scholarly journals A Deep Transfer NOx Emission Inversion Model of Diesel Vehicles with Multisource External Influence

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhenyi Xu ◽  
Ruibin Wang ◽  
Yu Kang ◽  
Yujun Zhang ◽  
Xiushan Xia ◽  
...  
Keyword(s):  
Working Condition ◽  
Data Distribution ◽  
Feature Space ◽  
Nox Emission ◽  
Monitoring Data ◽  
Emission Model ◽  
Emission Data ◽  
Inversion Model ◽  
Emission Monitoring ◽  
Diesel Vehicle

By installing on-board diagnostics (OBD) on tested vehicles, the after-treatment exhaust emissions can be monitored in real time to construct driving cycle-based emission models, which can provide data support for the construction of dynamic emission inventories of mobile source emission. However, in actual vehicle emission detection systems, due to the equipment installation costs and differences in vehicle driving conditions, engine operating conditions, and driving behavior patterns, it is impossible to ensure that the emission monitoring data of different vehicles always follow the same distribution. The traditional machine learning emission model usually assumes that the training set and test set of emission test data are derived from the same data distribution, and a unified emission model is used for estimation of different types of vehicles, ignoring the difference in monitoring data distribution. In this study, we attempt to build a diesel vehicle NOx emission prediction model based on the deep transfer learning framework with a few emission monitoring data. The proposed model firstly uses Spearman correlation analysis and Lasso feature selection to accomplish the selection of factors with high correlation with NOx emission from multiple sources of external factors. Then, the stacked sparse AutoEncoder is used to map different vehicle working condition emission data into the same feature space, and then, the distribution alignment of different vehicle working condition emission data features is achieved by minimizing maximum mean discrepancy (MMD) in the feature space. Finally, we validated the proposed method with the diesel vehicle OBD data that were collected by the Hefei Environmental Protection Bureau. The comprehensive experiment results show that our method can achieve the feature distribution alignment of emission data under different vehicle working conditions and improve the prediction performance of the NOx inversion model given a little amount of NOx emission monitoring data.

scholarly journals Research on a China 6b Heavy-duty Diesel Vehicle Real-world Engine out NOx Emission Deterioration and NOx Correction for Humidity and Temperature using On-board Sensors and Big Data Approach

2021 ◽  
Author(s):  
Peng Li ◽  
Lin Lü
Keyword(s):  
Big Data ◽  
Working Condition ◽  
Absolute Humidity ◽  
Nox Emission ◽  
Heavy Duty ◽  
Diesel Vehicles ◽  
Vehicles Emissions ◽  
Useful Life ◽  
Heavy Duty Diesel ◽  
Diesel Vehicle

Abstract China VI standard proposed higher requirements for durability of heavy-duty diesel vehicles emissions. Previous research which took advantages of both on-board sensors and big data approach to get the NOx deterioration factor was rather scarce. This paper used big data approach to study the deterioration of engine out NOx emission based on 254,622 km operation data getting from the on-board sensors or ECUs. Meanwhile, a formula for on-board NOx correction for ambient humidity and temperature had been fitted. The analyses revealed that engine out NOx would not be deteriorated during the useful life or even longer (deterioration factor was 1.014 @700,000km). For a steady working condition, the engine out NOx mass flow (g/h) is negatively linearly correlated with absolute humidity (R2 = 0.997). If Ha was lower than 12g/kg, Ha almost had no effect on engine out NOx concentration (ppm). Otherwise, there was also a negatively linear relationship between them (R2 = 0.978). It is hoped that the methods and conclusions of this paper could provide some enlightenment for future NOx emission deterioration research.

