Experimental Validation of an Unscented Kalman Filter for Estimating Transient Engine Exhaust Composition with Fourier Transform Infrared Spectroscopy

Wilson, David; Allen, Casey

doi:10.1021/acs.energyfuels.8b02543

Cited by 4 publications

(6 citation statements)

References 39 publications

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“…This renders calibrations (which map calculated absorption spectra to steady compositions) insufficient for inferring the sample composition from FTIR measurements, and the profile of composition evolution with respect to modulation must also be considered. This issue has been discussed extensively in a previous work …”

Section: Methodsmentioning

confidence: 91%

“…The limitations of FTIR spectroscopy regarding transient analysis are addressed with the implementation of a previously developed UKF, which has been experimentally validated against numerous measurements of compositionally evolving samples. This algorithm and its validation have been thoroughly detailed in previous works − and are therefore only briefly explained here. A UKF is an algorithm that couples model predictions with measurements to obtain statistically optimized estimations of the states of a system.…”

Section: Methodsmentioning

confidence: 99%

“…The UKF employs a measurement model to infer the true gas cell composition from biased FTIR measurements of chemically evolving samples. FTIR measurements, as discussed extensively in ref , weigh absorbances (thus, compositions) near the beginning of a forward moving mirror scan (centerburst) higher than absorbances at subsequent times. Since moving mirrors alternate scan directions every measurement, this centerburst weighting creates artificial oscillations in unfiltered emissions data.…”

Section: Methodsmentioning

confidence: 99%

“…This issue has been discussed extensively in a previous work. 34 The limitations of FTIR spectroscopy regarding transient analysis are addressed with the implementation of a previously developed UKF, which has been experimentally validated against numerous measurements of compositionally evolving samples. This algorithm and its validation have been thoroughly detailed in previous works 33− 35 and are therefore only briefly explained here.…”

Section: Energy and Fuelsmentioning

confidence: 99%

“…The model predicts VOC emissions according to the map value that corresponds to the current engine speed and load. To address sample recirculation and spectral IR intensity stationarity issues associated with FTIR measurements of the sample with rapidly evolving composition, a previously developed unscented Kalman filter (UKF) , is employed to filter FTIR measurements. This algorithm utilizes simple models for residence time distribution and sample absorbance evolution to estimate the composition of the sample entering the FTIR gas cell during a measurement period, thereby enabling time-resolved estimates of comprehensive emissions.…”

Section: Introductionmentioning

confidence: 99%

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Exploration of the Hysteresis in Speciated Emissions during Transient Gasoline Engine Combustion

2019

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Emissions of various fuel components (cyclohexane, ethanol, and pentane) and reaction intermediate species (acetylene, ethylene, formaldehyde, and methane) from a multicylinder, port-fuel-injected, spark-ignited gasoline engine undergoing transient loads are measured using Fourier transform infrared (FTIR) spectroscopy. The load profiles explored herein consist of positive and negative load ramps spanning brake mean effective pressures of 2–7 bar lasting 1, 2.5, and 5 s, as well as periodic load ramps of identical magnitudes and durations. Experiments are performed at two constant speed settings of 1500 and 2000 rpm. Fourier transform infrared spectroscopy measurements are processed with a recently developed unscented Kalman filter [WilsonD. Wilson, D. Energy Fuels2017311115611168; WilsonD. Wilson, D. Energy Fuels2018321189911912], which combats the biasing effects of sample recirculation and signal nonstationarity associated with transient FTIR measurements, to improve emission estimations. Emissions during load transients are compared to quasi-steady model predictions and estimated emission stochasticity. Overall, the data shows that transient effects (i.e., load ramp rate, speed/load history, nonstoichiometric equivalence ratio) substantially influence volatile organic compound (VOC) emissions in a deterministic manner, as quasi-steady prediction errors regularly exceed the combined effects of stochasticity and uncertainty. Negative load ramps (from 7 to 2 bar) result in the greatest quasi-steady prediction errors of all load profiles. For the periodic load ramps, the greatest quasi-steady prediction errors of the intermediate and fuel component emissions occur for 1 and 2.5 s load ramps, respectively. In both cases, these errors surpass the 95% confidence interval of statistical significance for each species except cyclohexane. Benzene and toluene emissions are unreported due to low quantities and excessive measurement noise, whereas 1,3-butadiene emissions show minimal relation to engine speed/load. The results of this work suggest that transient and historical effects must be taken into account when predicting VOC engine emissions and that the quasi-steady approach is insufficient.

