Jagtap, S., Garcia-Garcia, G. and Rahimifard, S. (2021) Optimisation of the resource efficiency of food manufacturing via the Internet of Things. Computers in Industry, 127. ISSN 0166-3615
Abstract
The food sector is currently very inefficient due to a large amount of food waste it generates, and the volumes of water and energy used. This problem is aggravated by increasing economic costs and stricter regulations associated with the disposal and treatment of food waste, carbon emissions and wastewater discharge. Because of this, resource efficiency is key to a sustainable food system. In this context, it is essential to reduce food waste, energy and water through transparent and accurate real-time monitoring to be able to understand the real reasons behind their generation/use. Understanding these reasons would help food manufacturers to redesign their processes and achieve operational improvements. The Internet of Things (IoT), a relatively new manufacturing concept within Industry 4.0, can support this. IoT consists of an information technology infrastructure for data collection and distribution, that can significantly influence the efficiency and performance of manufacturing systems. This article presents an IoT-based framework for monitoring the generation of food waste and the use of energy and water in the food sector. The framework supports the identification of improvements to optimise the resource efficiency of food manufacturing through the design and implementation of a number of IoT-based tools.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier. This is an author produced version of a paper subsequently published in Computers in Industry. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Food manufacturing; Food supply chains; Internet of Things; Industry 4.0; Sustainability; Resource efficiency |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Chemical and Biological Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Science Research Council EP/K030957/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Feb 2021 13:11 |
Last Modified: | 05 Feb 2023 01:13 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.compind.2021.103397 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:171251 |
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