Decision tree approach based on food waste valorisation pathways: A case study on moisture content level of spent coffee grounds in Singapore

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Decision tree approach based on food waste valorisation pathways: A case study on moisture content level of spent coffee grounds in Singapore
Title:
Decision tree approach based on food waste valorisation pathways: A case study on moisture content level of spent coffee grounds in Singapore
Journal Title:
Procedia CIRP
Publication Date:
07 May 2024
Citation:
Lee, Z. J., Chung, S. Y., Lee, A. W. L., & Tan, Y. S. (2024). Decision tree approach based on food waste valorisation pathways: A case study on moisture content level of spent coffee grounds in Singapore. Procedia CIRP, 122, 253–258. https://doi.org/10.1016/j.procir.2024.01.036
Abstract:
Waste reduction is a perennial challenge for cities like Singapore due to space constraints. In 2022, Singapore generated 813,000 tonnes of food waste with a mere 18% of the waste being recycled and the remaining 82% being incinerated. To reduce food waste, food waste valorisation is being explored. By adopting innovative technologies and sustainable practices, valorisation aims to extract maximum value from food waste that would otherwise end up in incineration or pollute the environment. However, choosing an inappropriate valorisation pathway can compromise its effectiveness which potentially leads to increased environmental harm and rendering the process unsustainable. The goal of this work is to help food producers and food waste collectors identify the suitable valorisation pathway for their food waste through a decision tree approach. A case study is presented where the proposed decision tree is applied to guide users in selecting a suitable valorisation pathway based on the moisture content level for one of the city’s major food waste – spent coffee ground (SCG). Validation is conducted using life cycle assessment (LCA) to study the environmental impacts of the different valorisation pathways.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
There was no specific funding for the research done
Description:
ISSN:
2212-8271