Application of ANN, RSM on engine response prediction using lemongrass biomaterial blends

Prakash, P.; Dhanasekaran, C.

doi:10.1016/j.matpr.2022.07.116

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…The training process for the ANN model was finished after 1000 epoch. The model is now reporting a gradient of and an error rate of 0 (Prakash and Dhanasekaran 2022 ; 2023 ). In addition, the validation data were also checked over a thousand times.…”

Section: Methodsmentioning

confidence: 99%

Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach

Rajak,

Chaurasiya,

Verma

et al. 2024

Environ Sci Pollut Res

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This article presents the outcomes of a research study focused on optimizing the performance of soybean biofuel blends derived from soybean seeds specifically for urban medium-duty commercial vehicles. The study took into consideration elements such as production capacity, economics and assumed engine characteristics. For the purpose of predicting performance, combustion and emission characteristics, an artificial intelligence approach that has been trained using experimental data is used. At full load, the brake thermal efficiency (BTE) dropped as engine speed increased for biofuel and diesel fuel mixes, but brake-specific fuel consumption (BSFC) increased. The BSFC increased by 11.9% when diesel compared to using biofuel with diesel blends. The mixes cut both maximum cylinder pressure and $${{\text{NO}}}_{{\text{x}}}$$ NO x emissions. The biofuel-diesel fuel proved more successful, with maximum reduction of 9.8% and 22.2 at rpm, respectively. The biofuel and diesel blend significantly improved carbon dioxide ($${{\text{CO}}}_{2}$$ CO 2 ) and smoke emissions. The biofuel blends offer significant advantages by decreeing exhaust pollutants and enhancing engine performance. Graphical Abstract

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Section: Methodsmentioning

confidence: 99%