AlAlaween, W.H., Mahfouf, M. orcid.org/0000-0002-7349-5396 and Salman, A.D. (2021) When swarm meets fuzzy logic: Batch optimisation for the production of pharmaceuticals. Powder Technology, 379. pp. 174-183. ISSN 0032-5910
Abstract
The concept of right-first-time production is an essential feature for a successful product development process and for companies to be competitive and profitable. However, achieving such a concept is a tricky exercise across a wide spectrum of industrial domains includes the pharmaceutical industry where granulation and tableting processes are considered to be the most critical operations in the production line. Therefore, this research paper presents a new approach that integrates a particle swarm optimization algorithm with a fuzzy logic system in order to implement a new framework by which right-first-time production of granules and tablets is ascertained systematically. The proposed approach consists of inverting the models that were previously developed. Through this control technique, one can identify the optimal operating conditions to produce the required granules and tablets, and can minimize the waste and recycling ratios. All frameworks have been successfully validated via real laboratory-scale experiments that include measurement tolerances.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2020 Elsevier B.V. This is an author produced version of a paper subsequently published in Powder Technology. 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: | Fusion model; Fuzzy logic system; Hybrid model; Incorporated model; Right-first-time production |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Nov 2020 11:53 |
Last Modified: | 09 Feb 2022 07:37 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.powtec.2020.10.066 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167833 |