Application of artificial neural networks for the prediction of performance and exhaust emissions in IC engine using biodiesel-diesel blends containing quantum dot based on carbon doped

Taheri‐Garavand, Amin; Heidari-Maleni, Aram; Mesri-Gundoshmian, Tarahom; Samuel, Olusegun David

doi:10.1016/j.ecmx.2022.100304

Cited by 6 publications

(2 citation statements)

References 44 publications

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“…In terms of exhaust emissions such as HC, CO2, NOx, and CO, ethanol has better performance than gasoline [29]. Several studies have shown promising results by adding ethanol in reducing exhaust emissions (HC, CO2, NOx, and CO) [11], [21], [22] and increasing engine performance (power, thermal efficiency, and specific fuel consumption) [28], [30], [31].…”

Section: Introductionmentioning

confidence: 99%

Performance and Emission Characteristics Using Dual Injection System of Gasoline and Ethanol

Sulistyo,

Sofyan,

Sukardi

et al. 2023

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This study successfully investigated the engine performance and emission characteristics of a dual injection system that uses both gasoline and ethanol fuels. The study utilized a microcontroller-based control system (PGM-FI) to substitute ethanol fuel injection for gasoline injection. Ethanol fuel was injected at the inlet with three different pressures: 1.0 bar, 1.2 bar, and 1.4 bar, while gasoline injector pressure was fixed at 2 bar. Results showed that substituting ethanol injection with a pressure of 1 bar resulted in a slight decrease in torque and power, but it was the best compared to the other pressures tested. The study found that the use of ethanol injection resulted in improved fuel economy at an ethanol injector pressure of 1 bar with a reduction in SFC of 8.89%. Exhaust emissions were also reduced, with a maximum reduction in CO emissions of 42.54% occurring at a pressure of 1 bar. Similarly, the lowest HC content in exhaust gas was observed at a pressure of 1 bar, which was reduced by 44.48%. However, the results highlighted that ethanol injection pressure could significantly reduce fuel consumption for case A-04 and increase the air-fuel ratio.

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Section: Introductionmentioning

confidence: 99%

Performance and Emission Characteristics Using Dual Injection System of Gasoline and Ethanol

Sulistyo,

Sofyan,

Sukardi

et al. 2023

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“…Ahmed et al [8] used of the ANN model to forecast the fuel e ciency and emissions makes the model suitable for reducing the testing cost since many types of fuels are used for testing. Amin et al [9] use the ANN model to forecast the e ciency and pollution attributes of an engine operating on a ternary blend of graphene quantum dots and waste sh oil biodiesel. Researchers observe that the correlation coe cients for BSFC, NO, CO 2 , and UHC are 0.92, 0.71, 0.96, and 0.95, respectively, and that the RSME was 1.5046, 0.0227, 0.0295, and 1.6545 as shown by ANN for both parameters, with predicted performance values close to experimental values.…”

Section: Introductionmentioning

confidence: 99%

Development of an ANN based model to predict the CI engine performance and emissions fueled with Zinc Oxide Nanoparticle based Biodiesel

Srinivasa

Srinivasa²,

Kumari

2023

Preprint

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Artificial Neural Network (ANN) is an approach in artificial intelligence that can be used to train and process the data using computers. Engineering, science, and pharmaceuticals are just a few of the many fields in which ANN is used. In the present work, ANN modeling has been used to forecast engine performance and emission characteristics. For network training, test data was gathered by running test rig using multiple fuel blends for a single-cylinder high-speed diesel engine. Data for the fuel input of the three distinct fuel used in the experiment - diesel, Mahua biodiesel (MME-20 and MME-50) and nano-blended fuels (50 ppm and 100 ppm) was taken from a framework. An artificial neural network based model was created to forecast performance and emissions using data from different fuels used in diesel engines. The simulation's findings showed that the developed diesel engine ANN 6-13-9 model could precisely forecast the engine performance and emission characteristics of a variety of alternative fuel blends. The Rtrain, Rval, Rtest, and Rall correction coefficients in the ANN 6-13-9 model were 0.99713, 0.99634, 0.99381, and 0.99617 respectively, indicating a stronger relationship between the expected and observed values.

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Optimizing CO₂ Purification in a Negative CO₂ Emission Power Plant

Amiri,

Mikielewicz,

Ziółkowski

et al. 2024

Chem Eng & Technol

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In the pursuit of mitigating CO2 emissions, this study investigates the optimization of CO2 purification within a negative CO2 emission power plant using a spray ejector condenser (SEC) coupled with a separator. The approach involves direct‐contact condensation of vapor, primarily composed of an inert gas (CO2), facilitated by a subcooled liquid spray. A comprehensive analysis is presented, employing a numerical model to simulate a cyclone separator under various SEC outlet conditions. Methodologically, the simulation, conducted in Fluent, encompasses three‐dimensional, transient, and turbulent characteristics using the Reynolds stress model turbulent model and mixture model to replicate the turbulent two‐phase flow within a gas–liquid separator. Structural considerations are delved into, evaluating the efficacy of single‐ and dual‐inlet separators to enhance CO2 purification efficiency. The study reveals significant insights into the optimization process, highlighting a notable enhancement in separation efficiency within the dual‐inlet cyclone, compared to its single inlet counterpart. Specifically, a 90.7 % separation efficiency is observed in the former, characterized by symmetrical flow patterns devoid of wavering CO2 cores, whereas the latter exhibits less desirable velocity vectors. Furthermore, the investigation explores the influence of key parameters, such as liquid volume fraction (LVF) and water droplet diameter, on separation efficiency. It is ascertained that a 10 % LVF with a water droplet diameter of 10 µm yields the highest separation efficiency at 90.7 %, whereas a 20 % LVF with a water droplet diameter of 1 µm results in a reduced efficiency of 50.79 %. Moreover, the impact of structural modifications, such as the addition of vanes, on separation efficiency and pressure drop is explored. Remarkably, the incorporation of vanes leads to a 9.2 % improvement in separation efficiency and a 16.8 % reduction in pressure drop at a 10 % LVF. The findings underscore the significance of structural considerations and parameter optimization in advancing CO2 capture technologies, with implications for sustainable energy production and environmental conservation.

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Application of artificial neural networks for the prediction of performance and exhaust emissions in IC engine using biodiesel-diesel blends containing quantum dot based on carbon doped

Cited by 6 publications

References 44 publications

Performance and Emission Characteristics Using Dual Injection System of Gasoline and Ethanol

Performance and Emission Characteristics Using Dual Injection System of Gasoline and Ethanol

Development of an ANN based model to predict the CI engine performance and emissions fueled with Zinc Oxide Nanoparticle based Biodiesel

Optimizing CO₂ Purification in a Negative CO₂ Emission Power Plant

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Application of artificial neural networks for the prediction of performance and exhaust emissions in IC engine using biodiesel-diesel blends containing quantum dot based on carbon doped

Cited by 6 publications

References 44 publications

Performance and Emission Characteristics Using Dual Injection System of Gasoline and Ethanol

Performance and Emission Characteristics Using Dual Injection System of Gasoline and Ethanol

Development of an ANN based model to predict the CI engine performance and emissions fueled with Zinc Oxide Nanoparticle based Biodiesel

Optimizing CO2 Purification in a Negative CO2 Emission Power Plant

Contact Info

Product

Resources

About

Optimizing CO₂ Purification in a Negative CO₂ Emission Power Plant