scholarly journals Impact of biomass burning emission on total peroxy nitrates: fire plume identification during the BORTAS campaign

2016 ◽  
Vol 9 (11) ◽  
pp. 5591-5606 ◽  
Author(s):  
Eleonora Aruffo ◽  
Fabio Biancofiore ◽  
Piero Di Carlo ◽  
Marcella Busilacchio ◽  
Marco Verdecchia ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Atmospheric Pressure ◽  
Thermal Dissociation ◽  
Ann Model ◽  
Fire Plume ◽  
Pressure Threshold ◽  
Peroxy Nitrates ◽  
Artificial Neural Network Ann ◽  
The Impact

Abstract. Total peroxy nitrate ( ∑ PN) concentrations have been measured using a thermal dissociation laser-induced fluorescence (TD-LIF) instrument during the BORTAS campaign, which focused on the impact of boreal biomass burning (BB) emissions on air quality in the Northern Hemisphere. The strong correlation observed between the  ∑ PN concentrations and those of carbon monoxide (CO), a well-known pyrogenic tracer, suggests the possible use of the  ∑ PN concentrations as marker of the BB plumes. Two methods for the identification of BB plumes have been applied: (1)  ∑ PN concentrations higher than 6 times the standard deviation above the background and (2)  ∑ PN concentrations higher than the 99th percentile of the  ∑ PNs measured during a background flight (B625); then we compared the percentage of BB plume selected using these methods with the percentage evaluated, applying the approaches usually used in literature. Moreover, adding the pressure threshold ( ∼  750 hPa) as ancillary parameter to  ∑ PNs, hydrogen cyanide (HCN) and CO, the BB plume identification is improved. A recurrent artificial neural network (ANN) model was adapted to simulate the concentrations of  ∑ PNs and HCN, including nitrogen oxide (NO), acetonitrile (CH3CN), CO, ozone (O3) and atmospheric pressure as input parameters, to verify the specific role of these input data to better identify BB plumes.

scholarly journals Impact of Biomass Burning emission on total peroxy nitrates: fire plume identification during the BORTAS campaign

2016 ◽  
Author(s):  
Eleonora Aruffo ◽  
Fabio Biancofiore ◽  
Piero Di Carlo ◽  
Marcella Busilacchio ◽  
Marco Verdecchia ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Atmospheric Pressure ◽  
Input Data ◽  
Thermal Dissociation ◽  
Ann Model ◽  
Fire Plume ◽  
Pressure Threshold ◽  
Peroxy Nitrates ◽  
The Impact

Abstract. The total peroxy nitrates (∑PNs) concentrations have been measured using a thermal dissociation laser induced fluorescence (TD-LIF) instrument during the BORTAS campaign, which focused on the impact of boreal biomass burning emissions on air quality in the Northern hemisphere. The strong correlation observed between the ∑PNs concentrations and those of the carbon monoxide (CO), a well-known pyrogenic tracer, suggests the possible use of the ∑PNs concentrations as marker of the biomass burning (BB) plumes. We applied both statistical and percentile methods to the ∑PNs concentrations, comparing the percentage of BB plume selected using these methods with the percentage evaluated applying the approaches usually used in literature. Moreover, adding the pressure threshold (~ 750 hPa) to ∑PNs, HCN and CO, as ancillary parameter, the BB plume identification is improved. An artificial recurrent neural network (ANN) model was adapted to simulate the concentrations of ∑PNs and the HCN including as input parameters ∑PNs, HCN, CO and atmospheric pressure, to verify the specific role of these input data to better identify BB plumes.

scholarly journals Production of peroxy nitrates in boreal biomass burning plumes over Canada during the BORTAS campaign

2016 ◽  
Vol 16 (5) ◽  
pp. 3485-3497 ◽  
Author(s):  
Marcella Busilacchio ◽  
Piero Di Carlo ◽  
Eleonora Aruffo ◽  
Fabio Biancofiore ◽  
Cesare Dari Salisburgo ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Forest Fire ◽  
Biomass Burning ◽  
Forest Fires ◽  
Chemical Mechanism ◽  
Fire Emissions ◽  
Fire Plumes ◽  
Peroxy Nitrates ◽  
In Fire ◽  
The Impact

