Developing of ANN model for prediction of performance and emission characteristics of VCR engine with orange oil biodiesel blends

Karthickeyan, V.; Balamurugan, P.; Rohith, G; Senthil, Ramalingam

doi:10.1007/s40430-017-0768-y

Cited by 45 publications

(9 citation statements)

References 29 publications

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“…The application of ANN was recommended for the estimation of NO x emissions from both EGR engines and non-EGR engines. Karthickeyan et al [110] used ANN model for estimation of performance and emissions characteristics from engine using orange oil-based biodiesel. Orange oil Methyl Ester (OME) with the VCR engine demonstrated higher efficiency and lesser fuel consumption.…”

Section: Consumption Engine Performance and Emissionsmentioning

confidence: 99%

Machine Learning Applications in Biofuels' Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Ahmad¹,

Kano²,

Menezes³

et al. 2020

Preprint

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Machine learning (ML) is penetrating in all walks of life and is one of the major driving forces behind the fourth industrial revolution, typically known as Industry 4.0. The purpose of the present study is to review the state-of-the-art ML applications in the biofuels' life cycle stages, i.e., soil, feedstock, production, consumption, and emissions. A keyword search is performed to retrieve relevant articles from the databases of the Web of Science and Google Scholar. ML applications in the soil stage were mostly based on the use of satellite images of land for estimation of biofuels yield or suitability analysis of agricultural land. In the second stage of the life cycle, assessment of rheological properties of the feedstocks and their effect on the quality of biofuels were dominant studies reported in the literature. The production stage included estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage included analysis of engine performance and estimation of emissions temperature and composition, such as NOx CO, and CO2. This study identified the following trends: dominant ML method, the stage of life cycle getting more usage of ML, the type of data used for the development of the ML-based models, and the stage-wise frequently used input and output variables. The findings of this article are beneficial for academia and industry-related people involved in model development in different stages of biofuel’s life cycle.

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Section: Consumption Engine Performance and Emissionsmentioning

confidence: 99%

Machine Learning Applications in Biofuels' Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Ahmad¹,

Kano²,

Menezes³

et al. 2020

Preprint

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show abstract

“…Those studies have revealed that the best training algorithm for engine emissions predictions are backpropagation neural networks with Levenberg-Marquardt 7) . The scope of previous works above is summarized in Table 1 More recent works have shown the ability of prediction of engine performance and emision using ANN models 8,9,10,11,12,13,14,15) In this paper, a methodology for predicting the engine performance is developed using an ANN model, and comparing the results with an engine simulation package "Engine Analyzer Pro," as described below.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Internal Combustion Engine Performance Using Artificial Intelligence

Shalahuddin

Suksmono

Sembiring

2023

Majalah Ilmiah Pengkajian Industri

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The potential of artificial intelligence (AI) application for prediction of internal combustion engine performance is assessed in this paper. A literature survey on this subject is first reviewed, in which previous researches utilized the advance of artificial neural networks (ANN) as one type of AI. Previous works commonly obtained the data from experimental engine tests. Under the same engines, they varied the fuel compositions or the engine operating conditions. Whereas in this study, an ANN model is developed to calculate the inputs from an engine simulation software package database and to predict the engine performance based on the simulation software outputs as the ANN target outputs. Results from the ANN model in the “learning” step indicates good agreement with the software simulation outputs. Improvement and development of the program are required, including optimation of the ANN model architecture, such as the choice of activation function, the number of neurons in the hidden layer, and the number of iterations, as well as the number and option of input engine parameters. The ANN model seems promising to predict engine performance, with root mean square errors in the range of 0.4-1.8%. Keywords: Artificial Intelligence; Neural Networks; Engine Performance.

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“…Based on the ANN studies in the recent literature, it can be said that generally predictability of exhaust emissions (HC, CO, NOx) [13][14][15], performance (BTE (Brake Thermal Efficiency), BSFC (Brake Specific Fuel Consumption) [13][14][15][16], and combustion (CPmax) characteristics [13,17,18], for various test fuels was tested. For example, Ağbulut et al, 2020b predicted CPmax, BSFC, CO, HC and NOx parameters by using an ANN algorithm for various test fuels, different engine loads and different injection pressures in a diesel engine [19].…”

Section: Introductionmentioning

confidence: 99%

ANN Based Prediction of Engine Performance and Exhaust Emission Responses of a CI Engine Powered By Ternary Blends

Karagöz

2020

International Journal of Automotive Science and Technology

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In this study, experimental data was gathered from a single cylinder diesel engine fuelled with pyrolytic oil, neat diesel and butanol fuel blends. The experiments were conducted at varying engine loads from 0.25 kW to 1 kW with the interval of 0.25 kW. The engine performance and emission data obtained were predicted using an artificial neural network (ANN) algorithm. Assessed responses are CO, NOx, BSFC, and BTE. The results were discussed in terms of R2, MBE, and RMSE metrics. The performance and emission responses were predicted with a good R2 value of 0.986, 0.963, 0.991, and 0967 for BTE, BSFC, NOx, and CO, respectively, and all MBE value is very close to zero and smaller than 1.14. In the conclusion, the present paper showed that the ternary form of n-butanol-pyrolytic fuel and diesel fuel can be used in a CI engine with no modification on the vehicular system and the emission and performance responses of ternary fuels can be accurately predicted using an artificial neural network.

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Developing of ANN model for prediction of performance and emission characteristics of VCR engine with orange oil biodiesel blends

Cited by 45 publications

References 29 publications

Machine Learning Applications in Biofuels' Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Machine Learning Applications in Biofuels' Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Prediction of Internal Combustion Engine Performance Using Artificial Intelligence

ANN Based Prediction of Engine Performance and Exhaust Emission Responses of a CI Engine Powered By Ternary Blends

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