This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Hybrid Physical and Machine Learning-Oriented Modeling Approach to Predict Emissions in a Diesel Compression Ignition Engine
Technical Paper
2021-01-0496
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Event:
SAE WCX Digital Summit
Language:
English
Abstract
The development and calibration of modern combustion engines is challenging in the area of continuously tightening emission limits and the necessity for meeting real driving emissions regulations. A focus is on the knowledge of the internal engine processes and the determination of pollutants formations in order to predict the engine emissions. A physical model-based development provides an insight into hardly measurable phenomena properties and is robust against changing input data. With increasing modeling depth the required computing capacities increase. As an alternative to physical modeling, data-driven machine learning methods can be used to enable high-performance modeling accuracy. However, these are dependent on the learned data. To combine the performance and robustness of both types of modeling a hybrid application of data-driven and physical models is developed in this paper as a grey box model for the exhaust emission prediction of a commercial vehicle diesel engine. Internal engine processes are physically investigated to determine combustion characteristic quantities influencing the formation of NOx, CO, HC and soot emissions. With the physically modeled inputs, models based on machine learning methods, including Support Vector Machine and Feedforward Neural Network, are developed for emission modeling. The models are trained using the data from a commercial vehicle engine, validated against different hyperparameters and network architectures and tested against each other at 772 different operating points. A comparison is made to black box models formed from the measured data. In general, feedforward neural networks and support vector machines were enhanced by selecting the physically modeled inputs. The feedforward neural networks for HC and soot modeling were improved by approximately 20% and 10% with respect to the RMSE of the test data. For the support vector machines, CO and soot modeling benefited the most by 30% and 20% respectively of the RMSE of the test data. For a trained NOx model based on low load data its coefficient of determination regarding test data by high load is increased from 0.807 to 0.908.
Recommended Content
Authors
Topic
Citation
Mohammad, A., Rezaei, R., Hayduk, C., Delebinski, T. et al., "Hybrid Physical and Machine Learning-Oriented Modeling Approach to Predict Emissions in a Diesel Compression Ignition Engine," SAE Technical Paper 2021-01-0496, 2021, https://doi.org/10.4271/2021-01-0496.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 | ||
Unnamed Dataset 2 |
Also In
References
- Office of Energy Analysis 2020
- Khurana , S. , Saxena , S. , Jain , S. , and Dixit , A. Predictive Modeling of Engine Emissions using Machine Learning: A Review Mater. Today Proc. 2020
- Rezaei , R. , Hayduk , C. , Alkan , E. , Kemski , T. et al. Hybrid Phenomenological and Mathematical-Based Modeling Approach for Diesel Emission Prediction 2020
- Yusri , I.M. , Majeed , A.P.P.A. , Mamat , R. , Ghazali , M.F. et al. A Review on the Application of Response Surface Method and Artificial Neural Network in Engine Performance and Exhaust Emissions Characteristics in Alternative Fuel Renew. Sustain. Energy Rev. 90 665 686 2018
- Han , H.-G. , Chen , Q. , and Qiao , J.-F. An Efficient Self-Organizing RBF Neural Network for Water Quality Prediction Neural Networks 24 7 717 725 2011
- Yegnanarayana , B. Artificial Neural Networks PHI Learning Pvt. Ltd. 2009
- Guhmann , C. , and Riedel , J.M. Comparison of Identification Methods for Nonlinear Dynamic Systems 6th Des. Exp. Engine Dev. Berlin, Ger. Expert verlag, Renningen 41 53 2011
- Solomatine , D. , See , L.M. , and Abrahart , R.J. Data-Driven Modelling: Concepts, Approaches and Experiences ” Practical Hydroinformatics Springer 2009 17 30
- Widodo , A. , and Yang , B.-S. Support Vector Machine in Machine Condition Monitoring and Fault Diagnosis Mech. Syst. Signal Process. 21 6 2560 2574 2007
