Skip to main navigation Skip to search Skip to main content

Computational modeling of material flow networks

    Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

    1 Citation (Scopus)

    Abstract

    Material Flow Networks (MFN) are a modeling instrument in the field of material flow analysis (MFA) that helps to characterize current or future material and energy flows and stocks. Often, efficiency analyses such as life cycle assessments and cost accounting use such models to calculate the relationship between positive outcomes and negative impacts. The systematic integration of stocks makes it possible to analyze infrastructures that provide services, which is necessary in order to assess the effects of replacing material goods by services. The first part of this paper outlines Material Flow Networks as a period-oriented accounting system that systematically integrates stock and flow accounting. The second part is concerned with the question of how to construct the models and calculate data. From this perspective, the instrument becomes a modeling framework.

    Original languageEnglish
    Title of host publicationICT Innovations for Sustainability
    EditorsLorenz M. Hilty, Bernard Aebischer
    Number of pages11
    Volume310
    PublisherSpringer International Publishing
    Publication date2015
    Pages301-311
    ISBN (Print)978-3-319-09227-0
    ISBN (Electronic)978-3-319-09228-7
    DOIs
    Publication statusPublished - 2015

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 7 - Affordable and Clean Energy
      SDG 7 Affordable and Clean Energy

    Research areas and keywords

    • Accounting
    • Dynamic material flow analysis
    • M Steady-state modeling
    • Material flow network
    • Process flowsheeting
    • Sustainability sciences, Communication

    Fingerprint

    Dive into the research topics of 'Computational modeling of material flow networks'. Together they form a unique fingerprint.

    Cite this