NOx Emission of a Correlation between the PEMS and SEMS over Different Test Modes and Real Driving Emission

The aim of this study is to verify the reliability of NOx emissions measured using Smart Emissions Measurement System (SEMS) equipment in comparison with the NOx emissions measured using certified Portable Emissions Measurement System (PEMS) equipment. The SEMS equipment is simple system, and it is less expensive than the PEMS equipment, as it comprises an On-Board Diagnostics (OBD) signal from the test vehicle and a NOx sensor. The SEMS equipment based on low-cost sensors has an advantage of building big data, but there are insufficient previous studies comparing of NOx emissions with certified the PEMS equipment. Therefore, this study is important in verifying the suitability of the SEMS equipment by comparing the NOx emissions measured by the various test modes and RDE using the two types of equipment. To analyze the correlation between the PEMS and SEMS equipment, the advanced diesel vehicle was equipped with the two types of equipment to simultaneously measure NOx emissions. After installing the equipment on the test vehicle, it was conducted under various test modes in the laboratory and the Real Driving Emission (RDE) test to verify the correlation of NOx emissions measured by the SEMS equipment. The correlation analysis for the NOx emissions measured by the PEMS and SEMS equipment under various test conditions and the RDE test indicated that the slope of the NOx emissions was approximately equal to 1, and the coefficient of determination was 0.9 or higher. Based on these test results, it was concluded that NOx emissions measured by the PEMS and SEMS equipment are highly similar.

Ammonia Slip Estimation Based on ASC Control-Oriented Modelling And OBD NOx Sensor Cross-Sensitivity Analysis

The incoming emission regulations for internal combustion engines are gradually introducing new pollutant species, which requires greater complexity of the exhaust gas aftertreatment systems concerning layout, control and diagnostics. This is the case of ammonia, which is already regulated in heavy-duty vehicles and to be included in the emissions standards applied to passenger cars. The ammonia is injected into the exhaust gas through urea injections for NOx abatement in selective catalytic reduction (SCR) systems and can be also generated in other aftertreatment systems as three-way catalysts. However, ammonia slip may require removal on a dedicated catalyst called ammonia slip catalyst (ASC). The set consisting of the urea injection system, SCR and ASC requires control and on-board diagnostic tools to ensure high NOx conversion efficiency and minimization of the ammonia slip under real driving conditions. These tasks are based on the use of NOx sensors ZrO2 pumping cell-based, which present as a drawback high cross-sensitivity to ammonia. Consequently, the presence of this species can affect the measurement of NOx and compromise SCR-ASC control strategies. In the present work, a methodology to predict ammonia and NOx tailpipe emissions is proposed. For this purpose, a control-oriented ASC model was developed to use its ammonia slip prediction to determine the cross-sensitivity correction of the NOx sensor placed downstream of the ASC. The model is based on a simplified solution of the transport equations of the species involved in the main ASC reactions. The ammonia slip model was calibrated using steady- and quasi-steady-state tests performed in a Euro 6c diesel engine. Finally, the performance of the proposed methodology to predict NOx and ammonia emissions was evaluated against experimental data corresponding to Worldwide harmonized Light vehicles Test Cycles (WLTC) applying different urea dosing strategies.

Ultrafast dynamics of heme distortion in the O2-sensor of a thermophilic anaerobe bacterium

Heme-Nitric oxide and Oxygen binding protein domains (H-NOX) are found in signaling pathways of both prokaryotes and eukaryotes and share sequence homology with soluble guanylate cyclase, the mammalian NO receptor. In bacteria, H-NOX is associated with kinase or methyl accepting chemotaxis domains. In the O2-sensor of the strict anaerobe Caldanaerobacter tengcongensis (Ct H-NOX) the heme appears highly distorted after O2 binding, but the role of heme distortion in allosteric transitions was not yet evidenced. Here, we measure the dynamics of the heme distortion triggered by the dissociation of diatomics from Ct H-NOX using transient electronic absorption spectroscopy in the picosecond to millisecond time range. We obtained a spectroscopic signature of the heme flattening upon O2 dissociation. The heme distortion is immediately (<1 ps) released after O2 dissociation to produce a relaxed state. This heme conformational change occurs with different proportions depending on diatomics as follows: CO < NO < O2. Our time-resolved data demonstrate that the primary structural event of allostery is the heme distortion in the Ct H-NOX sensor, contrastingly with hemoglobin and the human NO receptor, in which the primary structural events are respectively the motion of the proximal histidine and the rupture of the iron-histidine bond.

Prediction of engine NOx for virtual sensor using deep neural network and genetic algorithm

The Nitrogen Oxides (NOx) from engines aggravate natural environment and human health. Institutional regulations have attempted to protect the human body from them, while car manufacturers have tried to make NOx free vehicles. The formation of NOx emissions is highly dependent on the engine operating conditions and being able to predict NOx emissions would significantly help in enabling their reduction. This study investigates advanced method of predicting vehicle NOx emissions in pursuit of the sensorless engine. Sensors inside the engine are required to measure the operating condition. However, they can be removed or reduced if the sensing object such as the engine NOx emissions can be accurately predicted with a virtual model. This would result in cost reductions and overcome the sensor durability problem. To achieve such a goal, researchers have studied numerical analysis for the relationship between emissions and engine operating conditions. Also, a Deep Neural Network (DNN) is applied recently as a solution. However, the prediction accuracies were often not satisfactory where hyperparameter optimization was either overlooked or conducted manually. Therefore, this study proposes a virtual NOx sensor model based on the hyperparameter optimization. A Genetic Algorithm (GA) was adopted to establish a global optimum with DNN. Epoch size and learning rate are employed as the design variables, and R-squared based user defined function is adopted as the object function of GA. As a result, a more accurate and reliable virtual NOx sensor with the possibility of a sensorless engine could be developed and verified.

Predicting NOx sensor failure in heavy duty trucks using histogram-based random forests

Being able to accurately predict the impending failures of truck components is often associated with significant amount of cost savings, customer satisfaction and flexibility in maintenanceservice plans. However, because of the diversity in the way trucks typically are configured and their usage under different conditions, the creation of accurate prediction models is not an easy task. This paper describes an effort in creating such a prediction model for the NOx sensor, i.e., a component measuring the emitted level of nitrogen oxide in the exhaust of the engine. This component was chosen because it is vital for the truck to function properly, while at the same time being very fragile and costly to repair. As input to the model, technical specifications of trucks and their operational data are used. The process of collecting the data and making it ready for training the model via a slightly modified Random Forest learning algorithm is described along with various challenges encountered during this process. The operational data consists of features represented as histograms, posing an additional challenge for the data analysis task. In the study, a modified version of the random forest algorithm is employed, which exploits the fact that the individual bins in the histograms are related, in contrast to the standard approach that would consider the bins as independent features. Experiments are conducted using the updated random forest algorithm, and they clearly show that the modified version is indeed beneficial when compared to the standard random forest algorithm. The performance of the resulting prediction model for the NOx sensor is promising and may be adopted for the benefit of operators of heavy trucks.