PEMS: Monitoring NOx Emissions From Gas Turbines

1995 ◽  
Author(s):  
Wilfred S. Y. Hung ◽  
Fritz Langenbacher
Keyword(s):  
Monitoring System ◽  
Gas Turbines ◽  
High Reliability ◽  
Ambient Air ◽  
Nox Emission ◽  
Federal State ◽  
Nox Emissions ◽  
Emission Model ◽  
Report Generation ◽  
Emission Monitoring

Predictive Emission Monitoring System (PEMS) was developed in 1990 to provide continuous monitoring of NOx emissions from stationary gas turbines with minimum maintenance. This system will meet the Enhanced Monitoring requirements under Title V of the Clean Air Act Amendments of 1990 when these requirements are finalized. The PEMS has been well received by various United States federal, state and local environmental agencies. It has been certified in the state of Colorado, and accepted in Pennsylvania and Texas. This paper reviews the Enhanced Monitoring requirements for gas turbine NOx emissions monitoring and discusses the technical background of the PEMS. The PEMS design is described, including inputs, outputs and operator interface. Experiences with some of the installed systems are presented. The PEMS predicts NOx emissions from turbine control system inputs and measurements of ambient air conditions. The prediction algorithms are based upon a time tested NOx emission model for gas turbines. This model has successfully predicted all measured NOx emission phenomena from gas turbines since 1974. The PEMS has been proven to be accurate within the 20% relative accuracy required for certification. The PEMS operates unattended, with extremely low maintenance and high reliability. Record keeping and report generation are automated. The PEMS is typically integrated into the turbine control and condition monitoring system. The PEMS meets regulatory requirements with a much lower cost than a conventional Continuous Emission Monitoring System (CEMS).

Characteristics of NOx Emission of Light-duty Diesel Vehicle with LNT and SCR system by Season and RDE Phase

2021 ◽  
pp. 146750
Author(s):  
Yongjoo Lee ◽  
Seungil Lee ◽  
Seunghyun Lee ◽  
Hoimyung Choi ◽  
Kyoungdoug Min
Keyword(s):  
Nox Emission ◽  
Light Duty ◽  
Scr System ◽  
Diesel Vehicle

scholarly journals Model based NOx emission monitoring in natural gas fired hot water boilers

2015 ◽  
Vol 48 (30) ◽  
pp. 385-390 ◽  
Author(s):  
Timo Korpela ◽  
Pekka Kumpulainen ◽  
Yrjö Majanne ◽  
Anna Häyrinen
Keyword(s):  
Natural Gas ◽  
Hot Water ◽  
Nox Emission ◽  
Model Based ◽  
Emission Monitoring

Neural Network Based Predictive Emission Monitoring Module for a GE LM2500 Gas Turbine

2010 ◽  
Author(s):  
K. K. Botros ◽  
M. Cheung
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Training Data ◽  
Pipeline System ◽  
Emission Data ◽  
Ambient Temperatures ◽  
Emission Monitoring ◽  
Data Points ◽  
Monitoring Module

A Predictive Emission Monitoring (PEM) model has been developed for a non-DLE GE LM2500 gas turbine used on a natural gas compressor station on the TransCanada Pipeline System in Alberta. The PEM model is based on an optimized Neural Network (NN) architecture which takes four fundamental engine parameters as input variables. The model predicts NOx emission in ppmv-dry-O2 corrected and in kg/hr as NO2. The NN was trained using Continuous Emission Monitoring (CEM) measurements comprising two sets of actual emission data collected over two different dates in 2009, when the ambient ambient temperatures were vastly different (∼1° C and 24 °C), respectively. These training data were supplemented by other emission data generated by GE ‘Cycle-Deck’ tool to generate emission data at different ambient temperatures ranging from −30 to +30 °C. The outcome is a total of 1872 emission data of engine emissions at different operating conditions covering the range of the engine operating parameters (402 data points from CEM and 1470 data points from GE Cycle-Deck). The PEM model comprises a simple single hidden layer perceptron type NN with only two neurons in it. The performance of the NN-based model showed a correlation coefficient greater than 0.99, and error standard deviation of 4.5 ppmv of NOx and 1.4 kg/hr as NO2. Uncertainty analysis was conducted to assess the effects of uncertainties in the engine parameters on the NOx predictions by PEM. It was shown that for uncertainty in the ambient temperature of ±1 °C, the uncertainty in the NOx prediction is ± 0.9 to ±3.5%. Uncertainties of the order of ±1% in the other three input parameters results in uncertainties in NOx predictions by ±2.5 to ±6%. Finally, the PEM model was implemented in the station CEHM (Compressor Equipment Health Monitoring) system and NOx prediction were reported online on a minutely basis. These data are presented here over the first three months since implementation.

