Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios

Mehra, Roopesh Kumar; Duan, Hao; Luo, Shuyi; Rao, Anas; Ma, Fenglong

doi:10.1016/j.apenergy.2018.06.085

Cited by 111 publications

(37 citation statements)

References 50 publications

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“…During training a network the test result was segregated into 70% of training data, 15% of testing data, and remaining 15% of validating data 4 . These data were predefined for the ANN tool which is present in the MATLAB.…”

Section: Resultsmentioning

confidence: 99%

“…The CI engine fueled with waste cooking oil (WCO) and biodiesel blend predicts the engine performance and emission characteristics with acceptable mean relative error (MRE) of about 8% 3 . Even the study of hydrogen‐compressed natural‐gas‐fueled spark ignition (SI) engine predicts a good regression multilayered feed‐forward network trained with tansig and trainlm comparing different neurons in the hidden layer 4 . The cyclic variability in CI engine fueled with n‐butanol blend also developed a predicting model for COV imep using ANN which provides better result with R ranging from 0.858 to 0.983 5 .…”

Section: Introductionmentioning

confidence: 99%

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Developing a smart fuel using artificial neural network for compression ignition engine fueled with Calophyllum inophyllum diesel blend at various compression ratio

Venugopal

Kasimani

Chinnasamy

2020

Env Prog and Sustain Energy

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In machinery, it is evident that the computing system for self‐automated machinery derives nonlinear and complex equations by comparing the machinery's different input parameters with their corresponding performance output parameters. In order to operate the machinery with good performance and better efficiency, the computing system needs a machine‐learning algorithm. Most recent researchers have concentrated more on self‐driving vehicle, which seems to be lack of developing a strong algorithm for compression ignition (CI) engines to predict the performance and emission output parameter. Thus, this article deals with the prediction of performance and emission characteristics of CI engine fueled with 25% Calophyllum inophyllum and 75% diesel blend (CIB25) at various compression ratios using artificial neural network (ANN). Performance and emission tests were conducted in a single‐cylinder four‐stroke variable‐compression‐ratio CI engine fueled with CIB25 with varying loads and at a constant speed of operation. Experimental investigation indicates that 18:1 compression ratio gives better performance results when CIB25 is used as the fuel. Emission test results show better emission characteristics at 17:1 compression ratio. These results show that some input factors affect the output factors under some set of operating conditions, while some input factors improve them. ANN developed for the CI engine learns how the input factors affect and improve the output factors. Also, developed neural network is found to be satisfactory, and it predicts the output at a regression value of 0.998 with an average error of 1.77% in the case of CIB25.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Developing a smart fuel using artificial neural network for compression ignition engine fueled with Calophyllum inophyllum diesel blend at various compression ratio

Venugopal

Kasimani

Chinnasamy

2020

Env Prog and Sustain Energy

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“…In this study, the back-propagation network, with high prediction accuracy and the fastest learning rate, was utilized thereby as the research tool. The back-propagation network needs a convergence test procedure for validating the experiment performance suggested by Hsu et al [27], Du et al [28], Mehra et al [29], and Wang et al [30].…”

Section: Case Studymentioning

confidence: 99%

“…The 270 data sets were divided for training 80% (216 samples), validation 10% (27 samples), and testing 10% (27 samples) by a setting of the back-propagation network. The setting of percentage of training, validation, and testing is suggested by Mehra et al [29]. Through the operation procedure process of the back-propagation network, we obtained the following cycle convergence diagram in Fig.…”

Section: Case Studymentioning

confidence: 99%

An integrated model using the Taguchi method and artificial neural network to improve artificial kidney solidification parameters

