Soot Emission Modeling of a Compression Ignition Engine Using Machine Learning

Shahpouri, Saeid; Norouzi, Armin; Hayduk, Christopher; Rezaei, Reza; Shahbakhti, Mahdi; Koch, Charles Robert

doi:10.1016/j.ifacol.2021.11.274

Cited by 18 publications

(17 citation statements)

References 16 publications

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“…Here the results of the NMPC controller with LSTM-based data driven model and imitative LSTM-NMPC controller are compared to a the BM. The BM model here is calibrated ECU tables based on the Cummins production ECU that embedded in GT-power for ESM co-simulation-more details are available in out previous studies [47,34]. Figure 12 presents all the controller inputs and outputs for all three controllers tested.…”

Section: Resultsmentioning

confidence: 99%

“…The combustion model is calibrated using the experimental data. Details regarding the ESM are found in [47,34]. This model has accuracy of ± 5.8%, ± 4.6%, and ± 18.1% in prediction of maximum in-cylinder pressure, intake manifold pressure, and NO x .…”

Section: Engine Simulation Model (Esm)mentioning

confidence: 98%

“…The engine platform in this paper is a 4.5-liter mediumduty Cummins diesel engine. To test the proposed control strategies in simulation an Engine Simulation Model (ESM) of the real engine from our previous work [47,34] is used. The ESM contains several chemical and physical sub-models that simulate the complex combustion processes and gas exchange processes that is occurring during diesel combustion.…”

Section: Engine Simulation Model (Esm)mentioning

confidence: 99%

“…The main contributions of this paper are The structure of the paper is shown in Figure 1 and it includes logging data, data-driven model development, and controller implementation using ESM co-simulation. First step including experimental data collection and detailed physical modeling GT-power was done in our previous papers [47,34]. Then, randomly generated inputs are fed into the GT-power model and the output data is recorded for modeling.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning based Model Predictive Control for Compression Ignition Engines

Norouzi¹,

Shahpouri²,

Gordon³

et al. 2022

Preprint

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Machine learning (ML) and nonlinear model predictive control (NMPC) are used in this paper to minimize the emissions and fuel consumption of a compression ignition engine. In this work machine learning is used for two applications. In the first application, ML is to identify a model for implementation in model predictive control optimization problems. In the second application, ML is used as a replacement of a model predictive controller where the ML controller learns the optimal control action by imitating or mimicking the behavior of the model predictive controller. In this study, a deep recurrent neural network including long-short term memory (LSTM) layers are used to model the emission and performance of a 4.5 liter 4-cylinder Cummins compression ignition engine. This model is then used for model predictive controller implementation. Then, a deep learning scheme is deployed to clone the behavior of the developed controller. In the LSTM integration, a novel scheme is used by augmenting hidden and cell states of the network in an NMPC optimization problem. The developed LSTM-NMPC and the imitative NMPC are compared with the calibrated Cummins Engine Control Unit (ECU) model in an experimentally validated engine simulation platform. Results show a significant reduction in Nitrogen Oxides (NO x ) emissions and a slight decrease in the injected fuel quantity while maintaining the same load. In addition, the imitative NMPC has a similar performance as the NMPC but with two orders of magnitude less computation time.

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Section: Resultsmentioning

confidence: 99%

Section: Engine Simulation Model (Esm)mentioning

confidence: 98%

Section: Engine Simulation Model (Esm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning based Model Predictive Control for Compression Ignition Engines

Norouzi¹,

Shahpouri²,

Gordon³

et al. 2022

Preprint

Self Cite

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“…The challenge with RDE requirements are that test results are significantly affected by various external factors including the ambient conditions, traffic, and driver behavior. Based on RDE legislation, engines should operate cleanly under all con-ditions, which makes engine design and calibration much more challenging (Norouzi et al, 2021b). Complying with RDE legislation is a large shift from the previous legislation.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Integrated with Model Predictive Control for Imitative Optimal Control of Compression Ignition Engines

Norouzi¹,

Shahpouri²,

Gordon³

et al. 2022

Preprint

Self Cite

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The high thermal efficiency and reliability of the compression-ignition engine makes it the first choice for many applications. For this to continue, a reduction of the pollutant emissions is needed. One solution is the use of machine learning (ML) and model predictive control (MPC) to minimize emissions and fuel consumption, without adding substantial computational cost to the engine controller. ML is developed in this paper for both modeling engine performance and emissions and for imitating the behaviour of an Linear Parameter Varying (LPV) MPC. Using a support vector machine-based linear parameter varying model of the engine performance and emissions, a model predictive controller is implemented for a 4.5 Cummins diesel engine. This online optimized MPC solution offers advantages in minimizing the NO x emissions and fuel consumption compared to the baseline feedforward production controller. To reduce the computational cost of this MPC, a deep learning scheme is designed to mimic the behavior of the developed controller. The performance in reducing NOx emissions at a constant load by the imitative controller is similar to that of the online optimized MPC compared to the Cummins production controller. In addition, the imitative controller requires 50 times less computation time compared to that of the online MPC optimization.

