A Study to Predict Ignition Delay of an Engine Using Diesel and Biodiesel Fuel Based on the ANN and SVM Machine Learning Methods

Tuan, Nguyen Van; Minh, Duong Quang; Khoa, Nguyen Xuan

doi:10.1021/acsomega.2c07186

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…In their study, Van Tuan and colleagues 30 harnessed artificial neural networks (ANNs) and support vector machine (SVM) techniques to forecast ignition delay in a single-cylinder compression-ignition engine, focusing on both diesel and biodiesel fuels. To develop their predictive models, they employed a training data set comprising more than 700 experimental data points.…”

Section: Application Of An Artificial Neural Network In Ignition Dela...mentioning

confidence: 99%

Machine Learning Approaches for Predicting Ignition Delay in Combustion Processes: A Comprehensive Review

Molana,

Darougheh,

Biglar

et al. 2024

Ind. Eng. Chem. Res.

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This review explores machine learning approaches for predicting ignition delay in combustion processes. Ignition delay is a vital parameter in optimizing the engine design, fuel formulations, and combustion efficiency. The review examines the applications of artificial neural networks (ANNs) and convolutional neural networks (CNNs) in various combustion processes and equipment, such as engines, boilers, and rapid compression machines. The differences between ANNs and CNNs are discussed, highlighting their capabilities and limitations. Numerous studies are presented, demonstrating the successful application of neural networks in predicting ignition delay for different fuels and engines. Overall, machine learning approaches show great promise in accurately predicting the ignition delay and advancing energy utilization.

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Section: Application Of An Artificial Neural Network In Ignition Dela...mentioning

confidence: 99%

Machine Learning Approaches for Predicting Ignition Delay in Combustion Processes: A Comprehensive Review

Molana,

Darougheh,

Biglar

et al. 2024

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…To address these challenges, researchers are exploring data-based technologies, such as machine learning (ML), which involves creating, analyzing, and using techniques to enable machines to learn and perform AI-related tasks through a learning process using response data. Machine learning is considered a promising approach for resolving issues related to FAME synthesis and forecasting by leveraging its ability to handle complex relationships between input and response variables. , ML involves a learning process that helps a system adapt to its environment and observations. Deep learning neural networks, a subset of machine learning, simulate the functioning of the human brain by using data inputs, weights, and biases.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning is considered a promising approach for resolving issues related to FAME synthesis and forecasting by leveraging its ability to handle complex relationships between input and response variables. 34 , 35 ML involves a learning process that helps a system adapt to its environment and observations. Deep learning neural networks, a subset of machine learning, simulate the functioning of the human brain by using data inputs, weights, and biases.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Deep Learning Modeling of the Yield and Properties of Baobab-Derived Biodiesel Catalyzed by Waste Banana Bunch Stalk Biochar

Elendu,

Liu,

Aleem

et al. 2024

ACS Omega

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The integration of optimization techniques and deep learning models, which offer a promising avenue for improving the efficiency and sustainability of biodiesel production processes from baobab seed oil (BSO), is rare. This study utilized a multi-input-multioutput (MIMO) deep learning technique and the most recent central composite design (CCD) optimization tool to model and optimize the yield and properties of biodiesel produced from BSO. First, the baobab seed oil was extracted using a solvent extraction method. BSO was subsequently analyzed and converted to biodiesel by reacting CH3OH catalyzed by waste banana bunch stalk biochar activated by KOH. Multiobjective optimization and prediction of the biodiesel yield (Y) and several key fuel properties, including the cetane number (CN), kinematic viscosity (VS), and purity (P), were achieved. With better correlation coefficients of 0.9709, 0.9464, and 0.9714 for response training, response testing, and response validation, respectively, and a root-mean-square error of 0.00755, the MIMO model on the logsig transfer function accurately predicted the biodiesel yield and properties more than did the MISO and response surface methodology models. The optimum Y (96 wt %), CN (48), VS (3.3 mm2/s), and P (98.3%) were concurrently accomplished at a reaction temperature of 56 °C, a reaction time of 115 min, a CH3OH/BSO molar ratio of 15:1, a catalyst dosage of 6 wt %, and a stirring speed of 400 rpm with 98% optimal validation accuracy. CCD sensitivity analysis revealed that the CH3OH/BSO ratio was the most sensitive (50.9%) input predictor among the other input variables studied.

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“…For instance, one study employed a kernelbased extreme learning machine to assess the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends [9]. In another research, a model based on artificial neural networks and support vector machines was developed to predict the ignition delay of a diesel engine operating on both diesel and biodiesel fuels [10]. Furthermore, machine learning algorithms have been deployed to analyze the performance, combustion, and emission characteristics of a diesel engine fueled with pumpkin-maize biodiesel [11].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Engine Performance and Emission Values of Biodiesel Obtained from Waste Pumpkin Seeds with Machine Learning

Şahin,

Torun

2024

Agriculture

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This study was primarily conducted to investigate the potential use of pumpkin seed oil in biodiesel production. Initially, the fatty acid composition of oils extracted from discarded pumpkin seeds was determined. Then, biodiesel produced from discarded pumpkin seed oil was tested in an engine test setup. The performance and emission values of a four-cylinder diesel engine fueled with diesel (D100), biodiesel (PB100), and blended fuels (PB2D98, PB5D95, and PB20D80) were determined. Furthermore, three distinctive machine learning algorithms (artificial neural networks, XGBoost, and random forest) were employed to model engine performance and emission parameters. Models were generated based on the data from the PB100, PB2D98, and PB5D95 fuels, and model performance was assessed through the R2, RMSE, and MAPE metrics. The highest torque value (333.15 Nm) was obtained from 1200 rpm of D100 fuel. PB2D98 (2% biodiesel–98% diesel) had the lowest specific fuel consumption (194.33 g HPh−1) at 1600 rpm. The highest BTE (break thermal efficiency) value (30.92%) was obtained from diesel fuel at 1400 rpm. Regarding the blended fuels, PB2D98 exhibited the most fuel-efficient performance. Overall, in terms of engine performance and emission values, PB2M98 showed the closest results to diesel fuel. A comparison of machine learning algorithms revealed that artificial neural networks (ANNs) generally performed the best. However, the XGBoost algorithm proved to be more successful than other algorithms at predicting the performance and emissions of PB20D80 fuel. The present findings demonstrated that the XGBoost algorithm could be a more reliable option for predicting engine performance and emissions, especially for data-deficient fuels such as PB20D80.

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A Study to Predict Ignition Delay of an Engine Using Diesel and Biodiesel Fuel Based on the ANN and SVM Machine Learning Methods

Cited by 5 publications

References 35 publications

Machine Learning Approaches for Predicting Ignition Delay in Combustion Processes: A Comprehensive Review

Machine Learning Approaches for Predicting Ignition Delay in Combustion Processes: A Comprehensive Review

Optimization and Deep Learning Modeling of the Yield and Properties of Baobab-Derived Biodiesel Catalyzed by Waste Banana Bunch Stalk Biochar

Comparison of Engine Performance and Emission Values of Biodiesel Obtained from Waste Pumpkin Seeds with Machine Learning

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