Modeling and optimization of energy efficiency and product quality in staple food roasting using machine learning : a case study on cassava processing
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Date
Authors
Mwape, Mwewa Chikonkolo
Kulig, Boris
Nurkhoeriyati, Tina
Roman, Franz
Parmar, Aditya
Emmambux, Mohammad Naushad
Hensel, Oliver
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Please read abstract in the article.
Description
DATA AVAILABILITY: The authors affirm that the data supporting the study's conclusions are available within the article [and/or] in supplementary materials.
Keywords
Roasting prediction models, Machine learning modeling, Energy efficiency, Data-driven design, Post-harvest, I-optimal designs, SDG-07: Affordable and clean energy, SDG-09: Industry, innovation and infrastructure, SDG-12: Responsible consumption and production
Sustainable Development Goals
SDG-07:Affordable and clean energy
SDG-09: Industry, innovation and infrastructure
SDG-12:Responsible consumption and production
SDG-09: Industry, innovation and infrastructure
SDG-12:Responsible consumption and production
Citation
Mwape, M.C., Kulig, B., Nurkhoeriyati, T. et al. 2025, 'Modeling and optimization of energy efficiency and product quality in staple food roasting using machine learning: a case study on cassava processing', Thermal Science and Engineering Progress, vol. 60, art. 103258, pp. 1-20, doi : 10.1016/j.tsep.2025.103258.