Engine performance and emission optimization with waste cooking oil biodiesel/diesel blend using ANN and RSM techniques coupled with ACKTR-DE and HHO algorithms
Abstract In this experimental investigation, Artificial Neural Network (ANN) and Response Surface Methodology (RSM) model structures were constructed to predict and optimize the performance and exhaust emissions of a diesel engine operating on a blend of diesel fuel and waste oil biodiesel. The test...
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2025-05-01
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| Online Access: | https://doi.org/10.1186/s13065-025-01512-3 |
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| author | Mehmet Ali Biberci Mustafa Bahattin Çelik Esma Ozhuner |
| author_facet | Mehmet Ali Biberci Mustafa Bahattin Çelik Esma Ozhuner |
| author_sort | Mehmet Ali Biberci |
| collection | DOAJ |
| description | Abstract In this experimental investigation, Artificial Neural Network (ANN) and Response Surface Methodology (RSM) model structures were constructed to predict and optimize the performance and exhaust emissions of a diesel engine operating on a blend of diesel fuel and waste oil biodiesel. The test engine was operated with 0%, 50%, and 100% biodiesel content under varying injection pressures and loads. The RSM model was used to derive regression equations from the experimental results. The correlation coefficient (R2) for all responses of the constructed model ranged from 0.9785 to 0.9997. By applying the developed model, the brake thermal efficiency (BTE) response was optimized to its maximum value, while all other responses were minimized. All responses were predicted using an ANN model with R > 0.99 and a maximum mean absolute error (MAAE) of 1.723%. RSM-based optimization analysis was applied to the design of experiments (DOE). At an injection pressure of 180 bar, an engine torque of 3.846 Nm, and a 100 percent biodiesel ratio, optimal diesel engine performance characteristics, the lowest exhaust emissions, and the lowest specific fuel consumption values were achieved. In addition, the RSM approach performed satisfactorily, with a desirability value of 0.750. The RSM regression equations were assessed using the Actor Critic with Kronecker-Factored Trust Region-Differential Evolution (ACKTR-DE) and Harris Hawks Optimization (HHO) algorithms. The outcomes derived from the ACKTR-DE and HHO algorithms corroborated the results obtained from the RSM. Furthermore, verification experiments were conducted to confirm the optimal results, thus demonstrating that the combined use of RSM, ANN, and advanced algorithms offers a robust and accurate framework for optimizing biodiesel engine performance. Graphical Abstract |
| format | Article |
| id | doaj-art-6ad6815da2fa4ab6a6163cca404909f5 |
| institution | DOAJ |
| issn | 2661-801X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Chemistry |
| spelling | doaj-art-6ad6815da2fa4ab6a6163cca404909f52025-08-20T03:08:21ZengBMCBMC Chemistry2661-801X2025-05-0119111910.1186/s13065-025-01512-3Engine performance and emission optimization with waste cooking oil biodiesel/diesel blend using ANN and RSM techniques coupled with ACKTR-DE and HHO algorithmsMehmet Ali Biberci0Mustafa Bahattin Çelik1Esma Ozhuner2Department of Mechanical Engineering, Çankırı Karatekin UniversityDepartment of Mechanical Engineering, Karabuk UniversityDepartment of Plant and Animal Production, Food and Agriculture Vocational School, Çankırı Karatekin UniversityAbstract In this experimental investigation, Artificial Neural Network (ANN) and Response Surface Methodology (RSM) model structures were constructed to predict and optimize the performance and exhaust emissions of a diesel engine operating on a blend of diesel fuel and waste oil biodiesel. The test engine was operated with 0%, 50%, and 100% biodiesel content under varying injection pressures and loads. The RSM model was used to derive regression equations from the experimental results. The correlation coefficient (R2) for all responses of the constructed model ranged from 0.9785 to 0.9997. By applying the developed model, the brake thermal efficiency (BTE) response was optimized to its maximum value, while all other responses were minimized. All responses were predicted using an ANN model with R > 0.99 and a maximum mean absolute error (MAAE) of 1.723%. RSM-based optimization analysis was applied to the design of experiments (DOE). At an injection pressure of 180 bar, an engine torque of 3.846 Nm, and a 100 percent biodiesel ratio, optimal diesel engine performance characteristics, the lowest exhaust emissions, and the lowest specific fuel consumption values were achieved. In addition, the RSM approach performed satisfactorily, with a desirability value of 0.750. The RSM regression equations were assessed using the Actor Critic with Kronecker-Factored Trust Region-Differential Evolution (ACKTR-DE) and Harris Hawks Optimization (HHO) algorithms. The outcomes derived from the ACKTR-DE and HHO algorithms corroborated the results obtained from the RSM. Furthermore, verification experiments were conducted to confirm the optimal results, thus demonstrating that the combined use of RSM, ANN, and advanced algorithms offers a robust and accurate framework for optimizing biodiesel engine performance. Graphical Abstracthttps://doi.org/10.1186/s13065-025-01512-3ANN and multi-objective RSM optimization techniquesFuel blendDiesel engineExhaust emissionsHHO and ACKTR-DE optimization algorithmsBiodiesel |
| spellingShingle | Mehmet Ali Biberci Mustafa Bahattin Çelik Esma Ozhuner Engine performance and emission optimization with waste cooking oil biodiesel/diesel blend using ANN and RSM techniques coupled with ACKTR-DE and HHO algorithms BMC Chemistry ANN and multi-objective RSM optimization techniques Fuel blend Diesel engine Exhaust emissions HHO and ACKTR-DE optimization algorithms Biodiesel |
| title | Engine performance and emission optimization with waste cooking oil biodiesel/diesel blend using ANN and RSM techniques coupled with ACKTR-DE and HHO algorithms |
| title_full | Engine performance and emission optimization with waste cooking oil biodiesel/diesel blend using ANN and RSM techniques coupled with ACKTR-DE and HHO algorithms |
| title_fullStr | Engine performance and emission optimization with waste cooking oil biodiesel/diesel blend using ANN and RSM techniques coupled with ACKTR-DE and HHO algorithms |
| title_full_unstemmed | Engine performance and emission optimization with waste cooking oil biodiesel/diesel blend using ANN and RSM techniques coupled with ACKTR-DE and HHO algorithms |
| title_short | Engine performance and emission optimization with waste cooking oil biodiesel/diesel blend using ANN and RSM techniques coupled with ACKTR-DE and HHO algorithms |
| title_sort | engine performance and emission optimization with waste cooking oil biodiesel diesel blend using ann and rsm techniques coupled with acktr de and hho algorithms |
| topic | ANN and multi-objective RSM optimization techniques Fuel blend Diesel engine Exhaust emissions HHO and ACKTR-DE optimization algorithms Biodiesel |
| url | https://doi.org/10.1186/s13065-025-01512-3 |
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