Gas Emission Prediction for Environmental Sustainability via Heterogeneous Data Sources Correlation with Support Vector Regression

Chai Phing Chen, Sieh Kiong Tiong, Johnny Siaw Paw Koh, Fong Yu Chooi Albert, Md Fauzan K. Mohd Yapandi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

With the emerging of industrial revolution 4.0, artificial intelligence (AI) together with big data analytics will be playing an important role in environmental sustainability by improving system efficiency and intelligent environment monitoring. The increasing of electricity demand and urbanization process have caused more power plants to be built from time to time, which may cause environmental issue for its surrounding. Hence, necessary measures need to be taken to ensure environmental sustainability. This paper is to investigate the ability of a regression based artificial intelligent algorithm, namely Support Vector Regression (SVR), correlating with multiple sources of big data sets to predict the Sulfur Dioxide (SO 2 ) emission level at atmosphere surrounding a Combined Cycle Gas Turbine (CCGT) power plant. The heterogeneous data sources that have been used to train and establish the knowledge of SVR are meteorological data, terrain and land use data, historical emission data and power plant parameters particularly related to the point source emitter. With the correlation of multiple big data sources, SVR was then trained for the prediction of emission rate at the chimney and certain targeted areas such as residential area surrounding the power plant, which are classified as air sensitive receptors (ASR). Although there are a number of gasses emitted from power plant, SO 2 is selected as the key emission in this paper due to inhaling of sulfur dioxide will cause respiratory symptoms and diseases for living things. The developed predictive model is incorporated into an online monitoring tool namely Integrated Support Vector Regression Emission Monitoring System (i-SuVEMS). The predicted SO 2 gas emission result by i-Su VEMS was compared with the actual emissions results from the CEMS. The predicted values from i-SuVEMS shows good accuracy with RMSE less than 0.02 as compared to the actual measured emission values. This prediction performance result indicates that i-Su VEMS is able to meet the requirement of US EPA 40 CFR Part 60 in predicting the quantity of SO 2 gas emission into the atmosphere and consequently can be used as a tool for environmental sustainability monitoring.

Original languageEnglish
Title of host publication2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018
EditorsJoel J. P. C. Rodrigues, Sandro Nizetic, Luigi Patrono, Joel J. P. C. Rodrigues, Petar Solic, Toni Perkovic, Katarina Vukojevic, Zeljka Milanovic
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9789532900835
Publication statusPublished - 27 Aug 2018
Event3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018 - Split, Croatia
Duration: 26 Jun 201829 Jun 2018

Publication series

Name2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018

Other

Other3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018
CountryCroatia
CitySplit
Period26/06/1829/06/18

Fingerprint

Support Vector Regression
Sustainability
Gas emissions
Sustainable development
Power plants
Monitoring
Prediction
Power Plant
Sulfur dioxide
Gas turbine power plants
Sulfur Dioxide
Combined cycle power plants
Chimneys
Monitoring System
Land use
Artificial intelligence
Atmosphere
Electricity
Gas
On-line Monitoring

