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A data driven approach to reducing household food waste

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journal contribution
posted on 24.11.2021, 18:24 by Elliot WoolleyElliot Woolley, Zhengfan Luo, Aicha JellilAicha Jellil, Alessandro Simeone
Of all food wastes, that which is produced from the household is the most damaging in terms of environmental and economic impact. Many efforts have been made to quantify and analyse the reasons for and problems associated with household food waste generation which has led to the development of both technical solutions and behavioural interventions (including education and awareness) to try and reduce its generation. In this work a novel solution is proposed and developed which connects food providers and consumers, enabling more intelligent food planning, purchasing and consumption. A data driven Recipe Suggestion tool, supported by a Particle Swarm Optimisation (PSO) engine, is described for the first time. Recipes and associated ingredients are suggested for users which consider their preferences, remaining food items already held at home, expiry dates and minimum pack sizes. The tool is applied to a simulated case study to demonstrate its applicability and potential to generate a range of useful waste metrics. Results of the application of the tool, in terms of optimization capabilities and computation time, show encouraging potential for platform integration. The suitability of the tool to be incorporated into modern e-commerce systems is discussed.

Funding

EPSRC Centre for Innovative Manufacturing in Food

Engineering and Physical Sciences Research Council

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History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Sustainable Production and Consumption

Volume

29

Pages

600-613

Publisher

Elsevier B.V. on behalf of Institution of Chemical Engineers

Version

VoR (Version of Record)

Rights holder

@ The Author(s)

Publisher statement

This paper was accepted for publication in the journal Sustainable Production and Consumption and the definitive published version is available at

Acceptance date

05/11/2021

Publication date

2021-11-12

Copyright date

2021

eISSN

2352-5509

Language

en

Depositor

Dr Elliot Woolley. Deposit date: 8 November 2021