CORE
CO
nnecting
RE
positories
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Research partnership
About
About
About us
Our mission
Team
Blog
FAQs
Contact us
Community governance
Governance
Advisory Board
Board of supporters
Research network
Innovations
Our research
Labs
Analysis of process parameters related to the single-screw extrusion of recycled polypropylene blends by using design of experiments
Authors
Barry Haworth
Birley AW
+6 more
Leno Mascia
Rauwendaal C
Tingrui Fu
Vera-Sorroche J
Wagner JR
White JL
Publication date
1 January 2017
Publisher
'SAGE Publications'
Doi
Abstract
This paper was accepted for publication in the journal Journal of Plastic Film and Sheeting and the definitive published version is available at http://dx.doi.org/10.1177/8756087916649006© SAGE Publications.The process dynamics of single-screw extrusion on mixtures of polypropylene (PP) and recycled PP were studied using a statistical, design of experiments (DoE) approach. For a conventional screw design, the barrel temperature, screw speed and two vastly different melt viscosity polypropylene mixtures were selected as the independent factors, whilst melt pressure, mass output, screw torque and temperature rise at the die due to shear heating were the dependent responses. A central composite design (CCD) in the framework of response surface methodology (RSM) was constructed, and an analysis of variance (ANOVA) was carried out to determine the significance of the response surface models. The resulting statistical and response surface predictions have demonstrated that the low viscosity component concentration in the blend is a dominating factor on melt pressure and screw torque, apart from the expected effect of screw speed on output. Viscous heating is affected only by screw speed and recycled polypropylene concentration. Furthermore, the predictions have identified a wider process operating window with increased low-viscosity component concentration. The data confirm that statistical tools make quantitative predictions for the effects of experimental process variables, in accordance with the expected qualitative trends towards process optimisation, providing scope towards its application in scaled-up industrial processes
Similar works
Full text
Available Versions
Loughborough University Institutional Repository
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:figshare.com:article/92349...
Last time updated on 26/03/2020
Crossref
See this paper in CORE
Go to the repository landing page
Download from data provider
info:doi/10.1177%2F87560879166...
Last time updated on 11/12/2019