Trend modelling with artificial neural networks. Case study: operating zones identification for higher SO3 incorporation in cement clinker

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dc.creatorR. N. Lima
dc.creatorG. M. de Almeida
dc.creatorA. P. Braga
dc.creatorM. Cardoso
dc.date.accessioned2025-03-25T16:41:16Z
dc.date.accessioned2025-09-09T00:28:55Z
dc.date.available2025-03-25T16:41:16Z
dc.date.issued2016
dc.identifier.doihttps://doi.org/10.1016/j.engappai.2016.05.002
dc.identifier.issn0952-1976
dc.identifier.urihttps://hdl.handle.net/1843/80912
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofEngineering Applications of Artificial Intelligence
dc.rightsAcesso Restrito
dc.subjectRedes neurais (Computação)
dc.subjectEngenharia elétrica
dc.subjectCimento - Indústria
dc.subject.otherIndústria de cimento
dc.subject.otherRedes Neurais Artificiais
dc.subject.otherTrend modelling, ANN, Instantaneous measurements, Process data dispersion, Time alignment, Variable relationship
dc.titleTrend modelling with artificial neural networks. Case study: operating zones identification for higher SO3 incorporation in cement clinker
dc.typeArtigo de periódico
local.citation.epage25
local.citation.spage17
local.citation.volume54
local.description.resumoInstantaneous measurements of process variables are usually not representative of the process effects as a whole when defining the condition of an output sample mainly in case of laboratory analysis. Moreover, process data have considerable dispersion. This leads to uncertainty in input–output time alignment and in variable relationship. This work employs a trend data-based approach to overcome the negative effects of these uncertainties in both tasks variable selection commonly supported by correlation analysis and model identification. Two real case studies using a clinker rotary kiln from a cement plant and a chemical recovery boiler from a pulp mill were used for illustration purposes. More reliable data-driven system representation enhances the comprehension of the underlying system phenomena supporting a more rational basis for decision making.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA QUÍMICA
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S0952197616300860

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