Data-Driven AI Models within a User-Defined Optimization Objective Function in Cement Production.
cement kiln
cement mill
clustering
differential evolution
feature selection
key performance indicator
machine learning
optimization
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
14 Feb 2024
14 Feb 2024
Historique:
received:
15
12
2023
revised:
05
02
2024
accepted:
09
02
2024
medline:
24
2
2024
pubmed:
24
2
2024
entrez:
24
2
2024
Statut:
epublish
Résumé
This paper explores the energy-intensive cement industry, focusing on a plant in Greece and its mill and kiln unit. The data utilized include manipulated, non-manipulated, and uncontrolled variables. The non-manipulated variables are computed based on the machine learning (ML) models and selected by the minimum value of the normalized root mean square error (
Identifiants
pubmed: 38400383
pii: s24041225
doi: 10.3390/s24041225
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : European Regional Development Fund
ID : T2EDK-04748