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Prediction and optimization of emissions in cement manufacturing plant under uncertainty by using artificial intelligence-based surrogate modeling.

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Title: Prediction and optimization of emissions in cement manufacturing plant under uncertainty by using artificial intelligence-based surrogate modeling.
Authors: Usman, Muhammad1 (AUTHOR), Ahmad, Iftikhar1 (AUTHOR) iftikhar.salarzai@scme.nust.edu.pk, Ahsan, Muhammad1 (AUTHOR), Caliskan, Hakan2 (AUTHOR)
Source: Environment, Development & Sustainability. Feb2026, Vol. 28 Issue 2, p2841-2864. 24p.
Subject Terms: *Artificial intelligence, *Prediction models, *Cement plants, *Sensitivity analysis, *Robust optimization, *Genetic algorithms, *Particle swarm optimization
Abstract: Uncertainties in the process industries are the biggest challenge for smooth operation. This study is based on the use of artificial intelligence based surrogate modeling for predicting and optimizing emissions in combustion sections of cement manufacturing plants under uncertainty. Uncertainties in feed flow rate, kiln air flow rate, tertiary air flow rate, and coal flow rate in the combustion sections of the plant are the subject of the study. Initially, an Aspen Plus model of the kiln and calciner units of the cement plant was developed and converted into a dynamic mode to generate 700 data samples with 10% uncertainty. Then, genetic algorithm (GA), particle swarm optimization (PSO), and GA-PSO frameworks were used to optimize the process conditions using Artificial Neural Network (ANN) models as surrogates. The GA-PSO framework exhibited an advantage over GA and PSO in predicting and optimizing CO2, and CO emissions. Besides, ANN models were used as a surrogate within the SOBOL and Fourier Amplitude Sensitivity Test frameworks for performing sensitivity analysis of the process. The sensitivity analysis was performed to identify the process conditions with the highest sensitivity toward the emissions. Based on sensitivity analysis, total coal and tertiary air were found to be more influencing parameters on process output. This study provides a baseline for real-time predicting and optimizing emissions at the cement plant. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 192482104
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PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: Prediction and optimization of emissions in cement manufacturing plant under uncertainty by using artificial intelligence-based surrogate modeling.
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  Data: <searchLink fieldCode="AR" term="%22Usman%2C+Muhammad%22">Usman, Muhammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ahmad%2C+Iftikhar%22">Ahmad, Iftikhar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> iftikhar.salarzai@scme.nust.edu.pk</i><br /><searchLink fieldCode="AR" term="%22Ahsan%2C+Muhammad%22">Ahsan, Muhammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Caliskan%2C+Hakan%22">Caliskan, Hakan</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Feb2026, Vol. 28 Issue 2, p2841-2864. 24p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br />*<searchLink fieldCode="DE" term="%22Cement+plants%22">Cement plants</searchLink><br />*<searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Robust+optimization%22">Robust optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Uncertainties in the process industries are the biggest challenge for smooth operation. This study is based on the use of artificial intelligence based surrogate modeling for predicting and optimizing emissions in combustion sections of cement manufacturing plants under uncertainty. Uncertainties in feed flow rate, kiln air flow rate, tertiary air flow rate, and coal flow rate in the combustion sections of the plant are the subject of the study. Initially, an Aspen Plus model of the kiln and calciner units of the cement plant was developed and converted into a dynamic mode to generate 700 data samples with 10% uncertainty. Then, genetic algorithm (GA), particle swarm optimization (PSO), and GA-PSO frameworks were used to optimize the process conditions using Artificial Neural Network (ANN) models as surrogates. The GA-PSO framework exhibited an advantage over GA and PSO in predicting and optimizing CO2, and CO emissions. Besides, ANN models were used as a surrogate within the SOBOL and Fourier Amplitude Sensitivity Test frameworks for performing sensitivity analysis of the process. The sensitivity analysis was performed to identify the process conditions with the highest sensitivity toward the emissions. Based on sensitivity analysis, total coal and tertiary air were found to be more influencing parameters on process output. This study provides a baseline for real-time predicting and optimizing emissions at the cement plant. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=192482104
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10668-024-05068-5
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 24
        StartPage: 2841
    Subjects:
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Cement plants
        Type: general
      – SubjectFull: Sensitivity analysis
        Type: general
      – SubjectFull: Robust optimization
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
    Titles:
      – TitleFull: Prediction and optimization of emissions in cement manufacturing plant under uncertainty by using artificial intelligence-based surrogate modeling.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Usman, Muhammad
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            NameFull: Ahmad, Iftikhar
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            NameFull: Ahsan, Muhammad
      – PersonEntity:
          Name:
            NameFull: Caliskan, Hakan
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          Dates:
            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
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              Value: 1387585X
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              Value: 28
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Environment, Development & Sustainability
              Type: main
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