Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions
Industry 4.0 constitutes a major application domain for sensor data analytics. Industrial furnaces (IFs) are complex machines made with special thermodynamic materials and technologies used in industrial production applications that require special heat treatment cycles. One of the most critical iss...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/10/3947 |
| _version_ | 1827666444555386880 |
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| author | Javier Rubio-Loyola Wolph Ronald Shwagger Paul-Fils |
| author_facet | Javier Rubio-Loyola Wolph Ronald Shwagger Paul-Fils |
| author_sort | Javier Rubio-Loyola |
| collection | DOAJ |
| description | Industry 4.0 constitutes a major application domain for sensor data analytics. Industrial furnaces (IFs) are complex machines made with special thermodynamic materials and technologies used in industrial production applications that require special heat treatment cycles. One of the most critical issues while operating IFs is the emission of black carbon (EoBC), which is due to a large number of factors such as the quality and amount of fuel, furnace efficiency, technology used for the process, operation practices, type of loads and other aspects related to the process conditions or mechanical properties of fluids at furnace operation. This paper presents a methodological approach to predict EoBC during the operation of IFs with the use of predictive models of machine learning (ML). We make use of a real data set with historical operation to train ML models, and through evaluation with real data we identify the most suitable approach that best fits the characteristics of the data set and implementation constraints in real production environments. The evaluation results confirm that it is possible to predict the undesirable EoBC well in advance, by means of a predictive model. To the best of our knowledge, this paper is the first approach to detail machine-learning concepts for predicting EoBC in the IF industry. |
| first_indexed | 2024-03-10T01:51:46Z |
| format | Article |
| id | doaj.art-326eb55f322140bd890fffee28ce3f55 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:51:46Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-326eb55f322140bd890fffee28ce3f552023-11-23T13:03:56ZengMDPI AGSensors1424-82202022-05-012210394710.3390/s22103947Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon EmissionsJavier Rubio-Loyola0Wolph Ronald Shwagger Paul-Fils1Centre for Research and Advanced Studies (Cinvestav), Ciudad Victoria 87130, MexicoCentre for Research and Advanced Studies (Cinvestav), Ciudad Victoria 87130, MexicoIndustry 4.0 constitutes a major application domain for sensor data analytics. Industrial furnaces (IFs) are complex machines made with special thermodynamic materials and technologies used in industrial production applications that require special heat treatment cycles. One of the most critical issues while operating IFs is the emission of black carbon (EoBC), which is due to a large number of factors such as the quality and amount of fuel, furnace efficiency, technology used for the process, operation practices, type of loads and other aspects related to the process conditions or mechanical properties of fluids at furnace operation. This paper presents a methodological approach to predict EoBC during the operation of IFs with the use of predictive models of machine learning (ML). We make use of a real data set with historical operation to train ML models, and through evaluation with real data we identify the most suitable approach that best fits the characteristics of the data set and implementation constraints in real production environments. The evaluation results confirm that it is possible to predict the undesirable EoBC well in advance, by means of a predictive model. To the best of our knowledge, this paper is the first approach to detail machine-learning concepts for predicting EoBC in the IF industry.https://www.mdpi.com/1424-8220/22/10/3947industrial furnacesblack carbonmachine learningpredictive models |
| spellingShingle | Javier Rubio-Loyola Wolph Ronald Shwagger Paul-Fils Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions Sensors industrial furnaces black carbon machine learning predictive models |
| title | Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions |
| title_full | Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions |
| title_fullStr | Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions |
| title_full_unstemmed | Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions |
| title_short | Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions |
| title_sort | applied machine learning in industry 4 0 case study research in predictive models for black carbon emissions |
| topic | industrial furnaces black carbon machine learning predictive models |
| url | https://www.mdpi.com/1424-8220/22/10/3947 |
| work_keys_str_mv | AT javierrubioloyola appliedmachinelearninginindustry40casestudyresearchinpredictivemodelsforblackcarbonemissions AT wolphronaldshwaggerpaulfils appliedmachinelearninginindustry40casestudyresearchinpredictivemodelsforblackcarbonemissions |