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Section: Methodsmentioning

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Energy and Fuelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploration of the Hysteresis in Speciated Emissions during Transient Gasoline Engine Combustion

2019

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Ultrasensitive Online NO Sensor Based on a Distributed Parallel Self-Regulating Neural Network and Ultraviolet Differential Optical Absorption Spectroscopy for Exhaled Breath Diagnosis

Zhu,

Gao,

et al. 2024

ACS Sens.

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The concentration of fractional exhaled nitric oxide (FeNO) is closely related to human respiratory inflammation, and the detection of its concentration plays a key role in aiding diagnosing inflammatory airway diseases. In this paper, we report a gas sensor system based on a distributed parallel self-regulating neural network (DPSRNN) model combined with ultraviolet differential optical absorption spectroscopy for detecting ppb-level FeNO concentrations. The noise signals in the spectrum are eliminated by discrete wavelet transform. The DPSRNN model is then built based on the separated multipeak characteristic absorption structure of the UV absorption spectrum of NO. Furthermore, a distributed parallel network structure is built based on each absorption feature region, which is given self-regulating weights and finally trained by a unified model structure. The final self-regulating weights obtained by the model indicate that each absorption feature region contributes a different weight to the concentration prediction. Compared with the regular convolutional neural network model structure, the proposed model has better performance by considering the effect of separated characteristic absorptions in the spectrum on the concentration and breaking the habit of bringing the spectrum as a whole into the model training in previous related studies. Lab-based results show that the sensor system can stably achieve high-precision detection of NO (2.59−750.66 ppb) with a mean absolute error of 0.17 ppb and a measurement accuracy of 0.84%, which is the best result to date. More interestingly, the proposed sensor system is capable of achieving high-precision online detection of FeNO, as confirmed by the exhaled breath analysis.

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Comparison of nonlinear filtering techniques for photonic systems with blackbody radiation

et al. 2020

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This work explores a theoretical solution for noise reduction in photonic systems using blackbody radiators. Traditionally, signal noise can be reduced by increasing the integration time during signal acquisition. However, increasing the integration time during signal acquisition will reduce the acquisition speed of the signal. By developing and applying a filter using a model based on the theoretical equations for blackbody radiation, the noise of the signal can be reduced without increasing integration time. In this work, three filters, extended Kalman filter, unscented Kalman filter (UKF), and extended sliding innovation filter (ESIF), are compared for blackbody photonic systems. The filters are tested on a simulated signal from five scenarios, each simulating different experimental conditions. In particular, the nonlinear filters, UKF and ESIF, showed a significant reduction of noise from the simulated signal in each scenario. The results show great promise for photonic systems using blackbody radiators that require post-process for noise reduction.

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Experimental Validation of an Unscented Kalman Filter for Estimating Transient Engine Exhaust Composition with Fourier Transform Infrared Spectroscopy

Cited by 4 publications

References 39 publications

Exploration of the Hysteresis in Speciated Emissions during Transient Gasoline Engine Combustion

Exploration of the Hysteresis in Speciated Emissions during Transient Gasoline Engine Combustion

Ultrasensitive Online NO Sensor Based on a Distributed Parallel Self-Regulating Neural Network and Ultraviolet Differential Optical Absorption Spectroscopy for Exhaled Breath Diagnosis

Comparison of nonlinear filtering techniques for photonic systems with blackbody radiation

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