Ntamo, D., Papadopoulos, I., Omar, C. orcid.org/0000-0002-7839-608X et al. (2 more authors) (2025) A sustainability-oriented digital twin of the diamond pilot plant. Processes, 13 (1). 211. ISSN 2227-9717
Abstract
The pharmaceutical industry is undergoing a significant transition from batch to continuous manufacturing, driven by increasing regulatory requirements and sustainability pressures. Digital twins (DTs) play a pivotal role in facilitating this transition by enabling real-time data visualisation, process optimisation, and predictive analytics. While substantial progress has been made in the development and application of DTs, particularly in industries such as energy and automotive, there remains a critical need for further research and development focused on creating sustainability-oriented digital twins tailored to pharmaceutical processes. In the pharmaceutical sector, DTs are being progressively utilised not only for real-time monitoring and analysis but also as dynamic training platforms for engineers and operators, enhancing both operational efficiency and workforce competency. This paper examines the University of Sheffield’s Diamond Pilot Plant (DiPP), a facility showcasing the future of pharmaceutical manufacturing through the integration of Industry 4.0 technologies and advanced sensors. This paper focuses on developing a data-driven model to predict energy consumption in a twin-screw granulator (TSG) within the DiPP. The model, based on second-degree polynomial regression, demonstrates strong predictive accuracy with R-squared values exceeding 0.8. By optimising energy performance indicators, this work aims to improve the sustainability of pharmaceutical manufacturing processes. This research contributes to the field of pharmaceutical manufacturing by providing a foundation for creating energy models and advancing the development of comprehensive DT.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | twin-screw granulator; energy usage; continuous manufacturing; digital twin; machine learning; Industry 4.0; sustainability; data-driven models; mechanistic models |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Chemical, Materials and Biological Engineering The University of Sheffield > Faculty of Engineering (Sheffield) > Multidisciplinary Engineering Education (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Feb 2025 12:38 |
Last Modified: | 11 Feb 2025 12:38 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/pr13010211 |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:223139 |