scholarly journals CAMS-REG-v4: a state-of-the-art high-resolution European emission inventory for air quality modelling

2021 ◽  
Author(s):  
Jeroen Kuenen ◽  
Stijn Dellaert ◽  
Antoon Visschedijk ◽  
Jukka-Pekka Jalkanen ◽  
Ingrid Super ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Point Source ◽  
Power Plants ◽  
Emission Inventory ◽  
State Of The Art ◽  
Agricultural Waste ◽  
Emission Model ◽  
Air Quality Modelling ◽  
Emission Data

Abstract. This paper presents a state-of-the-art anthropogenic emission inventory developed for the European domain for a 18-year time series (2000–2017) at a 0.1° × 0.05° grid, specifically designed to support air quality modelling. The main air pollutants are included: NOx, SO2, NMVOC, NH3, CO, PM10 and PM2.5 and also CH4. To stay as close as possible to the emissions as officially reported and used in policy assessment, the inventory uses where possible the officially reported emission data by European countries to the UN Framework Convention on Climate Change and the Convention on Long-Range Transboundary Air Pollution as the basis. Where deemed necessary because of errors, incompleteness of inconsistencies, these are replaced with or complemented by other emission data, most notably the estimates included in the Greenhouse gas Air pollution Interaction and Synergies (GAINS) model. Emissions are collected at the high sectoral level, distinguishing around 250 different sector-fuel combinations, whereafter a consistent spatial distribution is applied for Europe. A specific proxy is selected for each of the sector-fuel combinations, pollutants and years. Point source emissions are largely based on reported facility level emissions, complemented by other sources of point source data for power plants. For specific sources, the resulting emission data were replaced with other datasets. Emissions from shipping (both inland and at sea) are based on the results from the a separate shipping emission model where emissions are based on actual ship movement data, and agricultural waste burning emissions are based on satellite observations. The resulting spatially distributed emissions are evaluated against earlier versions of the dataset as well as to alternative emission estimates, which reveals specific discrepancies in some cases. Along with the resulting annual emission maps, profiles for splitting PM and NMVOC into individual component are provided, as well as information on the height profile by sector and temporal disaggregation down to hourly level to support modelling activities. Annual grid maps are available in csv and NetCDF format (Kuenen et al., 2021).

Device Channel Temperature Measurement Using NIR Emission

2017 ◽  
Author(s):  
Alan J. Weger ◽  
Franco Stellari ◽  
Peilin Song
Keyword(s):  
Temperature Measurement ◽  
Integrated Circuit ◽  
Heat Dissipation ◽  
Near Infrared ◽  
Process Development ◽  
Emission Model ◽  
Emission Data ◽  
Channel Temperature ◽  
Nir Emission ◽  
On Chip

Abstract In this paper, we present a technique for device temperature measurement using spontaneous near infrared (NIR) emission from an Integrated Circuit (IC). By leveraging modeling and data analysis, time-integrated emission measurements are used to estimate the temperature increase due to switching activity inside the channel of CMOS transistors. The non-invasive nature of the technique allows one to reliably monitor the temperature of any device on-chip without the need for circuit modifications or dedicated on-chip sensors and with a higher spatial resolution than thermal cameras. This method has important applications for modeling heat dissipation during early process development, localizing hot spots, calibrating on-chip sensors, etc. In this paper, temperature is estimated by fitting empirical emission data to an emission model that can be solved for device channel temperature.

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