Machine learning algorithms for a diesel engine fuelled with biodiesel blends and hydrogen using LSTM networks

Zhang, Xiumei; Li, Hui; Sekar, Manigandan; Elgendi, Mahmoud; Krishnamoorthy, N.; Xia, Changlei; Matharasi, D. Priya

doi:10.1016/j.fuel.2022.126292

Cited by 33 publications

(6 citation statements)

References 43 publications

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“…There are two main types of time series forecasting techniques in the literature. These are traditional statistical techniques and machine learning methods (Zhang et al , 2023). RNNs are the leading of traditional statistical methods.…”

Section: Methodsmentioning

confidence: 99%

“…Liu et al examined the impact of microalgae mixes (20% and 30%) on the micro gas turbine in a recent study on aviation fuels. In addition to typical experimental calibrations, they used machine learning methods to examine differences in both performance and emission characteristics of biofuel blends with approximately 51 different fuel combinations using LSTM networks (Liu et al , 2022b; Zhang et al , 2023). As can be seen from the literature, machine learning algorithms have also started to be used in the aviation industry.…”

Section: Introductionmentioning

confidence: 99%

“…İn addition, they develop and test different fuel mixtures in new and innovative studies in the literature. As a result, both the damage to the environment can be reduced and fuel costs can be reduced (Liu et al , 2022b; Zhang et al , 2023).…”

Section: Introductionmentioning

confidence: 99%

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A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

Metlek

2023

AEAT

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Purpose The purpose of this study is to develop and test a new deep learning model to predict aircraft fuel consumption. For this purpose, real data obtained from different landings and take-offs were used. As a result, a new hybrid convolutional neural network (CNN)-bi-directional long short term memory (BiLSTM) model was developed as intended. Design/methodology/approach The data used are divided into training and testing according to the k-fold 5 value. In this study, 13 different parameters were used together as input parameters. Fuel consumption was used as the output parameter. Thus, the effect of many input parameters on fuel flow was modeled simultaneously using the deep learning method in this study. In addition, the developed hybrid model was compared with the existing deep learning models long short term memory (LSTM) and BiLSTM. Findings In this study, when tested with LSTM, one of the existing deep learning models, values of 0.9162, 6.476, and 5.76 were obtained for R2, root mean square error (RMSE), and mean absolute percentage error (MAPE), respectively. For the BiLSTM model when tested, values of 0.9471, 5.847 and 4.62 were obtained for R2, RMSE and MAPE, respectively. In the proposed hybrid model when tested, values of 0.9743, 2.539 and 1.62 were obtained for R2, RMSE and MAPE, respectively. The results obtained according to the LSTM and BiLSTM models are much closer to the actual fuel consumption values. The error of the models used was verified against the actual fuel flow reports, and an average absolute percent error value of less than 2% was obtained. Originality/value In this study, a new hybrid CNN-BiLSTM model is proposed. The proposed model is trained and tested with real flight data for fuel consumption estimation. As a result of the test, it is seen that it gives much better results than the LSTM and BiLSTM methods found in the literature. For this reason, it can be used in many different engine types and applications in different fields, especially the turboprop engine used in the study. Because it can be applied to different engines than the engine type used in the study, it can be easily integrated into many simulation models.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

Metlek

2023

AEAT

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“…It makes use of data to understand underlying patterns in data to establish correlation among data. By using a hybrid strategy, it is possible to enhance the effectiveness of the process by concurrently employing ML model-based prediction and RSM to determine the best combination of any number of input variables (Silitonga et al, 2018;Yang et al, 2023;X. Zhang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Soft computing-based modelling and optimization of NOx emission from a variable compression ratio diesel engine

Paramasivama,

Naima,

Dzida

2024

J. emerg. sci. eng.

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Machine learning method and statistical method used for model prediction and optimization of third generation biodiesel-diesel blend powered variable compression engine High R2 values of 0.9998 and 0.9994 were observed in the training and testing phase of the model, respectively, indicating that The results confirm the robustness of the forecasting system. It was shown that the model accuracy means squared errors remained low at 0.0002 and 0.0014. These results were then confirmed by desirability-based optimization, which succeeded in achieving the values of the set parameters It should be noted that the compression ratio (CR), fuel injection pressure, and engine load were optimized to meet the defined parameters, resulting in a NOx emissions reduction as 222.8 ppm. The research illustrates the efficacy of desirability-based optimization in attaining targeted performance targets across important engine parameters whilst also reducing the impact on the environment.

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“…Different applications have been tested in internal combustion engines that use biodiesel as fuel, such as water injection, alcohol, and gasoline fumigation modes, incorporation of guide vanes, thermal barrier coating, and even preheating of the engine. Among these, a very environmentally friendly alternative is the introduction of hydrogen into the biodiesel blend, input directly into the engine, as shown by Zhang et al [95], where an experimental and mathematical study was conducted with a diesel engine using different biodiesel blends as fuel, plus hydrogen injection for combustion. The biodiesel blends were produced from rubber seed (RSO) with concentrations from 10% to 30% and 10 L/min of constant hydrogen supplied directly to the combustion chamber through an air intake manifold.…”

mentioning

confidence: 99%

A Review of Tropical Organic Materials for Biodiesel as a Substitute Energy Source in Internal Combustion Engines: A Viable Solution?

et al. 2023

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In this article, the most important publications on the subject are compiled to highlight the progress in biodiesel production from tropical cultivars, including energy and environmental potential, raw materials, and the advantages and disadvantages of this biofuel. A critical and objective review of biodiesel production as an alternative fuel for power generation systems and its importance in the energy matrix was conducted. A survey of real applications, new computational and experimental trends, and proposals in internal combustion engines employing organic biofuel was performed. The main findings were as follows: (i) there is the possibility of integration and support in the energy matrix of different countries, as well as the competing with and complementing, energetically, other renewable energy sources, such as solar and wind; (ii) Jatropha curcas, sunflowers, soybean, Moringa oleifera, palm, cottonseed, castor, rubber seed, and coconut are tropical cultivars used to obtained oils into biodiesel; (iii) the findings can be utilized as a theoretical basis for future policies influencing the energy sector through regulatory measures.

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Machine learning algorithms for a diesel engine fuelled with biodiesel blends and hydrogen using LSTM networks

Cited by 33 publications

References 43 publications

A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

Soft computing-based modelling and optimization of NOx emission from a variable compression ratio diesel engine

A Review of Tropical Organic Materials for Biodiesel as a Substitute Energy Source in Internal Combustion Engines: A Viable Solution?

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