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Prediction of major pressure losses pumping non-Newtonian foods and beverages – The role of uncertainty propagation for power law fluids

Håkansson, Andreas LU orcid ; Ekelund, Hanna LU orcid ; Isendahl, Hanna ; Viklund, Hannah ; Wijkander, Helena and Arlov, Dragana LU (2025) In Food and Bioproducts Processing 154. p.489-499
Abstract
To predict major pressure losses from pumping shear thinning liquid foods and beverage, rheometer-measured flow behaviour (n) and flow consistency (K)
indices are needed. These are always obtained with some measurement
uncertainty. This contribution develops a tool that allows for
quantitatively estimating how a given measurement uncertainty in the
rheological parameters translate to prediction uncertainty in major
pressure losses, and how much measurement uncertainty can be allowed
while ensuing that the prediction uncertainty is within a given
tolerance. Methodologically, the study combines the general uncertainty
management framework, Monte Carlo simulations, and an artificial... (More)
To predict major pressure losses from pumping shear thinning liquid foods and beverage, rheometer-measured flow behaviour (n) and flow consistency (K)
indices are needed. These are always obtained with some measurement
uncertainty. This contribution develops a tool that allows for
quantitatively estimating how a given measurement uncertainty in the
rheological parameters translate to prediction uncertainty in major
pressure losses, and how much measurement uncertainty can be allowed
while ensuing that the prediction uncertainty is within a given
tolerance. Methodologically, the study combines the general uncertainty
management framework, Monte Carlo simulations, and an artificial neural
network regression to predict uncertainty propagation. Experimental data
on a range of liquid foods and beverages are used. Results show that
relative measurement uncertainties of ∼2–5 % in n and K results in a prediction uncertainty of less than 10 % in the major pressure losses. Moreover, uncertainty in n influences the prediction quality more severely than uncertainty in K, and prediction uncertainty is larger the closer n
is to 1. This is the first contribution providing a quantitative
relationship between measurement uncertainty and prediction uncertainty
for this application. It provides measurement guidelines and helps
identify the role of uncertainty propagation in predicting pressure
losses when pumping complex liquid foods. (Less)
Please use this url to cite or link to this publication:
author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
ANN, Power-law fluids, Pumping non-Newtonian fluids, Uncertainty management
in
Food and Bioproducts Processing
volume
154
pages
11 pages
publisher
Elsevier
external identifiers
  • scopus:105020256566
ISSN
0960-3085
DOI
10.1016/j.fbp.2025.10.017
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2025 The Authors
id
6b494582-fda1-46c6-bf5b-a099a5a42e81
date added to LUP
2025-11-12 06:24:06
date last changed
2025-11-18 10:57:23
@article{6b494582-fda1-46c6-bf5b-a099a5a42e81,
  abstract     = {{To predict major pressure losses from pumping shear thinning liquid foods and beverage, rheometer-measured flow behaviour (<em>n)</em> and flow consistency (<em>K)</em><br>
 indices are needed. These are always obtained with some measurement <br>
uncertainty. This contribution develops a tool that allows for <br>
quantitatively estimating how a given measurement uncertainty in the <br>
rheological parameters translate to prediction uncertainty in major <br>
pressure losses, and how much measurement uncertainty can be allowed <br>
while ensuing that the prediction uncertainty is within a given <br>
tolerance. Methodologically, the study combines the general uncertainty <br>
management framework, Monte Carlo simulations, and an artificial neural <br>
network regression to predict uncertainty propagation. Experimental data<br>
 on a range of liquid foods and beverages are used. Results show that <br>
relative measurement uncertainties of ∼2–5 % in <em>n</em> and <em>K</em> results in a prediction uncertainty of less than 10 % in the major pressure losses. Moreover, uncertainty in <em>n</em> influences the prediction quality more severely than uncertainty in <em>K</em>, and prediction uncertainty is larger the closer <em>n</em><br>
 is to 1. This is the first contribution providing a quantitative <br>
relationship between measurement uncertainty and prediction uncertainty <br>
for this application. It provides measurement guidelines and helps <br>
identify the role of uncertainty propagation in predicting pressure <br>
losses when pumping complex liquid foods.}},
  author       = {{Håkansson, Andreas and Ekelund, Hanna and Isendahl, Hanna and Viklund, Hannah and Wijkander, Helena and Arlov, Dragana}},
  issn         = {{0960-3085}},
  keywords     = {{ANN; Power-law fluids; Pumping non-Newtonian fluids; Uncertainty management}},
  language     = {{eng}},
  pages        = {{489--499}},
  publisher    = {{Elsevier}},
  series       = {{Food and Bioproducts Processing}},
  title        = {{Prediction of major pressure losses pumping non-Newtonian foods and beverages – The role of uncertainty propagation for power law fluids}},
  url          = {{http://dx.doi.org/10.1016/j.fbp.2025.10.017}},
  doi          = {{10.1016/j.fbp.2025.10.017}},
  volume       = {{154}},
  year         = {{2025}},
}