Machine learning of weighted superposition attraction algorithm for optimization diesel engine performance and emission fueled with butanol-diesel biofuel
Machine learning (ML) is a subset of artificial intelligence (AI) and computer science that employs data and algorithms and mimics human learning to self-enhance its accuracy. In biofuel research, butanol is widely recognized as a prospective alternative biofuel. Butanol addition in diesel or combus...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Ain Shams Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447924005070 |
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| author | Ibham Veza Aslan Deniz Karaoglan Sener Akpinar Martin Spraggon Muhammad Idris |
| author_facet | Ibham Veza Aslan Deniz Karaoglan Sener Akpinar Martin Spraggon Muhammad Idris |
| author_sort | Ibham Veza |
| collection | DOAJ |
| description | Machine learning (ML) is a subset of artificial intelligence (AI) and computer science that employs data and algorithms and mimics human learning to self-enhance its accuracy. In biofuel research, butanol is widely recognized as a prospective alternative biofuel. Butanol addition in diesel or combustion engine has been more and more studied recently. Gaining a comprehensive comprehension of butanol performance and emission characteristics using machine learning approach is an essential milestone in investigating alcohol-based biofuel addition in diesel engines. However, few studies investigated butanol effect on diesel engine emissions using machine learning for optimization. A novel optimization study is needed. This work aims to investigate the newly developed and efficient machine learning, weighted superposition attraction (WSA) algorithm, to optimize the emission and performance of diesel engines fuelled with butanol-diesel biofuel. Mathematical modeling between the factors (butanol (vol.%) and BMEP (bar)) and the responses (BTE (%), BSFC (g/kWh), Exhaust Temperature Texh (oC), NOx (g/kWh), CO (g/kWh), HC (g/kWh), and Smoke Opacity (%)) are governed using regression modeling. The optimized and best factor levels are determined employing the machine learning of WSA Algorithm. Confirmations are carried out. Optimization results indicate that the BTE is maximized, and the remainder of the responses are minimized. |
| format | Article |
| id | doaj-art-11c2505672014b4099a234c869c0ffe0 |
| institution | Kabale University |
| issn | 2090-4479 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-11c2505672014b4099a234c869c0ffe02024-12-18T08:48:27ZengElsevierAin Shams Engineering Journal2090-44792024-12-011512103126Machine learning of weighted superposition attraction algorithm for optimization diesel engine performance and emission fueled with butanol-diesel biofuelIbham Veza0Aslan Deniz Karaoglan1Sener Akpinar2Martin Spraggon3Muhammad Idris4Department of Mechanical Engineering, Faculty of Engineering, Universitas Bung Karno, Jl. Kimia No. 20. Menteng, Jakarta 10320, Indonesia; Corresponding author.Balikesir University, Department of Industrial Engineering, 10145, Balikesir, TurkeyDokuz Eylul University, Department of Industrial Engineering, 35397, Izmir, TurkeyCanadian University Dubai, United Arab EmiratesSchool of Environmental Science, University of Indonesia, Jakarta 10430, IndonesiaMachine learning (ML) is a subset of artificial intelligence (AI) and computer science that employs data and algorithms and mimics human learning to self-enhance its accuracy. In biofuel research, butanol is widely recognized as a prospective alternative biofuel. Butanol addition in diesel or combustion engine has been more and more studied recently. Gaining a comprehensive comprehension of butanol performance and emission characteristics using machine learning approach is an essential milestone in investigating alcohol-based biofuel addition in diesel engines. However, few studies investigated butanol effect on diesel engine emissions using machine learning for optimization. A novel optimization study is needed. This work aims to investigate the newly developed and efficient machine learning, weighted superposition attraction (WSA) algorithm, to optimize the emission and performance of diesel engines fuelled with butanol-diesel biofuel. Mathematical modeling between the factors (butanol (vol.%) and BMEP (bar)) and the responses (BTE (%), BSFC (g/kWh), Exhaust Temperature Texh (oC), NOx (g/kWh), CO (g/kWh), HC (g/kWh), and Smoke Opacity (%)) are governed using regression modeling. The optimized and best factor levels are determined employing the machine learning of WSA Algorithm. Confirmations are carried out. Optimization results indicate that the BTE is maximized, and the remainder of the responses are minimized.http://www.sciencedirect.com/science/article/pii/S2090447924005070Machine learning optimizationMachine learning for butanol-diesel biofuel optimizationMachine learning for diesel engine performance optimizationMachine learning for diesel engine emission optimizationWeighted superposition attraction (WSA) machine learning algorithmMachine learning for optimization diesel engine performance and emission |
| spellingShingle | Ibham Veza Aslan Deniz Karaoglan Sener Akpinar Martin Spraggon Muhammad Idris Machine learning of weighted superposition attraction algorithm for optimization diesel engine performance and emission fueled with butanol-diesel biofuel Ain Shams Engineering Journal Machine learning optimization Machine learning for butanol-diesel biofuel optimization Machine learning for diesel engine performance optimization Machine learning for diesel engine emission optimization Weighted superposition attraction (WSA) machine learning algorithm Machine learning for optimization diesel engine performance and emission |
| title | Machine learning of weighted superposition attraction algorithm for optimization diesel engine performance and emission fueled with butanol-diesel biofuel |
| title_full | Machine learning of weighted superposition attraction algorithm for optimization diesel engine performance and emission fueled with butanol-diesel biofuel |
| title_fullStr | Machine learning of weighted superposition attraction algorithm for optimization diesel engine performance and emission fueled with butanol-diesel biofuel |
| title_full_unstemmed | Machine learning of weighted superposition attraction algorithm for optimization diesel engine performance and emission fueled with butanol-diesel biofuel |
| title_short | Machine learning of weighted superposition attraction algorithm for optimization diesel engine performance and emission fueled with butanol-diesel biofuel |
| title_sort | machine learning of weighted superposition attraction algorithm for optimization diesel engine performance and emission fueled with butanol diesel biofuel |
| topic | Machine learning optimization Machine learning for butanol-diesel biofuel optimization Machine learning for diesel engine performance optimization Machine learning for diesel engine emission optimization Weighted superposition attraction (WSA) machine learning algorithm Machine learning for optimization diesel engine performance and emission |
| url | http://www.sciencedirect.com/science/article/pii/S2090447924005070 |
| work_keys_str_mv | AT ibhamveza machinelearningofweightedsuperpositionattractionalgorithmforoptimizationdieselengineperformanceandemissionfueledwithbutanoldieselbiofuel AT aslandenizkaraoglan machinelearningofweightedsuperpositionattractionalgorithmforoptimizationdieselengineperformanceandemissionfueledwithbutanoldieselbiofuel AT senerakpinar machinelearningofweightedsuperpositionattractionalgorithmforoptimizationdieselengineperformanceandemissionfueledwithbutanoldieselbiofuel AT martinspraggon machinelearningofweightedsuperpositionattractionalgorithmforoptimizationdieselengineperformanceandemissionfueledwithbutanoldieselbiofuel AT muhammadidris machinelearningofweightedsuperpositionattractionalgorithmforoptimizationdieselengineperformanceandemissionfueledwithbutanoldieselbiofuel |