Abstract. The observations collected during the BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites (BORTAS) campaign in summer 2011 over Canada are analysed to study the impact of forest fire emissions on the formation of ozone (O3) and total peroxy nitrates ∑PNs, ∑ROONO2). The suite of measurements on board the BAe-146 aircraft, deployed in this campaign, allows us to calculate the production of O3 and of  ∑PNs, a long-lived NOx reservoir whose concentration is supposed to be impacted by biomass burning emissions. In fire plumes, profiles of carbon monoxide (CO), which is a well-established tracer of pyrogenic emission, show concentration enhancements that are in strong correspondence with a significant increase of concentrations of ∑PNs, whereas minimal increase of the concentrations of O3 and NO2 is observed. The ∑PN and O3 productions have been calculated using the rate constants of the first- and second-order reactions of volatile organic compound (VOC) oxidation. The ∑PN and O3 productions have also been quantified by 0-D model simulation based on the Master Chemical Mechanism. Both methods show that in fire plumes the average production of ∑PNs and O3 are greater than in the background plumes, but the increase of ∑PN production is more pronounced than the O3 production. The average ∑PN production in fire plumes is from 7 to 12 times greater than in the background, whereas the average O3 production in fire plumes is from 2 to 5 times greater than in the background. These results suggest that, at least for boreal forest fires and for the measurements recorded during the BORTAS campaign, fire emissions impact both the oxidized NOy and O3,  but (1 ∑PN production is amplified significantly more than O3 production and (2) in the forest fire plumes the ratio between the O3 production and the ∑PN production is lower than the ratio evaluated in the background air masses, thus confirming that the role played by the ∑PNs produced during biomass burning is significant in the O3 budget. The implication of these observations is that fire emissions in some cases, for example boreal forest fires and in the conditions reported here, may influence more long-lived precursors of O3 than short-lived pollutants, which in turn can be transported and eventually diluted in a wide area.

scholarly journals Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2390 ◽  
Author(s):  
Olalekan Alade ◽  
Dhafer Al Shehri ◽  
Mohamed Mahmoud ◽  
Kyuro Sasaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heavy Oil ◽  
Atmospheric Pressure ◽  
Predictive Accuracy ◽  
Empirical Models ◽  
Viscosity Data ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The viscosity data of two heavy oil samples X and Y, with asphaltene contents 24.8% w/w and 18.5% w/w, respectively, were correlated with temperature and pressure using empirical models and the artificial neural network (ANN) approach. The viscosities of the samples were measured over a range of temperatures between 70 °C and 150 °C; and from atmospheric pressure to 7 MPa. It was found that the viscosity of sample X, at 85 °C and atmospheric pressure (0.1 MPa), was 1894 cP and that it increased to 2787 cP at 7 MPa. At 150 °C, the viscosity increased from 28 cP (at 0.1 MPa) to 33 cP at 7 MPa. For sample Y, the viscosity at 70 °C and 0.1 MPa increased from 2260 cP to 3022 cP at 7 MPa. At 120 °C, the viscosity increased from 65 cP (0.1 MPa) to 71 cP at 7 MPa. Notably, using the three-parameter empirical models (Mehrotra and Svrcek, 1986 and 1987), the correlation constants obtained in this study are very close to those that were previously obtained for the Canadian heavy oil samples. Moreover, compared to other empirical models, statistical analysis shows that the ANN model has a better predictive accuracy (R2 ≈ 1) for the viscosity data of the heavy oil samples used in this study.

