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MICE-PSO-RF Model for Predicting Coal Spontaneous Combustion Temperature Based on Multiple Imputation by Chained Equations.

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Title: MICE-PSO-RF Model for Predicting Coal Spontaneous Combustion Temperature Based on Multiple Imputation by Chained Equations.
Authors: Zheng, Xuezhao1,2,3 (AUTHOR), Li, Peihua1 (AUTHOR) 24220089058@stu.xust.edu.cn, Cai, Guobin1,2,3 (AUTHOR) 22120089018@stu.xust.edu.cn, Guo, Jun1,2,3 (AUTHOR), Liu, Yin1,2 (AUTHOR)
Source: Combustion Science & Technology. 2026, Vol. 198 Issue 8, p2225-2242. 18p.
Subject Terms: *Multiple imputation (Statistics), *Random forest algorithms, *Coal combustion, *Machine learning, *Particle swarm optimization, *Temperature measurements, *Missing data (Statistics)
Abstract: To address the issue of missing indicator gas data in coal spontaneous combustion temperature prediction models in practical applications, a novel prediction model is proposed, which integrates the MICE (Multiple Imputation by Chained Equations) method with PSO (Particle Swarm Optimization)-optimized RF (Random Forest) algorithm. A coal spontaneous combustion heating experiment was conducted, selecting O2, CO, CO2, CH4, and C2H6 as characteristic gases for coal combustion. Five sets of data with randomly introduced missing values were designed, and four imputation methods – KNN, RF, PSO-SVR, and MICE – were used to impute the missing values. The imputed data were then used to train three temperature prediction models (BP, RF, and SVR), with parameters optimized using PSO. The imputation and prediction results were compared. The results indicate that, across five different missing data rates, the MICE method outperforms the other imputation techniques. Without parameter tuning, all three models showed overfitting. However, after PSO optimization, the PSO-RF model achieved the highest prediction accuracy and demonstrated good stability. In practical applications, the MICE-PSO-RF model yielded an average temperature prediction error of 2.69°C across 15 test sets, demonstrating reliable performance in predicting coal temperature. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
FullText Text:
  Availability: 0
Header DbId: enr
DbLabel: Energy & Power Source
An: 193490012
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: MICE-PSO-RF Model for Predicting Coal Spontaneous Combustion Temperature Based on Multiple Imputation by Chained Equations.
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  Data: <searchLink fieldCode="AR" term="%22Zheng%2C+Xuezhao%22">Zheng, Xuezhao</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Peihua%22">Li, Peihua</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 24220089058@stu.xust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Cai%2C+Guobin%22">Cai, Guobin</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> 22120089018@stu.xust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Guo%2C+Jun%22">Guo, Jun</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Yin%22">Liu, Yin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Combustion+Science+%26+Technology%22">Combustion Science & Technology</searchLink>. 2026, Vol. 198 Issue 8, p2225-2242. 18p.
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  Data: *<searchLink fieldCode="DE" term="%22Multiple+imputation+%28Statistics%29%22">Multiple imputation (Statistics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Coal+combustion%22">Coal combustion</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Temperature+measurements%22">Temperature measurements</searchLink><br />*<searchLink fieldCode="DE" term="%22Missing+data+%28Statistics%29%22">Missing data (Statistics)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To address the issue of missing indicator gas data in coal spontaneous combustion temperature prediction models in practical applications, a novel prediction model is proposed, which integrates the MICE (Multiple Imputation by Chained Equations) method with PSO (Particle Swarm Optimization)-optimized RF (Random Forest) algorithm. A coal spontaneous combustion heating experiment was conducted, selecting O2, CO, CO2, CH4, and C2H6 as characteristic gases for coal combustion. Five sets of data with randomly introduced missing values were designed, and four imputation methods – KNN, RF, PSO-SVR, and MICE – were used to impute the missing values. The imputed data were then used to train three temperature prediction models (BP, RF, and SVR), with parameters optimized using PSO. The imputation and prediction results were compared. The results indicate that, across five different missing data rates, the MICE method outperforms the other imputation techniques. Without parameter tuning, all three models showed overfitting. However, after PSO optimization, the PSO-RF model achieved the highest prediction accuracy and demonstrated good stability. In practical applications, the MICE-PSO-RF model yielded an average temperature prediction error of 2.69°C across 15 test sets, demonstrating reliable performance in predicting coal temperature. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193490012
RecordInfo BibRecord:
  BibEntity:
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      – Type: doi
        Value: 10.1080/00102202.2025.2500516
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 2225
    Subjects:
      – SubjectFull: Multiple imputation (Statistics)
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Coal combustion
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Temperature measurements
        Type: general
      – SubjectFull: Missing data (Statistics)
        Type: general
    Titles:
      – TitleFull: MICE-PSO-RF Model for Predicting Coal Spontaneous Combustion Temperature Based on Multiple Imputation by Chained Equations.
        Type: main
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            NameFull: Zheng, Xuezhao
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            NameFull: Li, Peihua
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            NameFull: Cai, Guobin
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            NameFull: Guo, Jun
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            NameFull: Liu, Yin
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            – D: 15
              M: 05
              Text: 2026
              Type: published
              Y: 2026
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              Value: 198
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              Value: 8
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            – TitleFull: Combustion Science & Technology
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