Optimizing model parameters of artificial neural networks to predict vehicle emissions
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Seo, Jigu | - |
dc.contributor.author | Park, Sungwook | - |
dc.date.accessioned | 2022-12-20T04:52:18Z | - |
dc.date.available | 2022-12-20T04:52:18Z | - |
dc.date.created | 2022-12-07 | - |
dc.date.issued | 2023-02 | - |
dc.identifier.issn | 1352-2310 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/172725 | - |
dc.description.abstract | This paper presents a novel approach to predict carbon dioxide (CO2), nitrogen oxides (NOx), and carbon monoxide (CO) emissions of diesel vehicles using artificial neural network (ANN), which offer high degrees of accuracy and practicality. Six operating parameters (velocity, engine speed, engine torque, engine coolant temperature, fuel/air ratio, and intake air mass flow) collected through on-board diagnostic interface were used as predictors of exhaust emissions. The importance of each parameter to the emission predictions were comprehensively analyzed by comparing the coefficient of determination, root mean square error, cumulative emissions, and instantaneous emission rates. The emission prediction accuracy of ANN tends to increase as more parameters were considered as model inputs at the same time. However, the level of accuracy improvement depends on the input parameters. For CO2 emissions, engine torque and fuel/air ratio were good predictors for achieving high prediction accuracy. The relative importance of intake air mass flow rate and fuel/air ratio was high for NOx and CO predictions, respectively. In addition, the emission prediction accuracy of ANN depends on the vehicle type (Euro 5, Euro 6b, Euro 6d-temp). The emission prediction accuracy of vehicles equipped with after-treatment devices (selective catalytic reduction and lean NOx trap) was lower than that of vehicles without after-treatment devices. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Elsevier Ltd | - |
dc.title | Optimizing model parameters of artificial neural networks to predict vehicle emissions | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Sungwook | - |
dc.identifier.doi | 10.1016/j.atmosenv.2022.119508 | - |
dc.identifier.scopusid | 2-s2.0-85142320567 | - |
dc.identifier.wosid | 000890642300003 | - |
dc.identifier.bibliographicCitation | Atmospheric Environment, v.294, pp.1 - 12 | - |
dc.relation.isPartOf | Atmospheric Environment | - |
dc.citation.title | Atmospheric Environment | - |
dc.citation.volume | 294 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 12 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalResearchArea | Meteorology & Atmospheric Sciences | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Meteorology & Atmospheric Sciences | - |
dc.subject.keywordPlus | Air | - |
dc.subject.keywordPlus | Air intakes | - |
dc.subject.keywordPlus | Carbon dioxide | - |
dc.subject.keywordPlus | Carbon monoxide | - |
dc.subject.keywordPlus | Forecasting | - |
dc.subject.keywordPlus | Fuels | - |
dc.subject.keywordPlus | Mass transfer | - |
dc.subject.keywordPlus | Mean square error | - |
dc.subject.keywordPlus | Neural networks | - |
dc.subject.keywordPlus | Selective catalytic reduction | - |
dc.subject.keywordPlus | Vehicles | - |
dc.subject.keywordPlus | carbon dioxide | - |
dc.subject.keywordPlus | carbon monoxide | - |
dc.subject.keywordPlus | fuel | - |
dc.subject.keywordPlus | hydrocarbon | - |
dc.subject.keywordPlus | nitrogen oxide | - |
dc.subject.keywordPlus | Emission measurement systems | - |
dc.subject.keywordPlus | Emissions prediction | - |
dc.subject.keywordPlus | Engine torque | - |
dc.subject.keywordPlus | Fuel/air ratio | - |
dc.subject.keywordPlus | On-road emissions | - |
dc.subject.keywordPlus | Onboard diagnostic data | - |
dc.subject.keywordPlus | Portable emission measurement system | - |
dc.subject.keywordPlus | Prediction accuracy | - |
dc.subject.keywordPlus | Vehicle emission models | - |
dc.subject.keywordPlus | Vehicle-exhaust emission | - |
dc.subject.keywordPlus | artificial neural network | - |
dc.subject.keywordPlus | carbon dioxide | - |
dc.subject.keywordPlus | carbon monoxide | - |
dc.subject.keywordPlus | diesel engine | - |
dc.subject.keywordPlus | nitrogen oxides | - |
dc.subject.keywordPlus | air pollution control | - |
dc.subject.keywordPlus | airflow | - |
dc.subject.keywordPlus | Article | - |
dc.subject.keywordPlus | artificial neural network | - |
dc.subject.keywordPlus | diesel engine | - |
dc.subject.keywordPlus | exhaust gas | - |
dc.subject.keywordPlus | machine learning | - |
dc.subject.keywordPlus | nitrogen oxide emission | - |
dc.subject.keywordPlus | prediction | - |
dc.subject.keywordPlus | root mean squared error | - |
dc.subject.keywordPlus | temperature | - |
dc.subject.keywordPlus | temperature sensitivity | - |
dc.subject.keywordPlus | torque | - |
dc.subject.keywordPlus | velocity | - |
dc.subject.keywordPlus | Nitrogen oxides | - |
dc.subject.keywordAuthor | Artificial neural network | - |
dc.subject.keywordAuthor | On-road emission | - |
dc.subject.keywordAuthor | Onboard diagnostics data | - |
dc.subject.keywordAuthor | Portable emission measurement system | - |
dc.subject.keywordAuthor | Vehicle emission model | - |
dc.subject.keywordAuthor | Vehicle exhaust emission | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S1352231022005738?via%3Dihub | - |
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