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

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dc.citedby1
dc.contributor.authorChen C.P.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorKoh S.P.en_US
dc.contributor.authorChooi Albert F.Y.en_US
dc.contributor.authorMohd Yapandi M.F.K.en_US
dc.contributor.authorid25824552100en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid57203901015en_US
dc.contributor.authorid57203904298en_US
dc.date.accessioned2023-05-29T06:51:17Z
dc.date.available2023-05-29T06:51:17Z
dc.date.issued2018
dc.descriptionAir pollution control equipment; Artificial intelligence; Combined cycle power plants; Environmental Protection Agency; Forecasting; Gas turbines; Land use; Meteorology; Monitoring; Regression analysis; Sulfur dioxide; Sustainable development; Vectors; Combined cycle gas turbine; Emission monitoring system; Environmental sustainability; Heterogeneous data sources; Intelligent environment; Power plant parameters; Prediction performance; Support vector regression (SVR); Big dataen_US
dc.description.abstractWith 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 (SO2) 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, SO2is 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 SO2gas 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 SO2gas emission into the atmosphere and consequently can be used as a tool for environmental sustainability monitoring. � 2018 University of Split, FESB.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8448375
dc.identifier.scopus2-s2.0-85053439823
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85053439823&partnerID=40&md5=89180225608fc77cd989fd481d037644
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23726
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018
dc.titleGas Emission Prediction for Environmental Sustainability via Heterogeneous Data Sources Correlation with Support Vector Regressionen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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