scholarly journals Prediction of NOx Emissions for a Range of Engine Hardware Configurations Using Artificial Neural Networks

2020 ◽  
Author(s):  
Nick Papaioannou ◽  
XiaoHang Fang ◽  
Felix Leach ◽  
Martin H. Davy
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Operating Conditions ◽  
Data Accuracy ◽  
Nox Emissions ◽  
Ann Model ◽  
Baseline Training ◽  
Wide Range ◽  
Artificial Neural

Abstract The predictive ability of artificial neural networks where a large number of experimental data are available, has been studied extensively. Studies have shown that ANN models are capable of accurately predicting NOx emissions from engines under various operating conditions and different fuel types when trained well. One of the major advantages of an ANN model is its ability to relearn when new experimental data is available, thus continuously improving its accuracy. The present work explored the potential of an ANN model to predict NOx emissions for various engine configurations outside its training envelop. This work also looked into quantifying the amount of new data required to improve the accuracy of the model when exposed to unknown conditions. The chosen ANN model was constructed using data from a high-speed direct injection diesel engine and is capable of accurate NOx emissions over a wide range of operating conditions. The optimized network utilized 14 input parameters and is using 6 neurons in a single hidden layer feed-forward neural network. Experimental data from the various engine configurations tested, were then used to predict NOx from the existing ANN model. The results indicate that when the new data are within the baseline training envelop, the ANN model is capable of accurate NOx prediction even when there are substantial changes in engine configuration such as piston material. Similar results were also observed when the injector nozzle is changed. However, the model’s performance drops significantly when new data, outside the baseline training envelop, were employed indicating that additional training is required. As such, various methods for retraining the ANN model were explored with the selected method showing the best compromise between new-data accuracy and old-data accuracy retention. The retrained ANN model developed was found to be an effective tool in predicting NOx emissions for different engine configurations and operating conditions.

Estimation of Pressure Drop of Two-Phase Flow in Horizontal Long Pipes Using Artificial Neural Networks

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
Mostafa Safdari Shadloo ◽  
Amin Rahmat ◽  
Arash Karimipour ◽  
Somchai Wongwises
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pressure Drop ◽  
Newtonian Liquid ◽  
Operating Conditions ◽  
Two Phase ◽  
Wide Range ◽  
Liquid Systems ◽  
Artificial Neural

Abstract Gas–liquid two-phase flows through long pipelines are one of the most common cases found in chemical, oil, and gas industries. In contrast to the gas/Newtonian liquid systems, the pressure drop has rarely been investigated for two-phase gas/non-Newtonian liquid systems in pipe flows. In this regard, an artificial neural networks (ANNs) model is presented by employing a large number of experimental data to predict the pressure drop for a wide range of operating conditions, pipe diameters, and fluid characteristics. Utilizing a multiple-layer perceptron neural network (MLPNN) model, the predicted pressure drop is in a good agreement with the experimental results. In most cases, the deviation of the predicted pressure drop from the experimental data does not exceed 5%. It is observed that the MLPNN provides more accurate results for horizontal pipelines in comparison with other empirical correlations that are commonly used in industrial applications.

Detecting heavy metal contamination in soil using complex permittivity and artificial neural networks

2004 ◽  
Vol 41 (6) ◽  
pp. 1054-1067 ◽  
Author(s):  
J Q Shang ◽  
W Ding ◽  
R K Rowe ◽  
L Josic
Keyword(s):  
Heavy Metals ◽  
Neural Networks ◽  
Heavy Metal ◽  
Artificial Neural Networks ◽  
Complex Permittivity ◽  
Heavy Metal Contamination ◽  
Zinc Salt ◽  
Ann Model ◽  
Wide Range ◽  
Artificial Neural

The use of the complex permittivity, an intrinsic electrical property of materials, to detect the presence and type of heavy metals in soil is investigated. The soil specimens are prepared by mixing the soil with distilled and deionized water, NaCl solutions, and copper and zinc salt solutions and compacting at known water contents. The complex permittivities of the soil specimens are measured in the laboratory using a custom-developed apparatus. A database, which includes both contaminated and uncontaminated soil specimens, is developed, with the soil water content, density, and pore-fluid salinity varying over a relatively wide range. Two artificial neural network (ANN) models are developed to (i) identify whether the heavy metals are present in the soil; and, if so, (ii) distinguish the metal type, based on the complex permittivities measured on the soil specimens. The first ANN model (identification) can correctly identify the presence of heavy metals in 90% of cases. The second ANN model (classification) can correctly classify the type of the heavy metal in 95% of cases. Better performance can be achieved if more complex permittivity data are available for the training of the networks.Key words: heavy metals, soil contamination, contamination detection, complex permittivity, artificial neural networks.

Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Hadi Salehi ◽  
Mosayyeb Amiri ◽  
Morteza Esfandyari
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Energy Gain ◽  
Operating Conditions ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.

scholarly journals Mathematical Modeling of Photovoltaic Properties of Nipc/P-Si (Organic/Inorganic) Heterojunction by Using Artificial Neural Networks Model

2020 ◽  
Vol 17 ◽  
pp. 306-321
Author(s):  
R. A. Mohamed ◽  
Mahmoud. Y. El-Bakry ◽  
D. M. Habashy ◽  
E. H. Aamer
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Experimental Studies ◽  
Back Propagation ◽  
Ann Model ◽  
Photovoltaic Properties ◽  
Nickel Phthalocyanine ◽  
Artificial Neural ◽  
Model Training

In this research, the artificial neural network (ANN) and resilient back propagation (R-prop) training algorithm are utilized to model the photovoltaic properties of Nickel–phthalocyanine (NiPc/p-Si) heterojunction. The experimental data are extracted from experimental studies. Experimental data are utilized as inputs in the ANN model. Training of different structures of the ANN is processed to approach the minimum value of error. Eight artificial neural networks are trained to get a better mean square error (MSE) and best execution for the networks. The ANN performances are also investigated and their values are very small (MSE < 10-3). The simulation results of the current-voltage characteristics of NiPc films are produced and provided excellent matching with the corresponding experimental data. Utilization of ANN model for predictions is also processed and gives accurate results.  The equation which describes the relation between the inputs and outputs is obtained. The high accuracy of the ANN model has appeared in the major guessing power and the ability of generalization depending on the obtained equations.