An ANN Model to Estimate the Impact of Tea Process Parameters on Tea Quality

2015 ◽  
Vol 24 (09) ◽  
pp. 1550139
Author(s):  
Debashis Saikia ◽  
Diganta Kumar Sarma ◽  
P. K. Boruah ◽  
Utpal Sarma
Keyword(s):  
Process Condition ◽  
Correlation Study ◽  
Sensor Nodes ◽  
Quality Data ◽  
Inlet Temperature ◽  
Ann Model ◽  
Classification Rate ◽  
Tea Quality ◽  
Artificial Neural Network Ann ◽  
The Impact

Present study deals with the development of an artificial neural network (ANN)-based technique for tea quality quantification by monitoring fermentation and drying condition of the tea processing stages. An RS485 network-based instrumentation system has been developed and implemented for data collection for these two stages. Three calibrated sensor nodes are installed in the fermentation room due to its larger floor area to collect temperature and relative humidity (RH). Dryer inlet temperature is recorded using a calibrated thermocouple-based sensor node. From seven input parameters and target quality data obtained from tea taster, the ANN model has been developed to find the correlation between the process condition and the tea quality. From the correlation study, more than 90% classification rate is obtained from the model. The model is also validated with some independent data showing more than 60% correlation. Error in terms of root mean square error (RMSE) is about 0.17. This model will be helpful for improvement of tea quality.

scholarly journals The Concept of Synapse against Popular Opinion

2019 ◽  
pp. 1-3
Author(s):  
Jyh-Woei Lin
Keyword(s):  
Electrical Energy ◽  
Long Term Potentiation ◽  
Brain Diseases ◽  
Ann Model ◽  
Neuron Network ◽  
Optimal Weights ◽  
Operation Process ◽  
Biological Neuron ◽  
Artificial Neural Network Ann

Emulation of the operation process in the human brain was performed by Artificial Neural Network (ANN). The new comments of this study with the concept of progressive tense like an action to new Comparison ANN with Biological Neuron Network were stated against popular opinions. However, their opinions just pointed out the role of Synapse as the weights in the framework of ANN. In this paper, another concept was better suggested. The role of Synapse should be treated as the weights of ANN, which connect two neurons of two hidden different layers. There was a new proposed opinion in this study when an accurate ANN model was built with optimal weights. The role of Synapse should be both the converting the action potential into electrical energy and chemical energy and synaptic strengthening corresponding to long-term potentiation (LTP) in Biological Neuron Network. From the concept of pharmacology, the action of updating weights with optimal values after training more data, was similar as keeping a normal converting for LTP just using medicaments for resisting some ageing brain diseases e.g. Dementia. The new proposed opinion by comparing both Neural Networks should be reasonable in this study.

scholarly journals Antioxidant Activity of Selected Polyphenolics in Yeast Cells: The Case Study of Montenegrin Merlot Wine

Molecules ◽  
2018 ◽  
Vol 23 (8) ◽  
pp. 1971 ◽  
Author(s):  
Neda Đorđević ◽  
Nevena Todorović ◽  
Irena Novaković ◽  
Lato Pezo ◽  
Boris Pejin ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Antioxidant Defense System ◽  
Yeast Cells ◽  
Total Phenolic ◽  
Ann Model ◽  
Phenolic Components ◽  
Stressed Cells ◽  
Pre Treatment ◽  
Artificial Neural Network Ann ◽  
The Impact

Screens of antioxidant activity (AA) of various natural products have been a focus of the research community worldwide. This work aimed to differentiate selected samples of Merlot wines originated from Montenegro, with regard to phenolic profile and antioxidant capacity studied by survival rate, total sulfhydryl groups and activities of glutathione peroxidase (GPx), glutathione reductase and catalase in H2O2–stressed Saccharomyces cerevisiae cells. In this study, DPPH assay was also performed. Higher total phenolic content leads to an enhanced AA under both conditions. The same trend was observed for catechin and gallic acid, the most abundant phenolics in the examined wine samples. Finally, the findings of an Artificial Neural Network (ANN) model were in a good agreement (r2 = 0.978) with the experimental data. All tested samples exhibited a protective effect in H2O2–stressed yeast cells. Pre-treatment with examined wines increased survival in H2O2–stressed cells and shifted antioxidative defense towards GPx–mediated defense. Finally, sensitivity analysis of obtained ANN model highlights the complexity of the impact that variations in the concentrations of specific phenolic components have on the antioxidant defense system.

