Application of artificial intelligence (AI) in characterization of the performance–emission profile of a single cylinder CI engine operating with hydrogen in dual fuel mode: An ANN approach with fuzzy-logic based topology optimization

Deb, Madhujit; Majumder, Pinki; Majumder, Arindam; Roy, Sumit; Banerjee, Rahul

doi:10.1016/j.ijhydene.2016.07.016

Cited by 62 publications

(14 citation statements)

References 46 publications

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“…In the current study, ANN was applied to estimate the emission gases such as CO, CO 2 , NO x , HC, and smoke density for all blend samples. Feed forward backpropagation in combination with "TRAINLM" training function has been used as algorithm, and mean square error treated as performance function [11,12,[19][20][21][22][23][24][25].…”

Section: Analysis Using Annmentioning

confidence: 99%

“…Among all, both input and output layers require the particulars from the experimental statistics to create the network architecture and simulation of the system. Levenberg-Marquardt (Trainlm) training function has been practiced for error-free evaluation, in which mean square error decides the dereliction consequence of the network [20]. Most of the experimental investigations have been carried out on various engines with the application of ANN for optimizing the outcomes [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Analysis on Emissions and Performance of Ceramic Coated Diesel Engine Fueled with Novel Blends Using Artificial Intelligence

Kotteda¹,

Chekuri²,

raju³

et al. 2021

Advances in Materials Science and Engineering

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The exhaustive nature of petroleum products triggers the obstacles of scarcity, economic imbalance, and environmental depletion. It is difficult to avoid their usage all of a sudden and switch to clean electric prime movers. Under all these circumstances, the researchers may initiate their investigations on alternative fuels for preeminent solution. The present study covers the performance and emissions of a single cylinder, four-stroke, diesel engine fueled with Pongamia pinnata and Calophyllum inophyllum biodiesels added with n-butanol additive at various proportions. In this investigation, the piston has been coated with ceramic material with a thickness of 200 µm topcoat. The blends have been tested at 1500 rpm speed and rated compression ratio of 17.5 : 1 at various operating loads. A comparative result analysis has been made on the engine parameters operated by diesel and showed that mechanical efficiency gradually increases with a percentile increment of n-butanol in the blend. Moreover, emissions such as CO, CO2, NOx, and opacity were found to be reduced for the samples having high amount of n-butanol, whereas HC emissions slightly increased. In addition, all the exhaust gases have been predicted by using second-order polynomial equations generated and artificial Intelligence technique, and the comparative analysis has been made. It has been identified that ANN showed an average accuracy of prediction superior than regression analysis.

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Section: Analysis Using Annmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis on Emissions and Performance of Ceramic Coated Diesel Engine Fueled with Novel Blends Using Artificial Intelligence

Kotteda¹,

Chekuri²,

raju³

et al. 2021

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Deb et al [ 128 ] developed an ANN model to predict the performance and emission parameters of the diesel engine using hydrogen in dual fuel mode. 2-15-15-6 network architecture, along with logsig-tansig activation function, was used.…”

Section: Modeling Of Internal Combustion Enginesmentioning

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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“…Benefiting from the improvement of algorithms [1] and the emergence of various types of ANNs designed for different purposes [2], ANN's applications in the field of deep learning have gradually expanded to the industry, e.g. emission control of engines [3,4] and modeling of engine characteristics [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network based chemical mechanisms for computationally efficient modeling of hydrogen/carbon monoxide/kerosene combustion

Luo

et al. 2020

International Journal of Hydrogen Energy

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The high cost of high-resolution computational fluid/flame dynamics (CFD) has hindered its application in combustion related research, design and optimization. In this study, we propose a framework for turbulent combustion simulation based on the deep learning approach. An optimized deep convolutional neural network (CNN) inspired from a U-Net architecture and inception module is designed for constructing the deep learning solver, named CFDNN. CFDNN is then trained on simulation results of hydrogen combustion in a cavity with different inlet velocities. After training, CFDNN can not only accurately predict the flow and combustion fields within the range of the training set, but also shows an extrapolation ability for prediction outside the training set. The results from the CFDNN solver show excellent consistency with the conventional CFD results in terms of both predicted spatial distributions and temporal dynamics. Meanwhile, two orders of magnitude of acceleration are achieved by using the CFDNN solver compared to a conventional CFD solver. The successful development of such a deep learning-based solver opens up new possibilities of low-cost, high-accuracy simulations, fast prototyping, design optimization and real-time control of combustion systems such as gas turbines and scramjets.

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Application of artificial intelligence (AI) in characterization of the performance–emission profile of a single cylinder CI engine operating with hydrogen in dual fuel mode: An ANN approach with fuzzy-logic based topology optimization

Cited by 62 publications

References 46 publications

[Retracted] Analysis on Emissions and Performance of Ceramic Coated Diesel Engine Fueled with Novel Blends Using Artificial Intelligence

[Retracted] Analysis on Emissions and Performance of Ceramic Coated Diesel Engine Fueled with Novel Blends Using Artificial Intelligence

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

Artificial neural network based chemical mechanisms for computationally efficient modeling of hydrogen/carbon monoxide/kerosene combustion

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