Machine learning regression algorithms to predict emissions from steam boilers
Currently, the modeling of complex chemical-physical processes is drastically influencing industrial development. Therefore, the analysis and study of the combustion process of the boilers using machine learning (ML) techniques are vital to increase the efficiency with which this equipment operates...
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| Vydáno v: | Heliyon Ročník 10; číslo 5; s. e26892 |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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England
Elsevier Ltd
15.03.2024
Elsevier |
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| ISSN: | 2405-8440, 2405-8440 |
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| Abstract | Currently, the modeling of complex chemical-physical processes is drastically influencing industrial development. Therefore, the analysis and study of the combustion process of the boilers using machine learning (ML) techniques are vital to increase the efficiency with which this equipment operates and reduce the pollution load they contribute to the environment. This work aims to predict the emissions of CO, CO2, NOx, and the temperature of the exhaust gases of industrial boilers from real data. Different ML algorithms for regression analysis are discussed. The following are input variables: ambient temperature, working pressure, steam production, and the type of fuel used in around 20 industrial boilers. Each boiler's emission data was collected using a TESTO 350 Combustion Gas Analyzer. The modeling, with a machine learning approach using the Gradient Boosting Regression algorithm, showed better performance in the predictions made on the test data, outperforming all other models studied. It was achieved with predicted values showing a mean absolute error of 0.51 and a coefficient of determination of 99.80%. Different regression models (DNN, MLR, RFR, GBR) were compared to select the most optimal. Compared to models based on Linear Regression, the DNN model has better prediction performance. The proposed model provides a new method to predict CO2, CO, NOx emissions, and exhaust gas outlet temperature. |
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| AbstractList | Currently, the modeling of complex chemical-physical processes is drastically influencing industrial development. Therefore, the analysis and study of the combustion process of the boilers using machine learning (ML) techniques are vital to increase the efficiency with which this equipment operates and reduce the pollution load they contribute to the environment. This work aims to predict the emissions of CO, CO2, NOx, and the temperature of the exhaust gases of industrial boilers from real data. Different ML algorithms for regression analysis are discussed. The following are input variables: ambient temperature, working pressure, steam production, and the type of fuel used in around 20 industrial boilers. Each boiler's emission data was collected using a TESTO 350 Combustion Gas Analyzer. The modeling, with a machine learning approach using the Gradient Boosting Regression algorithm, showed better performance in the predictions made on the test data, outperforming all other models studied. It was achieved with predicted values showing a mean absolute error of 0.51 and a coefficient of determination of 99.80%. Different regression models (DNN, MLR, RFR, GBR) were compared to select the most optimal. Compared to models based on Linear Regression, the DNN model has better prediction performance. The proposed model provides a new method to predict CO2, CO, NOx emissions, and exhaust gas outlet temperature. Currently, the modeling of complex chemical-physical processes is drastically influencing industrial development. Therefore, the analysis and study of the combustion process of the boilers using machine learning (ML) techniques are vital to increase the efficiency with which this equipment operates and reduce the pollution load they contribute to the environment. This work aims to predict the emissions of CO, CO2, NOx, and the temperature of the exhaust gases of industrial boilers from real data. Different ML algorithms for regression analysis are discussed. The following are input variables: ambient temperature, working pressure, steam production, and the type of fuel used in around 20 industrial boilers. Each boiler's emission data was collected using a TESTO 350 Combustion Gas Analyzer. The modeling, with a machine learning approach using the Gradient Boosting Regression algorithm, showed better performance in the predictions made on the test data, outperforming all other models studied. It was achieved with predicted values showing a mean absolute error of 0.51 and a coefficient of determination of 99.80%. Different regression models (DNN, MLR, RFR, GBR) were compared to select the most optimal. Compared to models based on Linear Regression, the DNN model has better prediction performance. The proposed model provides a new method to predict CO2, CO, NOx emissions, and exhaust gas outlet temperature.Currently, the modeling of complex chemical-physical processes is drastically influencing industrial development. Therefore, the analysis and study of the combustion process of the boilers using machine learning (ML) techniques are vital to increase the efficiency with which this equipment operates and reduce the pollution load they contribute to the environment. This work aims to predict the emissions of CO, CO2, NOx, and the temperature of the exhaust gases of industrial boilers from real data. Different ML algorithms for regression analysis are discussed. The following are input variables: ambient temperature, working pressure, steam production, and the type of fuel used in around 20 industrial boilers. Each boiler's emission data was collected using a TESTO 350 Combustion Gas Analyzer. The modeling, with a machine learning approach using the Gradient Boosting Regression algorithm, showed better performance in the predictions made on the test data, outperforming all other models studied. It was achieved with predicted values showing a mean absolute error of 0.51 and a coefficient of determination of 99.80%. Different regression models (DNN, MLR, RFR, GBR) were compared