A grey-box machine learning based model of an electrochemical gas sensor

•A machine learning based grey-box NOx sensor model is developed in this work.•The model combines physics-based and data-driven sub-models.•Support Vector Machine technique is used for model training/test.•The model can be used for simulation and on-board diagnostics. A grey-box machine learning bas...

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Bibliographic Details
Published in:Sensors and actuators. B, Chemical Chemical, 2020-10, Vol.321, p.128414, Article 128414
Main Authors: Aliramezani, Masoud, Norouzi, Armin, Koch, Charles Robert
Format: Article
Language:English
Subjects:
Ammonia
Combustion engines
Cross sensitivity
Diesel engines
Electrochemical sensor
Exhaust systems
Gas sensors
Grey-box model
Kernels
Machine learning
Nitrogen oxides
NO[formula omitted] emission
On-board diagnostics
Pumping
Sensors
Spark ignition
Support vector machines
Trend analysis
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Summary:•A machine learning based grey-box NOx sensor model is developed in this work.•The model combines physics-based and data-driven sub-models.•Support Vector Machine technique is used for model training/test.•The model can be used for simulation and on-board diagnostics. A grey-box machine learning based model of an electrochemical O2–NOx sensor is developed using the physical understanding of the sensor working principles and a state-of-the-art machine learning technique: support vector machine (SVM). The model is used to predict the sensor response at a wide range of sensor operating conditions in the presence of different concentrations of NOx and ammonia. To prepare a comprehensive training and test data set, the production sensor is first mounted on the exhaust system of a spark ignition, a diesel engine, and then on a fully controlled sensor test rig. The sensor is not modified, rather the sensor working temperature, all of the sensor cell potentials, and the pumping current of the O2 sensing cell are the model inputs that can be varied while the pumping current of the NOx sensing cell is considered as the model output. A 9-feature low order model (LOM) and a 45-feature high order model (HOM) are developed with linear and Gaussian kernels. The model performance and generalizability are then verified by conducting input-output trend analysis. The LOM with Gaussian kernel and the HOM with linear kernel have shown the highest accuracy and the best response trend prediction.
ISSN:0925-4005
1873-3077
DOI:10.1016/j.snb.2020.128414