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Modelling and Simulation

Cite this

Chen, C. P., Tiong, S. K., Koh, J. S. P., Chooi Albert, F. Y., & Mohd Yapandi, M. F. K. (2018). Gas Emission Prediction for Environmental Sustainability via Heterogeneous Data Sources Correlation with Support Vector Regression. In J. J. P. C. Rodrigues, S. Nizetic, L. Patrono, J. J. P. C. Rodrigues, P. Solic, T. Perkovic, K. Vukojevic, ... Z. Milanovic (Eds.), 2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018 [8448375] (2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018). Institute of Electrical and Electronics Engineers Inc..
Chen, Chai Phing ; Tiong, Sieh Kiong ; Koh, Johnny Siaw Paw ; Chooi Albert, Fong Yu ; Mohd Yapandi, Md Fauzan K. / Gas Emission Prediction for Environmental Sustainability via Heterogeneous Data Sources Correlation with Support Vector Regression. 2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018. editor / Joel J. P. C. Rodrigues ; Sandro Nizetic ; Luigi Patrono ; Joel J. P. C. Rodrigues ; Petar Solic ; Toni Perkovic ; Katarina Vukojevic ; Zeljka Milanovic. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018).
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title = "Gas Emission Prediction for Environmental Sustainability via Heterogeneous Data Sources Correlation with Support Vector Regression",
abstract = "With the emerging of industrial revolution 4.0, artificial intelligence (AI) together with big data analytics will be playing an important role in environmental sustainability by improving system efficiency and intelligent environment monitoring. The increasing of electricity demand and urbanization process have caused more power plants to be built from time to time, which may cause environmental issue for its surrounding. Hence, necessary measures need to be taken to ensure environmental sustainability. This paper is to investigate the ability of a regression based artificial intelligent algorithm, namely Support Vector Regression (SVR), correlating with multiple sources of big data sets to predict the Sulfur Dioxide (SO 2 ) emission level at atmosphere surrounding a Combined Cycle Gas Turbine (CCGT) power plant. The heterogeneous data sources that have been used to train and establish the knowledge of SVR are meteorological data, terrain and land use data, historical emission data and power plant parameters particularly related to the point source emitter. With the correlation of multiple big data sources, SVR was then trained for the prediction of emission rate at the chimney and certain targeted areas such as residential area surrounding the power plant, which are classified as air sensitive receptors (ASR). Although there are a number of gasses emitted from power plant, SO 2 is selected as the key emission in this paper due to inhaling of sulfur dioxide will cause respiratory symptoms and diseases for living things. The developed predictive model is incorporated into an online monitoring tool namely Integrated Support Vector Regression Emission Monitoring System (i-SuVEMS). The predicted SO 2 gas emission result by i-Su VEMS was compared with the actual emissions results from the CEMS. The predicted values from i-SuVEMS shows good accuracy with RMSE less than 0.02 as compared to the actual measured emission values. This prediction performance result indicates that i-Su VEMS is able to meet the requirement of US EPA 40 CFR Part 60 in predicting the quantity of SO 2 gas emission into the atmosphere and consequently can be used as a tool for environmental sustainability monitoring.",
author = "Chen, {Chai Phing} and Tiong, {Sieh Kiong} and Koh, {Johnny Siaw Paw} and {Chooi Albert}, {Fong Yu} and {Mohd Yapandi}, {Md Fauzan K.}",
year = "2018",
month = "8",
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Chen, CP, Tiong, SK, Koh, JSP, Chooi Albert, FY & Mohd Yapandi, MFK 2018, Gas Emission Prediction for Environmental Sustainability via Heterogeneous Data Sources Correlation with Support Vector Regression. in JJPC Rodrigues, S Nizetic, L Patrono, JJPC Rodrigues, P Solic, T Perkovic, K Vukojevic & Z Milanovic (eds), 2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018., 8448375, 2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018, Institute of Electrical and Electronics Engineers Inc., 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018, Split, Croatia, 26/06/18.

Gas Emission Prediction for Environmental Sustainability via Heterogeneous Data Sources Correlation with Support Vector Regression. / Chen, Chai Phing; Tiong, Sieh Kiong; Koh, Johnny Siaw Paw; Chooi Albert, Fong Yu; Mohd Yapandi, Md Fauzan K.

2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018. ed. / Joel J. P. C. Rodrigues; Sandro Nizetic; Luigi Patrono; Joel J. P. C. Rodrigues; Petar Solic; Toni Perkovic; Katarina Vukojevic; Zeljka Milanovic. Institute of Electrical and Electronics Engineers Inc., 2018. 8448375 (2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Gas Emission Prediction for Environmental Sustainability via Heterogeneous Data Sources Correlation with Support Vector Regression

AU - Chen, Chai Phing

AU - Tiong, Sieh Kiong

AU - Koh, Johnny Siaw Paw

AU - Chooi Albert, Fong Yu

AU - Mohd Yapandi, Md Fauzan K.

