Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems

Watson, Nicholas J; Fisher, Oliver J; Escrig, Josep E; Witt, Rob; Porcu, Laura; Bacon, Darren; Rigley, Martin; Gomes, Rachel L

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.

Citation

Watson, N. J., Fisher, O. J., Escrig, J. E., Witt, R., Porcu, L., Bacon, D., Rigley, M., & Gomes, R. L. (2020). Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems. Computers and Chemical Engineering, 140, Article 106881. https://doi.org/10.1016/j.compchemeng.2020.106881

• Journal Article Type: Article
• Acceptance Date: Apr 20, 2020
• Online Publication Date: May 14, 2020
• Publication Date: Sep 2, 2020
• Deposit Date: May 19, 2020
• Publicly Available Date: May 12, 2022
• Journal: Computers and Chemical Engineering
• Print ISSN: 0098-1354
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 140
• Article Number: 106881
• DOI: https://doi.org/10.1016/j.compchemeng.2020.106881
• Keywords: General Chemical Engineering; Computer Science Applications
• Public URL: https://nottingham-repository.worktribe.com/output/4469329
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0098135419308373