Performance &amp; emission analysis of HHO enriched dual-fuelled diesel engine with artificial neural network prediction approaches

Kenanoğlu, Raif; Baltacıoğlu, Mustafa Kaan; Demir, Mehmet Hakan; Ozdemir, Merve Erkinay

doi:10.1016/j.ijhydene.2020.02.108

Cited by 52 publications

(10 citation statements)

References 43 publications

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“…It is also possible to notice that the results in Table 2 has a greater deviation than the reference works [7][8][9][10][11], the reason for this is probably because several items can impact the result of _ : Exhaust gas flow rate to equipment; engine production of PM (surely not perfectly repeatable); engine service equipment (dyno, cooling water and fuel temperature, combustion air conditioning); power measurement (speed and torque); exhaust gas flow rate measurement (or combustion air flow rate and fuel flow rate); sampling filters conditioning chamber (temperature and humidity stability and cleanliness); micro-balance accuracy and stability, and when adding up more items like: different laboratories, different days, different measuring equipment, for sure a larger deviation will occur.…”

Section: Discussionmentioning

confidence: 87%

“…The errors observed in the random forests model are higher than the emissions prediction models built in [7][8][9][10][11]. Several reasons may have contributed to this: the references analyses were carried out in a controlled environment, the same laboratory, the same engine, tests running in sequence, while this research was performed in a wide field, three laboratories were considered, different engines, tests took place on different days.…”

Section: Discussionmentioning

confidence: 98%

“…Besides, it was also found that the relative errors of more than 95% for the training data samples and 90% of the test data samples lied within 10%. In reference [9], the authors build an ANN model to predict NOx and reach an estimation error of all test between the error band within ±10% range.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predição da Emissão de Particulado Utilizando Algoritmo de Aprendizagem de Máquina

Dellaroza¹,

Lodi²,

Luersen³

2021

Blucher Engineering Proceedings

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One of the requirements of diesel engines certification process is that the engine do not exceed the specific particulate emissions limit in cycle ( _ ). To calculate the _ , a manual process is required. The total amount of mass impregnated in the particulate filter is obtained by weighting the filter on a balance after each test. Therefore, it is not possible to obtain it without human intervention. In order to allow test rigs to operate in automatic mode, without an operator conducting the tests, an automatic way of calculating _ is required. Thus, the aim of this work is to develop a _ prediction model that does not require human intervention. For this, a machine learning approach, based on the random forests algorithm, is used. Data collected from 2012 to 2019 from three test cells of 11 and 13 liters diesel engines of an automotive company, summing up 2500 valid test results, are used as input. This data are employed to train the algorithm and build a prediction model. The prediction model is then validated using another 72 validation tests results. The accuracy of the final model considering a confidence interval of 95% is ±3,00 mg/kWh for the European Transient Cycle, and ±1,96 mg/kWh for the European Stationary Cycle.

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Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 98%

See 1 more Smart Citation

Predição da Emissão de Particulado Utilizando Algoritmo de Aprendizagem de Máquina

Dellaroza¹,

Lodi²,

Luersen³

2021

Blucher Engineering Proceedings

View full text Add to dashboard Cite

show abstract

“…These neuron's connection is associated with weight and bias. The neurons have an activation function that determines the neuron's output [52]. The structure of the ANN equation is given below [51]:…”

Section: B Design and Development Of Oann Modelmentioning

confidence: 99%

An Optimized Artificial Neural Network Model using Genetic Algorithm for Prediction of Traffic Emission Concentrations