to select the most optimal. Compared to models based on Linear Regression, the DNN model has better prediction performance. The proposed model provides a new method to predict CO2, CO, NOx emissions, and exhaust gas outlet temperature. Currently, the modeling of complex chemical-physical processes is drastically influencing industrial development. Therefore, the analysis and study of the combustion process of the boilers using machine learning (ML) techniques are vital to increase the efficiency with which this equipment operates and reduce the pollution load they contribute to the environment. This work aims to predict the emissions of CO, CO , NOx, and the temperature of the exhaust gases of industrial boilers from real data. Different ML algorithms for regression analysis are discussed. The following are input variables: ambient temperature, working pressure, steam production, and the type of fuel used in around 20 industrial boilers. Each boiler's emission data was collected using a TESTO 350 Combustion Gas Analyzer. The modeling, with a machine learning approach using the Gradient Boosting Regression algorithm, showed better performance in the predictions made on the test data, outperforming all other models studied. It was achieved with predicted values showing a mean absolute error of 0.51 and a coefficient of determination of 99.80%. Different regression models (DNN, MLR, RFR, GBR) were compared to select the most optimal. Compared to models based on Linear Regression, the DNN model has better prediction performance. The proposed model provides a new method to predict CO , CO, NOx emissions, and exhaust gas outlet temperature. Currently, the modeling of complex chemical-physical processes is drastically influencing industrial development. Therefore, the analysis and study of the combustion process of the boilers using machine learning (ML) techniques are vital to increase the efficiency with which this equipment operates and reduce the pollution load they contribute to the environment. This work aims to predict the emissions of CO, CO₂, NOx, and the temperature of the exhaust gases of industrial boilers from real data. Different ML algorithms for regression analysis are discussed. The following are input variables: ambient temperature, working pressure, steam production, and the type of fuel used in around 20 industrial boilers. Each boiler's emission data was collected using a TESTO 350 Combustion Gas Analyzer. The modeling, with a machine learning approach using the Gradient Boosting Regression algorithm, showed better performance in the predictions made on the test data, outperforming all other models studied. It was achieved with predicted values showing a mean absolute error of 0.51 and a coefficient of determination of 99.80%. Different regression models (DNN, MLR, RFR, GBR) were compared to select the most optimal. Compared to models based on Linear Regression, the DNN model has better prediction performance. The proposed model provides a new method to predict CO₂, CO, NOx emissions, and exhaust gas outlet temperature. |
| ArticleNumber | e26892 |
| Author | Conde-García, Rebeca E. Nuñez-Alvarez, José R. Arias-Gilart, Ramón Palma-Ramírez, Dayana Hernández-Herrera, Hernan Ross-Veitía, Bárbara D. Espinel-Hernández, Alejandro Llosas-Albuerne, Yolanda E. |
| Author_xml | – sequence: 1 givenname: Bárbara D. surname: Ross-Veitía fullname: Ross-Veitía, Bárbara D. email: baby91@uo.edu.cu organization: National Center for Applied Electromagnetism (CNEA), Universidad de Oriente, Ave. de Las Américas s/n, 90100, Santiago de Cuba, Cuba – sequence: 2 givenname: Dayana orcidid: 0000-0002-5665-3153 surname: Palma-Ramírez fullname: Palma-Ramírez, Dayana email: dayana.palma@uo.edu.cu organization: National Center for Applied Electromagnetism (CNEA), Universidad de Oriente, Ave. de Las Américas s/n, 90100, Santiago de Cuba, Cuba – sequence: 3 givenname: Ramón orcidid: 0000-0003-2050-9712 surname: Arias-Gilart fullname: Arias-Gilart, Ramón email: ramonariasgilart@gmail.com organization: National Center for Applied Electromagnetism (CNEA), Universidad de Oriente, Ave. de Las Américas s/n, 90100, Santiago de Cuba, Cuba – sequence: 4 givenname: Rebeca E. surname: Conde-García fullname: Conde-García, Rebeca E. email: rebeca@uo.edu.cu organization: National Center for Applied Electromagnetism (CNEA), Universidad de Oriente, Ave. de Las Américas s/n, 90100, Santiago de Cuba, Cuba – sequence: 5 givenname: Alejandro orcidid: 0000-0003-4192-363X surname: Espinel-Hernández fullname: Espinel-Hernández, Alejandro email: espinel@uo.edu.cu organization: National Center for Applied Electromagnetism (CNEA), Universidad de Oriente, Ave. de Las Américas s/n, 90100, Santiago de Cuba, Cuba – sequence: 6 givenname: José R. orcidid: 0000-0002-6607-7305 surname: Nuñez-Alvarez fullname: Nuñez-Alvarez, José R. email: jnunez22@cuc.edu.co organization: Energy Department, Universidad de la Costa, (CUC), Calle 58 # 55-66, Barranquilla, 080002, Colombia – sequence: 7 givenname: Hernan surname: Hernández-Herrera fullname: Hernández-Herrera, Hernan email: hernan.hernadez@unisimon.edu.co organization: Faculty of Engineering, Universidad Simón Bolívar, Carrera 59 #59-132, Barranquilla, 080002, Colombia – sequence: 8 givenname: Yolanda E. surname: Llosas-Albuerne fullname: Llosas-Albuerne, Yolanda E. email: yolanda.llosas@utm.edu.ec organization: Electrical Engineering Department, Universidad Técnica de Manabí (UTM), Portoviejo, Manabí, 130105, Ecuador |
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| Cites_doi | 10.1109/ACCESS.2021.3091399 10.3390/s21217241 10.1007/s11069-020-04015-7 10.1016/j.fuel.2003.12.003 10.1016/j.aej.2023.01.048 10.1016/j.apenergy.2020.114566 10.1016/j.apenergy.2018.09.182 10.1016/j.aej.2022.10.053 10.3390/s22020458 10.3390/math7070629 10.1016/j.csite.2023.103440 10.1016/j.fuel.2023.128348 10.1016/j.energy.2013.08.027 10.15282/jmes.14.1.2020.07.0492 10.1016/j.scitotenv.2023.166108 10.1016/j.ijtm.2004.09.001 10.1016/j.procs.2016.05.512 10.1021/acs.nanolett.8b05196 10.3390/en14165196 10.1016/j.isatra.2022.06.009 10.1007/s00521-019-04644-5 10.3390/en14051267 10.1016/j.aej.2022.12.038 10.1016/j.apcatb.2023.123241 10.1016/j.energy.2015.03.095 10.1016/j.dche.2023.100115 |
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