Artificial neural networks based prediction of performance and exhaust emissions in direct injection engine using castor oil biodiesel-diesel blends

Shojaeefard, Mohammad Hassan; Etghani, Mir Majid; Akbari, Mostafa; Khalkhali, Abolfazl; Ghobadian, Barat

doi:10.1063/1.4769200

Cited by 14 publications

(6 citation statements)

References 44 publications

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“…Another reason is the higher frictional losses during the high speed. Both factors make the SFC rise [38]. Figure 12c,d show that as the speed increases, FC and EGT rise.…”

Section: Use Of Simulation With Annmentioning

confidence: 92%

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Performance Analysis of Direct Injection Diesel Engine Fueled with Diesel-Tomato Seed Oil Biodiesel Blending by ANOVA and ANN

et al. 2019

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Biodiesel is an alternative fuel for diesel engine. Considering the differences between diesel and biodiesel fuels, the engine condition should be modified based on the fuel or fuel blends to achieve optimum performance. This study presented a performance analysis of a direct-injected (DI) diesel engine with a dynamometer fueled with diesel-tomato seed biodiesel (TSOB) blends employing ANOVA and universal nonlinear model based on ANN. The experiments were carried out under conditions of some independent variables including different engine loads (0, 50, 100%) and speed (1800, 2150, and 2500 rpm) for four diesel-biodiesel combinations (B0, B5, B10, and B20). In this research, the effect of these factors on dependent variables including power, torque, SFC, FC, and Exhaust Gas Temperature (EGT) are investigated. Duncan s multi-domain test at a significance level of R < 0.01 shows that the highest and lowest of the torque and power are produced from B5 and B20, respectively. These results show that the lowest EGT of 613 K is related to B20 and the highest EGT is related to B5 and B10. The regression models showed that the torque decreases with increasing the engine speed and biodiesel percentage. These results also show that the highest and the lowest SFC is related to B0 and B20, respectively. The ANN model shows high capability of predicting the engine performance parameters and emissions, without running costly and time-consuming experiments with the histogram error of 0.004 and R = 0.96. It also proved that ANN is a non-linear model of choice to deal with these data, instead of multivariate linear regression employed for preliminary analysis.Energies 2019, 12, 4421 2 of 18 tomato seed was found to contain 18-23% oil, which can be used to make biodiesel [6,7]. Biodiesel is compatible with conventional diesel and could be mixed with any ratio of diesel for running an engine [8,9]. The main differences between biodiesel and diesel are lower calorific value and the source from which these products are derived, and the manufacturing process behind their production, which limit the oxygen content. The amount of oxygen in diesel is zero, while it is 10 to 12% in biodiesel, and this reduces the energy density as well as reduces the emissions of suspended particles. Biodiesel is considered a clean fuel; however, research shows that biodiesel has a large Cetane Index which reduces the ignition time, but, on the other hand, its Lower Heating Value (LHV) is lower than that of diesel and thus produces lower power in the engine [10][11][12]. Renewability, lower amounts of combustion pollutants are one of the most important reasons for choosing the biodiesels as a substitute for diesel [13]. Approximately, one-third of the world s fossil fuels are consumed in internal combustion engines (ICEs), which emit harmful exhaust gases and are the main reasons for environmental pollution. Diesel engines operate relatively more efficiently than petrol engines. The current and future regulation of pollution requires exploration of fuels t...

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“…Another reason is the higher frictional losses during the high speed. Both factors make the SFC rise [38]. Figure 12c,d show that as the speed increases, FC and EGT rise.…”

Section: Use Of Simulation With Annmentioning

confidence: 92%

“…Linear Equation (2), indicates that (%BD), (L) and (S) have positive effects on (P). The main reason for this behavior is the high viscosity and low thermal capacity of biodiesel compared to the diesel [38]. The (L) has the highest impact on (P).…”

Section: Powermentioning

confidence: 99%

Performance Analysis of Direct Injection Diesel Engine Fueled with Diesel-Tomato Seed Oil Biodiesel Blending by ANOVA and ANN

et al. 2019

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“…The ANN accurately predicted the engine performance, the engine torque, SFC, and EGT. Shojaeefard et al [109] used ANN to estimate performance and exhaust emissions, BSFC, brake power, and exhaust emissions of DI engine working on biodiesel developed from castor oil. The ANN performance was compared with a group method of data handling.…”

Section: Extreme Learning Methodsmentioning

confidence: 99%

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

et al. 2021

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Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models, and the frequently used input and output variables for each stage. The findings of this article would be beneficial for academia and industry-related professionals involved in model development in different stages of biofuel’s life cycle.

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“…The ANN accurately predicted the engine performance, the engine torque, SFC, and EGT. Shojaeefard et al [107] used ANN to estimate performance and exhaust emissions, BSFC, brake power, and exhaust emissions of DI engine working on biodiesel developed from castor oil. The ANN performance was compared with a group method of data handling.…”

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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Artificial neural networks based prediction of performance and exhaust emissions in direct injection engine using castor oil biodiesel-diesel blends

Cited by 14 publications

References 44 publications

Performance Analysis of Direct Injection Diesel Engine Fueled with Diesel-Tomato Seed Oil Biodiesel Blending by ANOVA and ANN

Performance Analysis of Direct Injection Diesel Engine Fueled with Diesel-Tomato Seed Oil Biodiesel Blending by ANOVA and ANN

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

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