Artificial Neural Network modeling of a hydrogen dual fueled diesel engine characteristics: An experiment approach

Javed, Syed; Baig, Rahmath Ulla; Algarni, Salem; Murthy, Y.V.V. Satyanarayana; Masood, M.; Inamurrahman, Mohammed

doi:10.1016/j.ijhydene.2017.04.096

Cited by 48 publications

(12 citation statements)

References 39 publications

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“…As indicated by the R 2 of .981, the 2‐LMLP neural network model can generalize 98.1% of the data set with minimal residuals (Figure 8B). The MLP model performance in this study is consistent with that reported by Karaci et al, 37 Syed et al, 38 Alsaffar et al, 39 and Mageed et al 40 for modeling hydrogen dual fueled diesel engine characteristic, hydrogen‐rich syngas production from pyrolysis, prediction of carbon deposition from methane dry reforming and hydrogen‐rich syngas production from bio‐oil and glycerol pyrolysis, respectively. The one‐layer MLP neural network model reported by Syed et al 38 was robust in predicting the characteristics of the hydrogen dual fueled diesel with a minimal prediction error.…”

Section: Resultssupporting

confidence: 89%

“…The MLP model performance in this study is consistent with that reported by Karaci et al, 37 Syed et al, 38 Alsaffar et al, 39 and Mageed et al 40 for modeling hydrogen dual fueled diesel engine characteristic, hydrogen‐rich syngas production from pyrolysis, prediction of carbon deposition from methane dry reforming and hydrogen‐rich syngas production from bio‐oil and glycerol pyrolysis, respectively. The one‐layer MLP neural network model reported by Syed et al 38 was robust in predicting the characteristics of the hydrogen dual fueled diesel with a minimal prediction error. As reported by Karaci et al, 37 the MLP neural network model efficiently predicted hydrogen‐rich syngas as a function of product types, catalyst types, catalyst amount, and the reaction temperature.…”

Section: Resultssupporting

confidence: 89%

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Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms

et al. 2021

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This study aimed to investigate the application of radial basis function (RBF) and multilayer perceptron (MLP) artificial neural networks for modeling hydrogen production by co-gasification of rubber and plastic wastes. Both the RBF and MLP neural networks were configured by determining the besthidden neurons that could offer optimized performance. Based on the besthidden neurons, a model architecture of 4-16-1, 4-20-1, 4-17-1, and 4-3-1 was obtained for RBF (with standard activation function), RBF (with ordinary activation function), one-layer MLP, and two-layer MLP, respectively, indicating the number of input nodes, the hidden neurons, and the output nodes. The predicted hydrogen production from the co-gasification closely agrees with the observed values. The 1-layer MLP with R 2 of .990 displayed the best performance with all the input parameters having a significant influence on 9 the model output. The neural network algorithm obtained in this study could be implemented in the eventuality of making a vital decision in the process operation of the co-gasification process for hydrogen production.

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Section: Resultssupporting

confidence: 89%

Section: Resultssupporting

confidence: 89%

Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms

et al. 2021

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“…Dynamic changes were also described by the dynamic model-based ANN algorithm [28]. In some previous studies, the SVM algorithm has already been combined with PLSR and ANN, respectively [37][38][39][40][41][42][43][44][45][46][47][48]. Before we fully understood the change mechanism of underground CO 2 concentration, all the above regression methods were reasonable based on our current knowledge.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Environmental Controls on the Quasi-Ocean and Ocean CO2 Concentration by Two Intelligent Algorithms

Zhou

Wang

et al. 2021

Mathematical Problems in Engineering

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Previous studies have demonstrated CO2 absorption by soils in arid regions, where the absorbed CO2 is conjectured to be finally sequestrated in the “subterranean ocean”—groundwater. This study compares environmental controls of ocean CO2 concentration (surface ocean pCO2) and quasi-ocean CO2 concentration (deep-soil pCO2). We aim to explore the latent relationship, both linear and nonlinear between the environmental variables, and CO2 concentration, utilizing two intelligent algorithms—the partial least linear regression (PLSR) algorithm and the artificial neural network (ANN) algorithm. For quasi-ocean CO2 concentration, RPD <1.4 and R2 <40%. While for ocean CO2 concentration, RPD >1.4 and R2 is 99.7%. Linear relationships between the considered environmental controls and ocean CO2 concentration are proved; however, there is no evident relationship between most of the considered environmental controls and quasi-ocean CO2 concentration. Groundwater level is proved to be a relatively important environmental control on the quasi-ocean CO2 concentration, suggesting groundwater discharge/recharge as a significant modulator of soil CO2 absorption in arid regions.

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“…Syed et al [ 111 ] in a similar study, compared the seven learning algorithms in combination with three transfer functions to evaluate an ANN model developed for hydrogen diesel dual-fuel mode engine. A comparative matrix with Seven training algorithm viz; traingdx, trainlm, trainrp, traingda, traincgf, trainbfg, and trainscg along with three transfer functions viz; purelin, logsig, and tansig were prepared for 16 data sets.…”

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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Artificial Neural Network modeling of a hydrogen dual fueled diesel engine characteristics: An experiment approach

Cited by 48 publications

References 39 publications

Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms

Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms

Analysis of Environmental Controls on the Quasi-Ocean and Ocean CO2 Concentration by Two Intelligent Algorithms

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

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