A novel optimal support vector machine ensemble model for NOX emissions prediction of a diesel engine

Liu, Bo; Hu, Jie; Yan, Fuwu; Turkson, Richard Fiifi; Lin, Feng

doi:10.1016/j.measurement.2016.06.015

Cited by 40 publications

(23 citation statements)

References 19 publications

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“…A support vector machine takes advantage of the kernel function to map the input data onto a high-dimensional feature space [34]. Therefore, the non-linear classification issue can be converted into a linear classification issue through non-linear feature mapping.…”

Section: Svm Modeling and Optimization Methodsmentioning

confidence: 99%

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

Zeng

et al. 2017

Sustainability

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Selective catalytic reduction (SCR) is one of the most effective technologies used for eliminating NO x from diesel engines. This paper presents a novel method based on a support vector machine (SVM) and particle swarm optimization (PSO) with grid search (GS) to diagnose the degree of aging of the V 2 O 5 /WO 3 -TiO 2 catalyst in the SCR system. This study shows the aging effect on the performance of a NH 3 slip based closed-loop SCR control system under different aging factors (α), which are defined by the SCR reaction rate (R scr ). A diagnosis of the performance of GS-PSO-SVM has been presented as compared to SVM, GS-SVM and PSO-SVM to get reliable results. The results show that the average prediction diagnosis accuracy of the degree of catalytic aging is up to 93.8%, 93.1%, 92.9% and 92.0% for GS-PSO-SVM, PSO-SVM, GS-SVM and SVM respectively. It is demonstrated that GS-PSO-SVM is able to identify the SCR catalyst's degree of aging, to ultimately assist with fault tolerance in the aging of the SCR catalyst.

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Section: Svm Modeling and Optimization Methodsmentioning

confidence: 99%

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

Zeng

et al. 2017

Sustainability

Self Cite

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“…First, in order to eliminate the negative impact caused by the huge difference between each variable in terms of values, there is a need to normalize each variable ranging from 0 to 1. Second, the total data sample matrix 48,026 × 23 (i.e., the number of the samples multiplied by the number of the input variables for each records) would be considerably complicated and time consuming to model and test for such a high-dimension data samples [22]. It is, therefore, essential for reducing the dimension of the data samples and extracting the typical features from the original data samples.…”

Section: Principal Component Analysismentioning

confidence: 99%

“…It can fuse relatively useful features and extract more sensitive factors through the evolution of the variance contribution rate and the cumulative variance contribution rate of each variable [22]. In this study, PCA was used for reducing variable redundancy for the proposed models.…”

Section: Principal Component Analysismentioning

confidence: 99%

A Modified Bayesian Network Model to Predict Reorder Level of Printed Circuit Board

Kim

Jin

et al. 2018

Applied Sciences

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Featured Application: The research was motivated by the requirement of a printed circuit board (PCB) manufacturer and the application of the work is to identify the repeated PCB orders for batch production according to the predicted reorder level.Abstract: Identifying the printed circuit board (PCB) orders with high reorder frequency for batch production can facilitate production capacity balance and reduce cost. In this paper, the repeated orders identification problem is transformed to a reorder level prediction problem. A prediction model based on a modified Bayesian network (BN) with Monte Carlo simulations is presented to identify related variables and evaluate their effects on the reorder level. From the historically accumulated data, different characteristic variables are extracted and specified for the model. Normalization and principal component analysis (PCA) are employed to reduce differences and the redundancy of the datasets, respectively. Entropy minimization based binning is presented to discretize model variables and, therefore, reduce input type and capture better prediction performance. Subsequently, conditional mutual information and link strength percentage are combined for the establishment of BN structure to avoid the defect of tree augmented naïve BN that easily misses strong links between nodes and generates redundant weak links. Monte Carlo simulation is conducted to weaken the influence of uncertainty factors. The model's performance is compared to three advanced approaches by using the data from a PCB manufacturer and results demonstrate that the proposed method has high prediction accuracy.

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“…The R 2 value between test and train data are 0.9771, 0.8663, 0.8917 and 0.9858 respectively. In [8], the authors generate a model to predict NOx using Support Vector Machines. NOx emissions were predicted with a reasonably good accuracy for both training and testing datasets.…”

Section: Introductionmentioning

confidence: 99%

Predição da Emissão de Particulado Utilizando Algoritmo de Aprendizagem de Máquina

Dellaroza¹,

Lodi²,

Luersen³

2021

Blucher Engineering Proceedings

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One of the requirements of diesel engines certification process is that the engine do not exceed the specific particulate emissions limit in cycle ( _ ). To calculate the _ , a manual process is required. The total amount of mass impregnated in the particulate filter is obtained by weighting the filter on a balance after each test. Therefore, it is not possible to obtain it without human intervention. In order to allow test rigs to operate in automatic mode, without an operator conducting the tests, an automatic way of calculating _ is required. Thus, the aim of this work is to develop a _ prediction model that does not require human intervention. For this, a machine learning approach, based on the random forests algorithm, is used. Data collected from 2012 to 2019 from three test cells of 11 and 13 liters diesel engines of an automotive company, summing up 2500 valid test results, are used as input. This data are employed to train the algorithm and build a prediction model. The prediction model is then validated using another 72 validation tests results. The accuracy of the final model considering a confidence interval of 95% is ±3,00 mg/kWh for the European Transient Cycle, and ±1,96 mg/kWh for the European Stationary Cycle.

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A novel optimal support vector machine ensemble model for NOX emissions prediction of a diesel engine

Cited by 40 publications

References 19 publications

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

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

A Modified Bayesian Network Model to Predict Reorder Level of Printed Circuit Board

Predição da Emissão de Particulado Utilizando Algoritmo de Aprendizagem de Máquina

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