PY - 2018/8/27

Y1 - 2018/8/27

N2 - With the emerging of industrial revolution 4.0, artificial intelligence (AI) together with big data analytics will be playing an important role in environmental sustainability by improving system efficiency and intelligent environment monitoring. The increasing of electricity demand and urbanization process have caused more power plants to be built from time to time, which may cause environmental issue for its surrounding. Hence, necessary measures need to be taken to ensure environmental sustainability. This paper is to investigate the ability of a regression based artificial intelligent algorithm, namely Support Vector Regression (SVR), correlating with multiple sources of big data sets to predict the Sulfur Dioxide (SO 2 ) emission level at atmosphere surrounding a Combined Cycle Gas Turbine (CCGT) power plant. The heterogeneous data sources that have been used to train and establish the knowledge of SVR are meteorological data, terrain and land use data, historical emission data and power plant parameters particularly related to the point source emitter. With the correlation of multiple big data sources, SVR was then trained for the prediction of emission rate at the chimney and certain targeted areas such as residential area surrounding the power plant, which are classified as air sensitive receptors (ASR). Although there are a number of gasses emitted from power plant, SO 2 is selected as the key emission in this paper due to inhaling of sulfur dioxide will cause respiratory symptoms and diseases for living things. The developed predictive model is incorporated into an online monitoring tool namely Integrated Support Vector Regression Emission Monitoring System (i-SuVEMS). The predicted SO 2 gas emission result by i-Su VEMS was compared with the actual emissions results from the CEMS. The predicted values from i-SuVEMS shows good accuracy with RMSE less than 0.02 as compared to the actual measured emission values. This prediction performance result indicates that i-Su VEMS is able to meet the requirement of US EPA 40 CFR Part 60 in predicting the quantity of SO 2 gas emission into the atmosphere and consequently can be used as a tool for environmental sustainability monitoring.

AB - With the emerging of industrial revolution 4.0, artificial intelligence (AI) together with big data analytics will be playing an important role in environmental sustainability by improving system efficiency and intelligent environment monitoring. The increasing of electricity demand and urbanization process have caused more power plants to be built from time to time, which may cause environmental issue for its surrounding. Hence, necessary measures need to be taken to ensure environmental sustainability. This paper is to investigate the ability of a regression based artificial intelligent algorithm, namely Support Vector Regression (SVR), correlating with multiple sources of big data sets to predict the Sulfur Dioxide (SO 2 ) emission level at atmosphere surrounding a Combined Cycle Gas Turbine (CCGT) power plant. The heterogeneous data sources that have been used to train and establish the knowledge of SVR are meteorological data, terrain and land use data, historical emission data and power plant parameters particularly related to the point source emitter. With the correlation of multiple big data sources, SVR was then trained for the prediction of emission rate at the chimney and certain targeted areas such as residential area surrounding the power plant, which are classified as air sensitive receptors (ASR). Although there are a number of gasses emitted from power plant, SO 2 is selected as the key emission in this paper due to inhaling of sulfur dioxide will cause respiratory symptoms and diseases for living things. The developed predictive model is incorporated into an online monitoring tool namely Integrated Support Vector Regression Emission Monitoring System (i-SuVEMS). The predicted SO 2 gas emission result by i-Su VEMS was compared with the actual emissions results from the CEMS. The predicted values from i-SuVEMS shows good accuracy with RMSE less than 0.02 as compared to the actual measured emission values. This prediction performance result indicates that i-Su VEMS is able to meet the requirement of US EPA 40 CFR Part 60 in predicting the quantity of SO 2 gas emission into the atmosphere and consequently can be used as a tool for environmental sustainability monitoring.

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M3 - Conference contribution

AN - SCOPUS:85053439823

SN - 9789532900835

T3 - 2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018

BT - 2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018

A2 - Rodrigues, Joel J. P. C.

A2 - Nizetic, Sandro

A2 - Patrono, Luigi

A2 - Rodrigues, Joel J. P. C.

A2 - Solic, Petar

A2 - Perkovic, Toni

A2 - Vukojevic, Katarina

A2 - Milanovic, Zeljka

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Chen CP, Tiong SK, Koh JSP, Chooi Albert FY, Mohd Yapandi MFK. Gas Emission Prediction for Environmental Sustainability via Heterogeneous Data Sources Correlation with Support Vector Regression. In Rodrigues JJPC, Nizetic S, Patrono L, Rodrigues JJPC, Solic P, Perkovic T, Vukojevic K, Milanovic Z, editors, 2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8448375. (2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018).