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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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

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.