Improving the Diagnosis Accuracy of Hydrothermal Aging Degree of V2O5/WO3–TiO2 Catalyst in SCR Control System Using an GS–PSO–SVM Algorithm

Hu, Jie; Zeng, Jiawei; Li, Wei; Yan, Fuwu

doi:10.3390/su9040611

Cited by 5 publications

(6 citation statements)

References 35 publications

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“…Schär et al introduced the popular three-cell three-state model for SCR in (Schär, Onder, Geering, & Elsener, 2004). Using this model, Hu et al simulated catalyst degradation by scaling the NOx reduction rate (Hu, Zeng, Wei, & Yan, 2017). In another paper, they scaled the NH 3 storage capacity, which is estimated using an Extended Kalman Filter (EKF) (Hu, Zeng, & Wei, 2018) (Jiang, Yan, & Zhang, 2019).…”

Section: Intrusive Vs Non-intrusivementioning

confidence: 99%

Diagnostics-oriented Model for Automotive SCR-ASC

Jain¹,

Kuo²,

Meckl

et al. 2023

IJPHM

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This paper presents a diagnostics-oriented aging model for combined Selective Catalytic Reduction (SCR) and Ammonia Slip Catalyst (ASC) system, along with a model-based on-board diagnostic (OBD) method applied to both test-cell data and on-road data from commercial trucks. The key challenge with model development was unavailability of NOx and NH3 measurements between SCR and ASC. Since it would have been very difficult to calibrate both SCR and ASC dynamics without any measurements between SCR and ASC, therefore ASC was modeled using static look-up tables to determine ASC’s NH3 conversion efficiency and its selectivity to NOx and N2O as a function of temperature and flow rate. The traditional three-state single-cell ordinary differential equation (ODE) model was used for SCR. Hot Federal Test Procedure (hFTP) was used to calibrate the model. Cold FTP (cFTP) and Ramped Mode Cycle (RMC) were used for validation. Results show that the SCR-ASC model can capture the aging signatures in tailpipe NOx, NH3, and N2O reasonably well for cFTP, hFTP, and RMC cycles in the testcell data. After slight re-calibration and combining with a simple model for commercial NOx sensor’s cross-sensitivity to NH3, the model works reasonably well for on-road data from commercial trucks. A model-based on-board diagnostic (OBD) method has been presented with enable conditions designed to detect operating conditions suitable for detecting aging signatures, while minimizing false positives and false negatives. The OBD method is applied to both test-cell and real-world truck data with commercial NOx sensors. Results on test-cell data demonstrate the challenges of robust SCR monitoring even on the limited data set used in this work. The model-based enable conditions are shown to be robust but extremely restrictive as the OBD gets enabled at very few points in the test-cell data. Application on truck data showed that the proposed OBD method can be implemented on commercial trucks with limited sensors. In the truck data, the enable conditions were satisfied on many more points than the test-cell data. Results on truck data show encouraging trends between relative degradation level and the number of miles on four trucks. In future work, these trends will be validated using more data from commercial trucks with known aging levels.

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Section: Intrusive Vs Non-intrusivementioning

confidence: 99%

Diagnostics-oriented Model for Automotive SCR-ASC

Jain¹,

Kuo²,

Meckl

et al. 2023

IJPHM

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“…al. [ 16 ] , implements a Machine Learning algorithm (SVM) to estimate the degree of aging and validates the same against a model. This approach is less sensitive to limitations of a model-based OBD such as sensor noise and others.…”

Section: Motivationmentioning

confidence: 99%

Data-Based On-Board Diagnostics for Diesel Engine NOx-Reduction Aftertreatment Systems

Tandale,

Jain,

Meckl

2023

ASME Letters in Dynamic Systems and Control

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The NOx conversion efficiency of a combined Selective Catalytic Reduction and Ammonia Slip Catalyst (SCR-ASC) in a Diesel Aftertreatment (AT) system degrades with time. A novel model-informed data-driven On-Board Diagnostic (OBD) binary classification strategy is proposed in this paper to distinguish an End of Useful Life (EUL) SCR-ASC catalyst from Degreened (DG) ones. An optimized, supervised machine learning model was used for the classification with a calibrated single-cell 3-state Continuous Stirred Tank Reactor (CSTR) observer used for state estimation. Percentage of samples classified as EUL (%EUL), w.r.t. classification boundary of 50%, was used as an objective criterion of classification. The method resulted in 87.5% classification accuracy when tested on 8 day-files from 4 trucks (2 day-files per truck; 1 DG and 1 EUL) operating in real-world on-road conditions. Each day-file had ~86,000 samples of data. Mileage of the same truck was used as ground truth for classification. However, mileage across different trucks cannot be used for classification since the operating conditions would vary across trucks.

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“…SVM is a linear two-class classifier whose purpose is to find the maximum distance between categories and construct a classification hyperplane at the center of the maximum distance [21,22]. These two categories are labeled +1 (positive example) for the case above the hyperplane, and −1 (negative example) for the case below the hyperplane.…”

Section: Support Vector Machinementioning

confidence: 99%

“…We introduce the PSO algorithm to adjust the parameters of SVM [21,22]. Each member of the group is regarded by PSO as a "particle", which moves at a set speed within its search range.…”

Section: Optimization Of the Svm By Psomentioning

confidence: 99%

“…The following are available online at https://www.mdpi.com/article/ 10.3390/photonics8120555/s1, Algorithm S1: RFECV-RF; Algorithm S2: The structure of PSO algorithm; Figure S1: Comparison of the confusion matrix (values) of the four algorithms. References [19][20][21][22] are cited in the supplementary materials Author Contributions: X.L. proposed the concept and designed the experiments.…”

Section: Supplementary Materialsmentioning

confidence: 99%

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A PSO-SVM for Burst Header Packet Flooding Attacks Detection in Optical Burst Switching Networks

2021

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An Optical Burst Switching (OBS) network is vulnerable to Burst Header Packet (BHP) flooding attack. In flooding attacks, edge nodes send BHPs at a high rate to reserve bandwidth for unrealized data bursts, which leads to a waste of bandwidth, a decrease in network performance, and massive data loss. Machine learning techniques are utilized to detect this attack in the OBS network. In this paper, we propose a particle swarm optimization–support vector machine (PSO-SVM) model for detecting BHP flooding attacks, in which the PSO is used to optimize the parameters of the SVM. We use the dataset provided by the UCI warehouse to train and test the model. The experimental results show that the detection accuracy of the PSO-SVM model reaches 95.0%, which is 9.4%, 9.6%, 20.7%, 8% higher than naïve Bayes, SVM, k-nearest neighbor, and decision tree. Although DCNN outperforms our model, it requires more processing and training time. Collectively, our approach is effective and high-efficiency in detecting flooding attacks in optical burst switching networks and maintaining network stability and security.

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Improving the Diagnosis Accuracy of Hydrothermal Aging Degree of V2O5/WO3–TiO2 Catalyst in SCR Control System Using an GS–PSO–SVM Algorithm

Cited by 5 publications

References 35 publications

Diagnostics-oriented Model for Automotive SCR-ASC

Diagnostics-oriented Model for Automotive SCR-ASC

Data-Based On-Board Diagnostics for Diesel Engine NOx-Reduction Aftertreatment Systems

A PSO-SVM for Burst Header Packet Flooding Attacks Detection in Optical Burst Switching Networks

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