply Chain Law – An introductory paper discussing the Opportunities and Boundaries of a digitalized Supply Chain

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