An Intelligent Artificial Neural Network-Response Surface Methodology Method for Accessing the Optimum Biodiesel and Diesel Fuel Blending Conditions in a Diesel Engine from the Viewpoint of Exergy and Energy Analysis

Najafi, Bahman; Ardabili, Sina Faizollahzadeh; Mosavi, Amir; Shamshirband, Shahaboddin; Rabczuk, Timon

doi:10.3390/en11040860

Cited by 78 publications

(41 citation statements)

References 38 publications

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“…Modeling is a scientific approach and essential part of many scientific disciplines to represent ideas about the natural of the phenomenon under investigation from the viewpoint of science and to present an alternative to the real phenomenon, to quantify, define, visualize, or simulate it by referring to the existing knowledge. There are several types of modeling approaches, among which the most widely used are mathematical and intelligent modeling approaches [10].…”

Section: Modelingmentioning

confidence: 99%

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

Gendy¹,

Ghoneim²,

Zakhary³

2020

WJET

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The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabilized confined jet diffusion flames in the presence of different geometries of bluff-body burners. Two stabilizer disc burners tapered at 30˚ and 60˚ and another frustum cone of 60˚/30˚ inclination angle were employed all having the same diameter of 80 (mm) acting as flame holders. The measured radial mean temperature profiles of the developed stabilized flames at different normalized axial distances () j x d were considered as the model example of the physical process. The RSM and ANN methods analyze the effect of the two operating parameters namely () r , the radial distance from the center line of the flame, and () j x d on the measured temperature of the flames, to find the predicted maximum temperature and the corresponding process variables. A three-layered Feed Forward Neural Network in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function and the optimized topology of 2:10:1 (input neurons: hidden neurons: output neurons) was developed. Also the ANN method has been employed to illustrate such effects in the three and two dimensions and shows the location of the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of R 2 and F Ratio are 0.868-0.947 and 231.7-864.1 for RSM method compared to 0.964-0.987 and 2878.8 7580.7 for ANN method beside lower values for error analysis terms.

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Section: Modelingmentioning

confidence: 99%

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

Gendy¹,

Ghoneim²,

Zakhary³

2020

WJET

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“…In particular, in ref. [21] an ANN-based model was developed and applied to predict the variation of energy efficiency, exergy efficiency and exhaust temperature based on biodiesel type and engine load. In ref.…”

Section: State Of the Art In Low-throughput Combustion Modeling For Mmentioning

confidence: 99%

Real-Time Simulation of Torque and Nitrogen Oxide Emissions in an 11.0 L Heavy-Duty Diesel Engine for Model-Based Combustion Control

Finesso

Hardy²,

Mancarella

et al. 2019

Energies

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A real-time combustion model was assessed and applied to simulate BMEP (Brake Mean Effective Pressure) and NOx (Nitrogen Oxide) emissions in an 11.0 L FPT Cursor 11 diesel engine for heavy-duty applications. The activity was carried out in the frame of the IMPERIUM H2020 EU Project. The developed model was used as a starting base to derive a model-based combustion controller, which is able to control indicated mean effective pressure and NOx emissions by acting on the injected fuel quantity and main injection timing. The combustion model was tested and assessed at steady-state conditions and in transient operation over several load ramps. The average root mean square error of the model is of the order of 110 ppm for the NOx simulation and of 0.3 bar for the BMEP simulation Moreover, a statistical robustness analysis was performed on the basis of the expected input parameter deviations, and a calibration sensitivity analysis was carried out, which showed that the accuracy is almost unaffected when reducing the calibration dataset by about 80%. The model was also tested on a rapid prototyping device and it was verified that it features real-time capability, since the computational time is of the order of 300–400 µs. Finally, the basic functionality of the model-based combustion controller was tested offline at steady-state conditions.

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“…Moreover, for processing ELM algorithm no considerable human intervention is required, consequently, it can run considerably more rapidly than the ordinary procedures. It was demonstrated that by applying the ELM, the learning procedure develops fast with leading to a tough performance (Mosavi & Rabczuk, 2017a;Najafi, Faizollahzadeh Ardabili, Mosavi, Shamshirband, & Rabczuk, 2018). In view of that, several research works have been effectively conceded utilizing ELM algorithm for answering the complications in a number of scientific arenas.…”

Section: Extreme Learning Machinementioning

confidence: 99%

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

Ghazvinei

Darvishi

Mosavi

et al. 2018

Engineering Applications of Computational Fluid Mechanics

Self Cite

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Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production datadriven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R 2 ) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results. ARTICLE HISTORY

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An Intelligent Artificial Neural Network-Response Surface Methodology Method for Accessing the Optimum Biodiesel and Diesel Fuel Blending Conditions in a Diesel Engine from the Viewpoint of Exergy and Energy Analysis

Cited by 78 publications

References 38 publications

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

Real-Time Simulation of Torque and Nitrogen Oxide Emissions in an 11.0 L Heavy-Duty Diesel Engine for Model-Based Combustion Control

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

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