- Moraes , R. , Valiati , J.F. , and Neto , W.P.G. Document-Level Sentiment Classification: An Empirical Comparison between SVM and ANN Expert Syst. Appl. 40 621 633 2013
- Ren , J. ANN vs. SVM: Which One Performs Better in Classification of Mccs in Mammogram Imaging Knowledge-Based Syst. 26 144 153 2012
- Ahmad , A.S. et al. A Review on Applications of ANN and SVM for Building Electrical Energy Consumption Forecasting Renew. Sustain. Energy Rev. 33 102 109 2014
- De Giorgi , M.G. , Campilongo , S. , Ficarella , A. , and Congedo , P.M. Comparison between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN) Energies 7 8 5251 5272 2014
- Niu , X. , Yang , C. , Wang , H. , and Wang , Y. Investigation of ANN and SVM Based on Limited Samples for Performance and Emissions Prediction of a CRDI-Assisted Marine Diesel Engine Appl. Therm. Eng. 111 1353 1364 2017
- Bidarvatan , M. , Thakkar , V. , Shahbakhti , M. , Bahri , B. et al. Grey-Box Modeling of HCCI Engines Appl. Therm. Eng. 70 1 397 409 2014
- Omidvarborna , H. , Kumar , A. , and Kim , D.-S. Recent Studies on Soot Modeling for Diesel Combustion Renew. Sustain. Energy Rev. 48 635 647 2015
- Lang , M. , Bloch , P. , Koch , T. , Eggert , T. et al. Application of a Combined Physical and Data-Based Model for Improved Numerical Simulation of a Medium-Duty Diesel Engine Automot. Engine Technol. 5 1 1 20 2020
- Gamma Technologies Inc
- Pedregosa , F. et al. Scikit-Learn: Machine Learning in Python Journal of Machine Learning Research 2825 2830 2011
- Rezaei , R. , Eckert , P. , Seebode , J. , and Behnk , K. Zero-Dimensional Modeling of Combustion and Heat Release Rate in DI Diesel Engines SAE International Journal of Engines 2012
- Rezaei , R. , Dinkelacker , F. , Tilch , B. , Delebinski , T. et al. Phenomenological Modeling of Combustion and NO x Emissions Using Detailed Tabulated Chemistry Methods in Diesel Engines International Journal of Engine Research 2015
- Vapnik , V. , and Lerner , A. Pattern Recognition using Generalized Portrait Method Automation and Remote Control 24 774 780 1963
- Drucker , H. , Burges , C.J. , Kaufman , L. , Smola , A.J. et al. Support Vector Regression Machines Advances in Neural Information Processing Systems 155 161 1997
- Smola , A.J. , and Schölkopf , B. A Tutorial on Support Vector Regression Statistics and Computing 14 199 222 2004
- Vapnik , V. The Nature of Statistical Learning Theory Springer Science and Business Media 2013
- Chang , C. , and Lin , C. LIBSVM: A Library for Support Vector Machines ACM Transactions on Intelligent Systems and Technology 2 2 1 27 27, 2011
- Fan , R.-E. , Chen , P.-H. , and Lin , C.-J. Working Set Selection using Second Order Information for Training SVM Journal of Machine Learning Research 6 1889 1918 2005
- Karush , W. Minima of Functions of Several Variables with Inequalities as Side Constraints Dept. of Mathematics Univ. of Chicago 1939
- Kuhn , H.W. , and Tucker , A.W. Nonlinear Programming Proc. 2nd Berkeley Symposium on Mathematical Statistics and Probalistics 481 492 Berkeley 1951
- Boser , B.E. , Guyon , I.M. , and Vapnik , V.N. A Training Algorithm for Optimal Margin Classifiers Haussler , D. Proceedings of the Annual Conference on Computational Learning Theory 144 152 Pittsburgh, PA July 1992 ACM Press
- Aizerman , M.A. , Braverman , É.M. , and Rozonoér , L.I. Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning Automation and Remote Control 25 821 837 1964
- Nelles , O. Nonlinear System Identification - From Classical Approaches to Neural Networks and Fuzzy Models 2001
- Frochte , J. Maschinelles Lernen - Grundlagen und Algorithmen in Python 2019
- LeCun , Y. , Bottou , L. , Orr , G.B. , and Müller , K.-R. Efficient BackProp Neural Networks: Trick of the Trade Springer 1998
- Scales , L.E. Introduction to Non-Linear Optimization Computer and Science Series Macmillan, London 1985
- Lui , D.C. , and Nocedal , J. On the Limited Memory BFGS Method for Large Scale Optimization Mathematical Proramming 45 503 528 1989
- Merker , G. , Schwarz , C. Grundlagen Verbrennungsmotoren - Simulation der Gemischbildung, Verbrennung und Schadstoffbildung und Aufladung - 4. Auflage Vieweg + Teubner 2009