Shie

Lin

et al. 2019

BioMed Eng OnLine

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Background Hemodialysis mainly relies on the “artificial kidney,” which plays a very important role in temporarily or permanently substituting for the kidney to carry out the exchange of waste and discharge of water. Nevertheless, a previous study on the artificial kidney has paid little attention to the optimization of factors and levels for reducing the solidification of the artificial kidney during the hemodialysis procedure. Thus, this study proposes an integrated model that uses the Taguchi method, omega formula, and back-propagation network to determine the optimal factors and levels for addressing this issue. Methods First, we collected the recommendations of medical doctors and nursing staff through a small group discussion, and used the Taguchi method to analyze the key factors at different levels. Next, the omega formula was used to convert the analysis results from the Taguchi method to assess the defect rate. Finally, we utilized back-propagation network algorithms to predict the optimal factors and levels for artificial kidney solidification, in order to confirm that the key factors and levels identified can effectively improve the solidification rate of the artificial kidney and thereby enhance the effect of hemodialysis. Results The research finding proposes the following as the optimal factors and levels for artificial kidney solidification: the amount of anticoagulation should be set at 500 units, the velocity of blood flow at 300 ml/min, the dehydration volume at 2.5 kg, and the vascular access type as autologous blood vessels. We obtained 270 sets of data from the patients of End Stage Renal Disease (ESRD) under the setting of the optimal combination of the factors at different levels; the defect rate of artificial kidney solidification is 12.9%, which is better than the defect rate of 32% in the original experiment. Meanwhile, the patient characteristics for physiological status in BMI, serum calcium, hematocrit, ferritin, and transferrin saturation percentage are improved by this study. Conclusion This conclusion validates the ability of the proposed model in this study to improve the solidification rate of the artificial kidney, thereby confirming the model’s use as a standard operation procedure in the hemodialysis experiment. The ideas behind and the implications of the proposed model are further discussed in this study.

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“…22 Models for performance and emissions including BSFC, Torque, NO x , CO, THC, and CH 4 of a hydrogen enriched compressed natural gas (HCNG) engine were built using ANNs. 23 Network structures of each output were individually considered, but models could be combined in some model groups if DNNs were used. Alcan et al 24 studied NOx models for steady and transient cycles via sigmoid based nonlinear autoregressive with exogenous input (NARX).…”

Section: Introductionmentioning

confidence: 99%

Deep learning procedure for knock, performance and emission prediction at steady-state condition of a gasoline engine

Shin

Lee

Kim

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Recently, deep learning has played an important role in the rise of artificial intelligence, and its accuracy has gained recognition in various research fields. Although engine phenomena are very complicated, they can be predicted with high accuracy using deep learning because they are based on the fundamentals of physics and chemistry. In this research, models were built with deep neural networks for gasoline engine prediction. The model consists of two sub-models. The first predicts the knock occurrence, and the second predicts performance, combustion, and emissions. This includes maximum cylinder pressure, crank angle at maximum cylinder pressure, maximum pressure rise rate, and brake mean effective pressure, brake-specific fuel consumption, brake-specific nitrogen oxides, and brake-specific carbon oxide, which are representative results of the engine (for normal combustion cases without knock). Model input parameters were selected considering engine operating conditions, and physically measurable sensor values. For test cases, the accuracy of the first model for knock classification is 99.0%, and the coefficient of determination (R2) values for the second model are all above 0.99. Test times of both models were approximately 2 ms. The robustness of all the models was verified using K-fold cross-validation. A sensitivity study of accuracy, according to the amount of training utilized, was also conducted to determine how many data points are required to effectively train the deep learning model. Accordingly, a deep learning approach was applied to predict the steady-state conditions of a gasoline engine. Achieved model accuracies and robustness proved deep learning to be an effective modeling approach, and test time was recognized to be able to apply for the real-time prediction. The sensitivity analysis can be applied for the preliminary study to define the number of experimental points for the deep learning model.

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Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios

Cited by 111 publications

References 50 publications

Developing a smart fuel using artificial neural network for compression ignition engine fueled with Calophyllum inophyllum diesel blend at various compression ratio

Developing a smart fuel using artificial neural network for compression ignition engine fueled with Calophyllum inophyllum diesel blend at various compression ratio

An integrated model using the Taguchi method and artificial neural network to improve artificial kidney solidification parameters

Deep learning procedure for knock, performance and emission prediction at steady-state condition of a gasoline engine

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