Modeling and optimization of energy efficiency and product quality in staple food roasting using machine learning : a case study on cassava processing
JavaScript is disabled for your browser. Some features of this site may not work without it.
| dc.contributor.author | Mwape, Mwewa Chikonkolo
|
|
| dc.contributor.author | Kulig, Boris
|
|
| dc.contributor.author | Nurkhoeriyati, Tina
|
|
| dc.contributor.author | Roman, Franz
|
|
| dc.contributor.author | Parmar, Aditya
|
|
| dc.contributor.author | Emmambux, Mohammad Naushad
|
|
| dc.contributor.author | Hensel, Oliver
|
|
| dc.date.accessioned | 2025-03-27T11:58:19Z | |
| dc.date.available | 2025-03-27T11:58:19Z | |
| dc.date.issued | 2025-04 | |
| dc.description | DATA AVAILABILITY: The authors affirm that the data supporting the study's conclusions are available within the article [and/or] in supplementary materials. | en_US |
| dc.description.abstract | Please read abstract in the article. | en_US |
| dc.description.department | Consumer and Food Sciences | en_US |
| dc.description.librarian | hj2024 | en_US |
| dc.description.sdg | SDG-07:Affordable and clean energy | en_US |
| dc.description.sdg | SDG-09: Industry, innovation and infrastructure | en_US |
| dc.description.sdg | SDG-12:Responsible consumption and production | en_US |
| dc.description.sponsorship | The SUNGARI project from the German Federal Ministry of Education and Research and the European Union's Long-Term Joint European Union-African Union Research and Innovation Partnership on Renewable Energy (LEAP-RE). | en_US |
| dc.description.uri | https://www.sciencedirect.com/journal/thermal-science-and-engineering-progress | en_US |
| dc.identifier.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. | en_US |
| dc.identifier.issn | 2451-9049 (online) | |
| dc.identifier.other | 10.1016/j.tsep.2025.103258 | |
| dc.identifier.uri | http://hdl.handle.net/2263/101762 | |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.rights | © 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | en_US |
| dc.subject | Roasting prediction models | en_US |
| dc.subject | Machine learning modeling | en_US |
| dc.subject | Energy efficiency | en_US |
| dc.subject | Data-driven design | en_US |
| dc.subject | Post-harvest | en_US |
| dc.subject | I-optimal designs | en_US |
| dc.subject | SDG-07: Affordable and clean energy | en_US |
| dc.subject | SDG-09: Industry, innovation and infrastructure | en_US |
| dc.subject | SDG-12: Responsible consumption and production | en_US |
| dc.title | Modeling and optimization of energy efficiency and product quality in staple food roasting using machine learning : a case study on cassava processing | en_US |
| dc.type | Article | en_US |
