Fisher, O.J., Watson, N.J. orcid.org/0000-0001-5216-4873, Escrig, J.E. et al. (5 more authors) (2020) Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems. Computers & Chemical Engineering, 140. 106881. ISSN 0098-1354
Abstract
The increasing availability of data, due to the adoption of low-cost industrial internet of things technologies, coupled with increasing processing power from cloud computing, is fuelling increase use of data-driven models in manufacturing. Utilising case studies from the food and drink industry and waste management industry, the considerations and challenges faced when developing data-driven models for manufacturing systems are explored. Ensuring a high-quality set of model development data that accurately represents the manufacturing system is key to the successful development of a data-driven model. The cross-industry standard process for data mining (CRISP-DM) framework is used to provide a reference at to what stage process manufacturers will face unique considerations and challenges when developing a data-driven model. This paper then explores how data-driven models can be utilised to characterise process streams and support the implementation of the circular economy principals, process resilience and waste valorisation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/) |
Keywords: | Data-driven models; Process resilience; Waste valorisation; Mathematical modelling; Machine learning; Industry 4.0 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Nutrition and Public Health (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Jul 2024 09:35 |
Last Modified: | 12 Jul 2024 09:35 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.compchemeng.2020.106881 |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214624 |