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A digital twin integrating multi-objective optimization to support fryer operators in managing potato crisps production.

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Title: A digital twin integrating multi-objective optimization to support fryer operators in managing potato crisps production.
Authors: Perrignon, Manon1 (AUTHOR), Emily, Mathieu2 (AUTHOR), Munch, Mélanie3 (AUTHOR), Debuire, Paul4 (AUTHOR), Jeantet, Romain1 (AUTHOR), Croguennec, Thomas1 (AUTHOR)
Source: Journal of Food Engineering. Mar2026, Vol. 406, pN.PAG-N.PAG. 1p.
Subjects: Digital twin, Multi-objective optimization, Product quality, Food production, Machine learning, Snack food industry, Decision support systems
Abstract: The production of potato crisps currently relies on the expertise of human operators, known as fryers, whose training is long and demanding. With the decline in fryer vocations and the increase in consumer quality expectations, it is now essential to develop decision-support tools to make fryer work easier and better control product quality. This study proposes a digital twin (DT) approach that incorporates multi-objective optimization to assist fryers in managing their crisp production line. Data from crisp production is collected and used to model key crisp physicochemical indicators (Fat content, Moisture content, and Lightness) using a Machine Learning approach, specifically the Random Forest method. Then, a multi-objective optimization is carried out using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, which identifies optimal adjustments to enhance the three crisp physicochemical indicators. The optimization is tested on 2 batches of potatoes as inputs. The algorithm generates a set of optimal solutions, from which a final solution is selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making method. This solution provides practical recommendations for adjusting production parameters according to a given potato batch quality. The results show that physicochemical parameters of the crisps are similar after optimization, regardless of the quality the potato batch. Variation in potato batch quality is compensated by appropriate adjustments of the crisp manufacturing process parameters ensuring consistent and optimal production. In conclusion, this digital twin, integrating multi-objective optimization, proves to be a valuable tool for improving fryer decision-making and optimizing production line management. [Display omitted] • Digital twin integrating multi-objective optimization for crisp manufacturing. • Machine learning models predict fat, moisture and lightness performance indicators. • Optimization delivers optimal solutions proving differential control necessity between potato batches. • Process parameters must be adjusted based on potato quality to meet crisp standards. • Framework provides a decision-support tool for process control by fryer operators. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Food Engineering is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 188753250
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PubType: Academic Journal
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  Data: A digital twin integrating multi-objective optimization to support fryer operators in managing potato crisps production.
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  Data: <searchLink fieldCode="AR" term="%22Perrignon%2C+Manon%22">Perrignon, Manon</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Emily%2C+Mathieu%22">Emily, Mathieu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Munch%2C+Mélanie%22">Munch, Mélanie</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Debuire%2C+Paul%22">Debuire, Paul</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jeantet%2C+Romain%22">Jeantet, Romain</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Croguennec%2C+Thomas%22">Croguennec, Thomas</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Food+Engineering%22">Journal of Food Engineering</searchLink>. Mar2026, Vol. 406, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Product+quality%22">Product quality</searchLink><br /><searchLink fieldCode="DE" term="%22Food+production%22">Food production</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Snack+food+industry%22">Snack food industry</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: The production of potato crisps currently relies on the expertise of human operators, known as fryers, whose training is long and demanding. With the decline in fryer vocations and the increase in consumer quality expectations, it is now essential to develop decision-support tools to make fryer work easier and better control product quality. This study proposes a digital twin (DT) approach that incorporates multi-objective optimization to assist fryers in managing their crisp production line. Data from crisp production is collected and used to model key crisp physicochemical indicators (Fat content, Moisture content, and Lightness) using a Machine Learning approach, specifically the Random Forest method. Then, a multi-objective optimization is carried out using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, which identifies optimal adjustments to enhance the three crisp physicochemical indicators. The optimization is tested on 2 batches of potatoes as inputs. The algorithm generates a set of optimal solutions, from which a final solution is selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making method. This solution provides practical recommendations for adjusting production parameters according to a given potato batch quality. The results show that physicochemical parameters of the crisps are similar after optimization, regardless of the quality the potato batch. Variation in potato batch quality is compensated by appropriate adjustments of the crisp manufacturing process parameters ensuring consistent and optimal production. In conclusion, this digital twin, integrating multi-objective optimization, proves to be a valuable tool for improving fryer decision-making and optimizing production line management. [Display omitted] • Digital twin integrating multi-objective optimization for crisp manufacturing. • Machine learning models predict fat, moisture and lightness performance indicators. • Optimization delivers optimal solutions proving differential control necessity between potato batches. • Process parameters must be adjusted based on potato quality to meet crisp standards. • Framework provides a decision-support tool for process control by fryer operators. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Food Engineering is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.jfoodeng.2025.112800
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Digital twin
        Type: general
      – SubjectFull: Multi-objective optimization
        Type: general
      – SubjectFull: Product quality
        Type: general
      – SubjectFull: Food production
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Snack food industry
        Type: general
      – SubjectFull: Decision support systems
        Type: general
    Titles:
      – TitleFull: A digital twin integrating multi-objective optimization to support fryer operators in managing potato crisps production.
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            NameFull: Perrignon, Manon
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            NameFull: Emily, Mathieu
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            NameFull: Munch, Mélanie
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            NameFull: Debuire, Paul
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            NameFull: Jeantet, Romain
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          Dates:
            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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              Value: 406
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