scholarly journals Modelling of an Atmospheric–Pressure Air Glow Discharge Operating in High–Gas Temperature Regimes: The Role of the Associative Ionization Reactions Involving Excited Atoms

Plasma ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 12-26
Author(s):  
Ezequiel Cejas ◽  
Beatriz Mancinelli ◽  
Leandro Prevosto
Keyword(s):  
Atmospheric Pressure ◽  
Vibrational Excitation ◽  
Thermal Dissociation ◽  
Ionization Mechanism ◽  
Gas Temperature ◽  
Excited Atoms ◽  
Temperature Regimes ◽  
Ionization Reaction ◽  
Associative Ionization

A model of a stationary glow-type discharge in atmospheric-pressure air operated in high-gas-temperature regimes (1000 K < Tg < 6000 K), with a focus on the role of associative ionization reactions involving N(2D,2P)-excited atoms, is developed. Thermal dissociation of vibrationally excited nitrogen molecules, as well as electronic excitation from all the vibrational levels of the nitrogen molecules, is also accounted for. The calculations show that the near-threshold associative ionization reaction, N(2D) + O(3P) → NO+ + e, is the major ionization mechanism in air at 2500 K < Tg < 4500 K while the ionization of NO molecules by electron impact is the dominant mechanism at lower gas temperatures and the high-threshold associative ionization reaction involving ground-state atoms dominates at higher temperatures. The exoergic associative ionization reaction, N(2P) + O(3P) → NO+ + e, also speeds up the ionization at the highest temperature values. The vibrational excitation of the gas significantly accelerates the production of N2(A3∑u+) molecules, which in turn increases the densities of excited N(2D,2P) atoms. Because the electron energy required for the excitation of the N2(A3∑u+) state from N2(X1∑g+, v) molecules (e.g., 6.2 eV for v = 0) is considerably lower than the ionization energy (9.27 eV) of the NO molecules, the reduced electric field begins to noticeably fall at Tg > 2500 K. The calculated plasma parameters agree with the available experimental data.

scholarly journals Prediction Model for Random Variation in FinFET Induced by Line-Edge-Roughness (LER)

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 455
Author(s):  
Jinwoong Lee ◽  
Taeeon Park ◽  
Hongjoon Ahn ◽  
Jihwan Kwak ◽  
Taesup Moon ◽  
...  
Keyword(s):  
Prediction Model ◽  
Threshold Voltage ◽  
Drain Current ◽  
Ann Model ◽  
Line Edge Roughness ◽  
Threshold Voltages ◽  
Edge Roughness ◽  
Artificial Neural Network Ann ◽  
The Impact ◽  
Random Variation

As the physical size of MOSFET has been aggressively scaled-down, the impact of process-induced random variation (RV) should be considered as one of the device design considerations of MOSFET. In this work, an artificial neural network (ANN) model is developed to investigate the effect of line-edge roughness (LER)-induced random variation on the input/output transfer characteristics (e.g., off-state leakage current (Ioff), subthreshold slope (SS), saturation drain current (Id,sat), linear drain current (Id,lin), saturation threshold voltage (Vth,sat), and linear threshold voltage (Vth,lin)) of 5 nm FinFET. Hence, the prediction model was divided into two phases, i.e., “Predict Vth” and “Model Vth”. In the former, LER profiles were only used as training input features, and two threshold voltages (i.e., Vth,sat and Vth,lin) were target variables. In the latter, however, LER profiles and the two threshold voltages were used as training input features. The final prediction was then made by feeding the output of the first model to the input of the second model. The developed models were quantitatively evaluated by the Earth Mover Distance (EMD) between the target variables from the TCAD simulation tool and the predicted variables of the ANN model, and we confirm both the prediction accuracy and time-efficiency of our model.

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