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Effect of hybrid nanoparticles MgO and Al₂O₃ added water‐in‐diesel emulsion fueled diesel engine using hybrid deep neural network‐based spotted hyena optimization

Patel¹,

Dhiman

2022

Heat Trans

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The automotive sector is one of the top energy consumers globally compared with any other sector where oil plays the main role. According to http://statista.com (an online platform that displays the stats on oil consumption and reserves), in 2016, the demand for crude oil was about 85.3 million barrels per day worldwide. In 2019, the demand almost reached 100 barrels per day, and then it was reduced to 91 million per day during the 2020 global COVID‐19 crisis. However, it was predicted that the global crude oil requirements will exceed 100 million barrels per day by 2023 and will continue to raise. Also, geographically, not all countries worldwide have the oil reservoirs or the technology to extract oil from the reservoirs. Although the oil demand worldwide is increasing every year, researchers have estimated that there are only 47 years of oil left on the planet earth if the current oil consumption will raise this way. Researchers are looking for finding alternative fuels, such as electrochemical energy (electric vehicles), biodiesel, electricity, ethanol, hydrogen, natural gas, propane, emerging fuels, or the use of such fuels in the existing engines to minimize the use of nonrenewable energy resources. However, researchers are also looking more at fuels based on hybrid nanoparticles added to emulsified fuels. Due to the limitations of utilizing electrochemical energy or biofuels include high charging time, limited millage, harmful exhaust emissions, complicated production process, engine efficiency, the cost of fuel, and the advantages over hybrid nanoparticles added emulsified fuels than biofuels. Hence, after going through numerous researches, in this present experimentation, “hybrid nanoparticles (MgO and Al2O3) added water‐in‐diesel emulsion” are formulated to enhance the quality of emission and improve the performance of the compression ignition engine. The best combination of diesel, MgO, and Al2O3, a water‐in‐diesel (W/D) emulsion blend, is proposed. The results were further validated using deep neural network‐based spotted hyena optimization (DNN‐SHO) prediction and compared with traditional machine learning approaches artificial neural network (ANN), convolution neural network (CNN), regression‐based network (RBN), recurrent neural network (RNN), and DNN. As a result, the best proportions of the proposed nanoparticles added into the W/D bend are identified to be 10% W/D, 50 ppm of MgO, and 50 ppm of Al2O in terms of engine performance and emission characteristics brake thermal efficiency 30.7%, brake‐specific fuel consumption 0.29 kg/kW‐h, CO 0.027 vol%, NOx 855 ppm vol, and HC 12 ppm vol. Besides this, the DNN‐SHO‐based validated outcomes are in good agreement with the experimental values and outperformed other traditional approaches ANN, CNN, RBN, RNN, and DNN used in this study.

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Soot Emission Modeling of a Compression Ignition Engine Using Machine Learning

Cited by 18 publications

References 16 publications

Deep Learning based Model Predictive Control for Compression Ignition Engines

Deep Learning based Model Predictive Control for Compression Ignition Engines

Machine Learning Integrated with Model Predictive Control for Imitative Optimal Control of Compression Ignition Engines

Effect of hybrid nanoparticles MgO and Al₂O₃ added water‐in‐diesel emulsion fueled diesel engine using hybrid deep neural network‐based spotted hyena optimization

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Soot Emission Modeling of a Compression Ignition Engine Using Machine Learning

Cited by 18 publications

References 16 publications

Deep Learning based Model Predictive Control for Compression Ignition Engines

Deep Learning based Model Predictive Control for Compression Ignition Engines

Machine Learning Integrated with Model Predictive Control for Imitative Optimal Control of Compression Ignition Engines

Effect of hybrid nanoparticles MgO and Al2O3 added water‐in‐diesel emulsion fueled diesel engine using hybrid deep neural network‐based spotted hyena optimization

Contact Info

Product

Resources

About

Effect of hybrid nanoparticles MgO and Al₂O₃ added water‐in‐diesel emulsion fueled diesel engine using hybrid deep neural network‐based spotted hyena optimization