Application of Soft Computing in the Field of Internal Combustion Engines: A Review

Shrivastava, Nitin; Khan, Zubair

doi:10.1007/s11831-017-9212-9

Cited by 41 publications

(15 citation statements)

References 93 publications

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“…High-precision instruments and equipment are required to measure the different operating parameters with these biofuels. These kinds of tests are expensive and time-consuming [23][24][25].…”

Section: Gepmentioning

confidence: 99%

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Bhatt

Shrivastava

2021

Arch Computat Methods Eng

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The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.

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Section: Gepmentioning

confidence: 99%

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Bhatt

Shrivastava

2021

Arch Computat Methods Eng

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“…The decoding of the genome‐ encoded information is known as translation. ETs and genome, with their distinct functions, help in generating a highly efficient algorithm compared to other modern adaptive methods 23,30,40 …”

Section: Modelingmentioning

confidence: 99%

Artificial intelligence‐based model prediction of biodiesel‐fueled engine performance and emission characteristics: A comparative evaluation of gene expression programming and artificial neural network

Sharma¹

2021

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The prognostic capability of gene expression programming (GEP) and artificial neural network (ANN) are compared to estimate the engine performance and emission characteristics. A stationary diesel engine was powered with linseed oil biodiesel‐mineral diesel blends. A total of 60 lab‐based test‐run were conducted by varying the engine input operating conditions, namely fuel injection parameters, diesel/biodiesel blending ratio, and engine load. The engine output data, namely brake thermal efficiency and brake‐specific fuel consumption, were calculated, while emission data for oxides of nitrogen, carbon monoxide, and unburnt hydrocarbon, were recorded. The experimental data were used for predictive model development using artificial intelligence‐based GEP and ANN techniques. The developed models were tested on statistical outcomes, such as the absolute fraction of variance (0.9698–0.997 for GEP and 0.9949–0.9998 for ANN), correlation coefficient (0.9848–0.998 for GEP and 0.9974–0.9998 for ANN), establishing these two models as an efficient machine identical tool. Also, Nash–Sutcliffe efficiency (0.937–0.9999 for GEP and 0.995–0.999 for ANN) and Kling–Gupta efficiency (0.834–0.9999 for GEP and 0.989–0.999 for ANN) elevate the prediction quality of developed models. The result showed that the ANN model was slightly more accurate than the GEP‐based model for the same parametric range.

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“…Os motores a combustão interna são máquinas térmicas que convertem a energia proveniente da queima de combustíveis em energia mecânica, em inúmeras aplicações, tais como, veículos automotores 18 , geração de energia elétrica 19 além de máquinas e equipamentos agrícolas 20 e industriais 21 . O desenvolvimento dos primeiros motores a combustão interna foi impulsionado pela Revolução Industrial, evento mundial histórico iniciado na Inglaterra na segunda metade do século XVIII, que culminou em mudanças no setor industrial com mecanização de processos e aplicação de máquinas a vapor em substituição de mão-de-obra humana e tração animal.…”

Section: Impactos Ambientais De Combustíveisunclassified

Qualidade de Combustíveis e as Novas Políticas Ambientais

Faria¹,

Silva²,

Cavalcanti³

et al. 2021

Rev. Proc. Q.

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Os combustíveis fósseis possuem origem não-renovável e são amplamente utilizados em todo o mundo, estando associados a uma grande parcela da emissão de gases poluentes na atmosfera terrestre. Existe uma demanda muito grande e crescente pela maior participação de fontes energéticas renováveis e menos poluentes na matriz energética mundial, sendo os biocombustíveis uma alternativa promissora e em crescente utilização no Brasil. A utilização de biocombustíveis em motores a combustão interna, apesar de favorecer a redução de poluentes, gera alguns problemas técnicos associados à sua menor estabilidade e suscetibilidade à degradação quando comparado a combustíveis fósseis. Como alternativa, aditivos podem ser aplicados de forma a garantir estabilidade e manutenção das propriedades físico-químicas dos biocombustíveis e suas misturas com combustíveis fósseis.

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Application of Soft Computing in the Field of Internal Combustion Engines: A Review

Cited by 41 publications

References 93 publications

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Artificial intelligence‐based model prediction of biodiesel‐fueled engine performance and emission characteristics: A comparative evaluation of gene expression programming and artificial neural network

Qualidade de Combustíveis e as Novas Políticas Ambientais

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