In-cylinder soot concentration measurement by Neural Network Two Colour technique (NNTC) on a GDI engine

2020 ◽  
Vol 217 ◽  
pp. 331-345 ◽  
Author(s):  
Marco Potenza ◽  
Marco Milanese ◽  
Fabrizio Naccarato ◽  
Arturo de Risi
Keyword(s):  
Neural Network ◽  
Concentration Measurement ◽  
Soot Concentration ◽  
Gdi Engine

Development of a concentration measurement technique for steady state solid-liquid mixing using a neural network

2013 ◽  
Vol 22 (5) ◽  
pp. 478-483 ◽  
Author(s):  
Deog Hee Doh ◽  
Joo Ho Yum ◽  
Gyeong Rae Cho ◽  
Myung Ho Kim ◽  
Gyong Won Ryu ◽  
...  
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Measurement Technique ◽  
Concentration Measurement ◽  
Liquid Mixing ◽  
Solid Liquid

scholarly journals Safe Hb Concentration Measurement during Bladder Irrigation Using Artificial Intelligence

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5723
Author(s):  
Gerd Reis ◽  
Xiaoying Tan ◽  
Lea Kraft ◽  
Mehmet Yilmaz ◽  
Dominik Stephan Schoeb ◽  
...  
Keyword(s):  
Neural Network ◽  
Bubble Formation ◽  
Concentration Measurement ◽  
Air Bubbles ◽  
Irrigation Systems ◽  
Bladder Irrigation ◽  
Non Invasive ◽  
Maximum Value ◽  
Temporal Features ◽  
Hb Concentration

We have developed a sensor for monitoring the hemoglobin (Hb) concentration in the effluent of a continuous bladder irrigation. The Hb concentration measurement is based on light absorption within a fixed measuring distance. The light frequency used is selected so that both arterial and venous Hb are equally detected. The sensor allows the measurement of the Hb concentration up to a maximum value of 3.2 g/dL (equivalent to ≈20% blood concentration). Since bubble formation in the outflow tract cannot be avoided with current irrigation systems, a neural network is implemented that can robustly detect air bubbles within the measurement section. The network considers both optical and temporal features and is able to effectively safeguard the measurement process. The sensor supports the use of different irrigants (salt and electrolyte-free solutions) as well as measurement through glass shielding. The sensor can be used in a non-invasive way with current irrigation systems. The sensor is positively tested in a clinical study.

Protection Windows Interference of the Multidispersion Soot on NDIR NO2 Concentration Accuracy

2014 ◽  
Vol 886 ◽  
pp. 228-231
Author(s):  
Yan Jun Zhao ◽  
Cheng Bin Gao
Keyword(s):  
Numerical Simulation ◽  
Light Intensity ◽  
Nitrogen Dioxide ◽  
Measurement Accuracy ◽  
Solution Method ◽  
Mie Theory ◽  
Measurement Methods ◽  
Concentration Measurement ◽  
Soot Concentration ◽  
Simulation Based

NDIR method is one of the important nitrogen dioxide concentration measurement methods. The original and the transmission light intensity on the nitrogen dioxide attached on the protection windows is attenuated because of the soot scattering and absorption and the received light intensity on the nitrogen dioxide Characteristic absorption wavelength is deviated from the theoretical absorption light intensity, so the nitrogen dioxide concentration measurement accuracy is decreased. The protection windows pollution interference caused by the monodispersion soot is discussed in this paper. The numerical simulation based on the Mie theory results show that the nitrogen dioxide concentration measurement accuracy is related to the soot concentration, soot diameter and so on. The solution method of the windows pollution interference is brought out and the nitrogen dioxide concentration measurement accuracy can be improved.

scholarly journals An Artificial Neural Network for the Low-Cost Prediction of Soot Emissions

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4787 ◽  
Author(s):  
Mehdi Jadidi ◽  
Stevan Kostic ◽  
Leonardo Zimmer ◽  
Seth B. Dworkin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soot Formation ◽  
Computational Cost ◽  
Concentration Field ◽  
Performance Testing ◽  
Training Data ◽  
Soot Concentration ◽  
Data Set ◽  
Artificial Neural

Soot formation in combustion systems is a growing concern due to its adverse environmental and health effects. It is considered to be a tremendously complicated phenomenon which includes multiphase flow, thermodynamics, heat transfer, chemical kinetics, and particle dynamics. Although various numerical approaches have been developed for the detailed modeling of soot evolution, most industrial device simulations neglect or rudimentarily approximate soot formation due to its high computational cost. Developing accurate, easy to use, and computationally inexpensive numerical techniques to predict or estimate soot concentrations is a major objective of the combustion industry. In the present study, a supervised Artificial Neural Network (ANN) technique is applied to predict the soot concentration fields in ethylene/air laminar diffusion flames accurately with a low computational cost. To gather validated data, eight different flames with various equivalence ratios, inlet velocities, and burner geometries are modeled using the CoFlame code (a computational fluid dynamics (CFD) parallel combustion and soot model) and the Lagrangian histories of soot-containing fluid parcels are computed and stored. Then, an ANN model is developed and optimized using the Levenberg-Marquardt approach. Two different scenarios are introduced to validate the network performance; testing the prediction capabilities of the network for the same eight flames that are used to train the network, and for two new flames that are not within the training data set. It is shown that for both of these cases the ANN is able to predict the overall soot concentration field very well with a relatively low integrated error.

Sign in / Sign up

Export Citation Format

Share Document