Artificial neural networks vs. gene expression programming for predicting emission & engine efficiency of SI operated on blends of gasoline-methanol-hydrogen fuel
While retaining environmental friendliness, robust modelling and enhancing spark ignition engine efficacy can be done using improved innovative fuel and unconventional robust hybrid tools. This study is the first to employ Al techniques such as artificial neural networks (ANN) and gene expression pr...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X2300415X |
| _version_ | 1797729923752787968 |
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| author | Chao-zhe Zhu Olusegun D. Samuel Noureddine Elboughdiri Mohamed Abbas C Ahamed Saleel Nataraj Ganesan Christopher C. Enweremadu H. Fayaz |
| author_facet | Chao-zhe Zhu Olusegun D. Samuel Noureddine Elboughdiri Mohamed Abbas C Ahamed Saleel Nataraj Ganesan Christopher C. Enweremadu H. Fayaz |
| author_sort | Chao-zhe Zhu |
| collection | DOAJ |
| description | While retaining environmental friendliness, robust modelling and enhancing spark ignition engine efficacy can be done using improved innovative fuel and unconventional robust hybrid tools. This study is the first to employ Al techniques such as artificial neural networks (ANN) and gene expression programming (GEP) to predict the performance and emissions of a gasohol/hydrogen-powered SI engine. The ANN was adopted to correlate the engine variables viz. engine speed and gasohol/hydrogen mix vs. responses namely brake thermal efficiency (BTE), brake specific energy consumption (BSEC), carbon monoxide (CO), hydrocarbon (HC), oxides of nitrogen (NOx) and carbon dioxide (CO2). GEP model was further employed to predict BTE, BSEC, CO, HC, NOx and CO2. To examine the prediction efficacy of both AI techniques, a set of advanced statistical approaches was used. A set of advanced statistical techniques were employed to test the prediction efficiency of both AI techniques. It was revealed that ANN outperformed the GEP since the values for R in the case of ANN were 0.9864–0.9998 whereas the values for R in the case of GEP were 0.9864–0.9994. Similarly, in the instance of R2, ANN outperformed GEP. Furthermore, Kling-Gupta efficiency was greater in the case of ANN (0.9684–0.9999) than in GEP (0.8912–0.9991). Both AI approaches, however, displayed great prognostic effectiveness in forecasting engine performance and emissions. |
| first_indexed | 2024-03-12T11:37:35Z |
| format | Article |
| id | doaj.art-9da819aca38b4f0db82ba99670601876 |
| institution | Directory Open Access Journal |
| issn | 2214-157X |
| language | English |
| last_indexed | 2024-03-12T11:37:35Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj.art-9da819aca38b4f0db82ba996706018762023-09-01T05:01:11ZengElsevierCase Studies in Thermal Engineering2214-157X2023-09-0149103109Artificial neural networks vs. gene expression programming for predicting emission & engine efficiency of SI operated on blends of gasoline-methanol-hydrogen fuelChao-zhe Zhu0Olusegun D. Samuel1Noureddine Elboughdiri2Mohamed Abbas3C Ahamed Saleel4Nataraj Ganesan5Christopher C. Enweremadu6H. Fayaz7School of Medical Engineering, Jining Medical University, Jining City, Shandong Province, ChinaDepartment of Mechanical Engineering, Federal University of Petroleum Resources, Effurun, P.M.B 1221, Delta State, Nigeria; Department of Mechanical Engineering, University of South Africa, Science Campus, Private Bag X6, Florida, 1709, South Africa; Corresponding author. Department of Mechanical Engineering, Federal University of Petroleum Resources, Effurun, P.M.B 1221, Delta State, Nigeria.Chemical Engineering Department, College of Engineering, University of Ha'il, P.O. Box 2440, Ha'il, 81441, Saudi Arabia; Chemical Engineering Process Department, National School of Engineers Gabes, University of Gabes, Gabes, 6029, TunisiaElectrical Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi ArabiaDepartment of Mechanical Engineering, College of Engineering, King Khalid University, PO Box 394, Abha, 61421, Saudi ArabiaSchool of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaDepartment of Mechanical Engineering, University of South Africa, Science Campus, Private Bag X6, Florida, 1709, South AfricaModeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet NamWhile retaining environmental friendliness, robust modelling and enhancing spark ignition engine efficacy can be done using improved innovative fuel and unconventional robust hybrid tools. This study is the first to employ Al techniques such as artificial neural networks (ANN) and gene expression programming (GEP) to predict the performance and emissions of a gasohol/hydrogen-powered SI engine. The ANN was adopted to correlate the engine variables viz. engine speed and gasohol/hydrogen mix vs. responses namely brake thermal efficiency (BTE), brake specific energy consumption (BSEC), carbon monoxide (CO), hydrocarbon (HC), oxides of nitrogen (NOx) and carbon dioxide (CO2). GEP model was further employed to predict BTE, BSEC, CO, HC, NOx and CO2. To examine the prediction efficacy of both AI techniques, a set of advanced statistical approaches was used. A set of advanced statistical techniques were employed to test the prediction efficiency of both AI techniques. It was revealed that ANN outperformed the GEP since the values for R in the case of ANN were 0.9864–0.9998 whereas the values for R in the case of GEP were 0.9864–0.9994. Similarly, in the instance of R2, ANN outperformed GEP. Furthermore, Kling-Gupta efficiency was greater in the case of ANN (0.9684–0.9999) than in GEP (0.8912–0.9991). Both AI approaches, however, displayed great prognostic effectiveness in forecasting engine performance and emissions.http://www.sciencedirect.com/science/article/pii/S2214157X2300415XANNGene expression programmingModellingGasoholHydrogenSpark ignition engine |
| spellingShingle | Chao-zhe Zhu Olusegun D. Samuel Noureddine Elboughdiri Mohamed Abbas C Ahamed Saleel Nataraj Ganesan Christopher C. Enweremadu H. Fayaz Artificial neural networks vs. gene expression programming for predicting emission & engine efficiency of SI operated on blends of gasoline-methanol-hydrogen fuel Case Studies in Thermal Engineering ANN Gene expression programming Modelling Gasohol Hydrogen Spark ignition engine |
| title | Artificial neural networks vs. gene expression programming for predicting emission & engine efficiency of SI operated on blends of gasoline-methanol-hydrogen fuel |
| title_full | Artificial neural networks vs. gene expression programming for predicting emission & engine efficiency of SI operated on blends of gasoline-methanol-hydrogen fuel |
| title_fullStr | Artificial neural networks vs. gene expression programming for predicting emission & engine efficiency of SI operated on blends of gasoline-methanol-hydrogen fuel |
| title_full_unstemmed | Artificial neural networks vs. gene expression programming for predicting emission & engine efficiency of SI operated on blends of gasoline-methanol-hydrogen fuel |
| title_short | Artificial neural networks vs. gene expression programming for predicting emission & engine efficiency of SI operated on blends of gasoline-methanol-hydrogen fuel |
| title_sort | artificial neural networks vs gene expression programming for predicting emission amp engine efficiency of si operated on blends of gasoline methanol hydrogen fuel |
| topic | ANN Gene expression programming Modelling Gasohol Hydrogen Spark ignition engine |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X2300415X |
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