Abdullah¹,

Usmani²,

Pillai³

et al. 2021

IJACSA

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Global warming and climate change have become universal issues recently. One of the leading sources of climate change is automobiles. Automobiles are the prime source of air pollution in urban areas globally. This has resulted in a problematic and chaotic state in the development of an automatic traffic management system for capturing and monitoring vehicles' hourly and daily passage. With the significant advancement of sensor technology, atmospheric information such as air pollution, meteorological, and motor vehicle data can be harvested and stored in databases. However, due to the complexity and non-linear associations between air quality, meteorological, and traffic variables, it is difficult for the traditional statistical and mathematical models to analyze them. Recently, machine learning algorithms in the field of traffic emissions prediction have become a popular tool. Meteorological and traffic variables influence the variation and the trend of the traffic pollutants. In this paper, an optimized artificial neural network (OANN) was developed to enhance the existing artificial neural network (ANN) model by updating the initial weights in the network using a Genetic Algorithm (GA). The OANN model was implemented to predict the concentration of CO, N O, N O2, and N Ox pollutants produced by motor vehicles in Kuala Lumpur, Malaysia. OANN was compared with Artificial Neural Network (ANN), Random Forest (RF), and Decision Tree (DT) models. The results show that the developed OANN model performed better than the ANN, RF, and DT models with the lowest MSE values of 0.0247 for CO, 0.0365 for N O, 0.0542 N O2, and 0.1128 for N Ox. It can be concluded that the developed OANN model is a better choice in predicting traffic emission concentrations. The developed OANN model can help environmental agencies monitor traffic-related air pollution levels efficiently and take necessary measures to ensure the effectiveness of traffic management policy. The OANN model can also help decision-makers mitigate traffic emissions to protect citizens living in the neighborhood of highways.

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“…Ali Yimilaz et al also studied the outcome of HHO addition on engine performance and reported that engine torque was amplified by 19.1%, and an average gain of 14% was achieved using HHO [24]. The effect of HHO gas on the performance of a Mitsubishi Canter brand, four-stroke, water-cooled diesel engine was conducted by Raif et al They varied the flow rate of HHO from 3 LPM to 7 LPM and observed that with HHO enrichment, the torque and brake power increased, whereas fuel consumption decreased [25]. HHO has also the potential to be used as a blending agent with other fuels.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Led Optimization of Oxyhydrogen Hybridized Diesel Operated Engine

et al. 2021

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The prevailing massive exploitation of conventional fuels has staked the energy accessibility to future generations. The gloomy peril of inflated demand and depleting fuel reservoirs in the energy sector has supposedly instigated the urgent need for reliable alternative fuels. These very issues have been addressed by introducing oxyhydrogen gas (HHO) in compression ignition (CI) engines in various flow rates with diesel for assessing brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The enrichment of neat diesel fuel with 10 dm3/min of HHO resulted in the most substantial decrease in BSFC and improved BTE at all test speeds in the range of 1000–2200 rpm. Moreover, an Artificial Intelligence (AI) approach was employed for designing an ANN performance-predicting model with an engine operating on HHO. The correlation coefficients (R) of BSFC and BTE given by the ANN predicting model were 0.99764 and 0.99902, respectively. The mean root errors (MRE) of both parameters (BSFC and BTE) were within the range of 1–3% while the root mean square errors (RMSE) were 0.0122 kg/kWh and 0.2768% for BSFC and BTE, respectively. In addition, ANN was coupled with the response surface methodology (RSM) technique for comprehending the individual impact of design parameters and their statistical interactions governing the output parameters. The R2 values of RSM responses (BSFC and BTE) were near to 1 and MRE values were within the designated range. The comparative evaluation of ANN and RSM predicting models revealed that MRE and RMSE of RSM models are also well within the desired range but to be outrightly accurate and precise, the choice of ANN should be potentially endorsed. Thus, the combined use of ANN and RSM could be used effectively for reliable predictions and effective study of statistical interactions.

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Performance & emission analysis of HHO enriched dual-fuelled diesel engine with artificial neural network prediction approaches

Cited by 52 publications

References 43 publications

Predição da Emissão de Particulado Utilizando Algoritmo de Aprendizagem de Máquina

Predição da Emissão de Particulado Utilizando Algoritmo de Aprendizagem de Máquina

An Optimized Artificial Neural Network Model using Genetic Algorithm for Prediction of Traffic Emission Concentrations

Artificial Neural Network Led Optimization of Oxyhydrogen Hybridized Diesel Operated Engine

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