Predicting Viscosities of Heavy Oils and Solvent–Heavy Oil Mixtures Using Artificial Neural Networks

2021 ◽  
Vol 143 (11) ◽  
Author(s):  
Zehua Chen ◽  
Daoyong Yang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Performance ◽  
Viscosity Data ◽  
Heavy Oils ◽  
Ann Model ◽  
Wide Range ◽  
Data Points ◽  
Ann Models ◽  
Artificial Neural

Abstract This study investigates the potential of artificial neural networks (ANNs) to accurately predict viscosities of heavy oils (HOs) as well as mixtures of solvents and heavy oils (S–HOs). The study uses experimental data collected from the public domain for HO viscosities (involving 20 HOs and 568 data points) and S–HO mixture viscosities (involving 12 solvents and 4057 data points) for a wide range of temperatures, pressures, and mass fractions. The natural logarithm of viscosity (instead of viscosity itself) is used as predictor and response variables for the ANNs to significantly improve model performance. Gaps in HO viscosity data (with respect to pressure or temperature) are filled using either the existing correlations or ANN models that innovatively use viscosity ratios from the available data. HO viscosities and mixture viscosities (weight-based, molar-based, and volume-based) from the trained ANN models are found to be more accurate than those from commonly used empirical correlations and mixing rules. The trained ANN model also fares well for another dataset of condensate-diluted HOs.

scholarly journals Efficiency of Artificial Neural Networks in Determining Scour Depth at Composite Bridge Piers

2019 ◽  
Author(s):  
Ata Amini ◽  
Shahriar Hamidi ◽  
Marlinda Malek ◽  
Thamer Mohammad ◽  
Ataollah Shirzadi ◽  
...  
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bridge Piers ◽  
Scour Depth ◽  
Ann Model ◽  
Composite Bridge ◽  
Artificial Neural ◽  
Bridge Failure ◽  
Propagation Network

Scouring is the most common cause of bridge failure. This study was conducted to evaluate the efficiency of the Artificial Neural Networks (ANN) in determining scour depth around composite bridge piers. The experimental data, attained in different conditions and various pile cap locations, were used to obtain the ANN model and to compare the results of the model with most well-known empirical, HEC-18 and FDOT, methods. The data were divided into training and evaluation sets. The ANN models were trained using the experimental data, and their efficiency was evaluated using statistical test. The results showed that to estimate scour at the composite piers, feedforward propagation network with three neurons in the hidden layer and hyperbolic sigmoid tangent transfer function was with the highest accuracy. The results also indicated a better estimation of the scour depth by the proposed ANN than the empirical methods.

scholarly journals Identifying nonclassicality from experimental data using artificial neural networks

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Valentin Gebhart ◽  
Martin Bohmann ◽  
Karsten Weiher ◽  
Nicola Biagi ◽  
Alessandro Zavatta ◽  
...  
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural

scholarly journals Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1070
Author(s):  
Abdul Gani Abdul Jameel
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Functional Groups ◽  
Average Error ◽  
Ann Model ◽  
Oh Groups ◽  
Unseen Data ◽  
Learning Capabilities ◽  
Artificial Neural ◽  
Oxygenated Fuels

The self-learning capabilities of artificial neural networks (ANNs) from large datasets have led to their deployment in the prediction of various physical and chemical phenomena. In the present work, an ANN model was developed to predict the yield sooting index (YSI) of oxygenated fuels using the functional group approach. A total of 265 pure compounds comprising six chemical classes, namely paraffins (n and iso), olefins, naphthenes, aromatics, alcohols, and ethers, were dis-assembled into eight constituent functional groups, namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic –CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups, and ether O groups. These functional groups, in addition to molecular weight and branching index, were used as inputs to develop the ANN model. A neural network with two hidden layers was used to train the model using the Levenberg–Marquardt (ML) training algorithm. The developed model was tested with 15% of the random unseen data points. A regression coefficient (R2) of 0.99 was obtained when the experimental values were compared with the predicted YSI values from the test set. An average error of 3.4% was obtained, which is less than the experimental uncertainty associated with most reported YSI measurements. The developed model can be used for YSI prediction of hydrocarbon fuels containing alcohol and ether-based oxygenates as additives with a high degree of accuracy.

scholarly journals Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2332
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Prediction Models ◽  
Meteorological Station ◽  
Ann Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

scholarly journals Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 44
Author(s):  
Fernando A. N. Silva ◽  
João M. P. Q. Delgado ◽  
Rosely S. Cavalcanti ◽  
António C. Azevedo ◽  
Ana S. Guimarães ◽  
...  
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Cross Sections ◽  
Influential Factors ◽  
Average Error ◽  
Ann Model ◽  
Cement Ratio ◽  
Water Cement Ratio ◽  
Artificial Neural

The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.

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