Developing a multi-zone model for a HCCI engine to obtain optimal conditions using genetic algorithm

Nazoktabar, Mohsen; Jazayeri, Seyed Ali; Arshtabar, Kamran; Ganji, D.D.

doi:10.1016/j.enconman.2017.12.001

Cited by 22 publications

(4 citation statements)

References 30 publications

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“…The Adaboost algorithm (Nazoktabar et al, 2018) focuses on training a dataset to obtain multiple classification results and integrating them to distinguish samples by weighting the misclassified samples. Adaboost has been applied in many contexts.…”

Section: Related Literaturesmentioning

confidence: 99%

Predicting the reaction efficiency of ginkgo biloba residues pyrolysis by using artificial intelligent algorithms under the background of Carbon Neutrality

Liu

Chen

et al. 2022

Front. Energy Res.

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Since the beginning of 2016, China’s annual emissions of herbal residues (HR) have exceeded 30 million tons. As a kind of solid waste, HR still contains a large amount of organic matter, which requires further industrial extraction procedure. Most of the existing studies are concerned with the feasibility of utilizing traditional Chinese medicine residues, meanwhile there are very few studies regarding the kinetics of pyrolysis in the process of resource utilization of traditional Chinese medicine residues. In this study, we comprehensively studied the kinetics characteristics of raw materials with various heating rates (10, 20, 30, and 40°C/min) using a synchronous thermogravimetric analysis, and we adopted Coats-Redfern model to study the thermal kinetics and thermal analysis of GBR. A novel method combining Genetic algorithm and Adaboost algorithm (GA-Adaboost) is proposed to predict the thermogravimetric curve of the raw plant material with respect to the heating rate and temperature. The experimental result shows that the activation energy of the raw material was determined by the Kissinger-Akahira-Sunose (KAS) (E=148.71kJ/mol), and the correlation coefficient was greater than 0.99. The optimal reaction mechanism determined by the Coats - Redfern method was random nucleation and subsequent growth. The GA-Adaboost model achieved good performance (with a fitting degree of 99.88% on training data, 99.80% on verification data, and MSE of3.4173) while predicting the pyrolysis process of ginkgo biloba residue. This study will provide theoretical basis and technical support for the efficient resource utilization of pharmaceutical residues and reduce environmental pressure.

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Section: Related Literaturesmentioning

confidence: 99%

Predicting the reaction efficiency of ginkgo biloba residues pyrolysis by using artificial intelligent algorithms under the background of Carbon Neutrality

Liu

Chen

et al. 2022

Front. Energy Res.

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“…A detailed kinetic scheme is used to evaluate the auto-ignition of the mixture. Nazoktabar et al [28] also developed a multi-zone model coupled with a detailed chemical kinetic scheme to compute the auto-ignition of the mixture inside the combustion chamber. This model is then used to create a grey-box model to predict the performance and emission parameters in transient conditions.…”

Section: Introductionmentioning

confidence: 99%

“…From an extensive study of the available works, it was possible to find out that while multi-zone models to describe HCCI combustion have been widely presented and developed for many years in the scientific literature, the simulation of pollutant emissions of HCCI engines is still poorly explored in a 0D framework. Predicted emissions in a limited number of engine operating points are shown in [28]. In none of the literature works, extensive validation of pollutant emissions produced by HCCI engines has been found.…”

Section: Introductionmentioning

confidence: 99%

A Tabulated Chemistry Multi-Zone Combustion Model of HCCI Engines Supplied with Pure Fuel and Fuel Blends

et al. 2022

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Homogeneous charge compression ignition is considered a promising solution to face the increasing regulations imposed by the legislator in the transport sector, thanks to pollutant and CO2 emissions reduction. In this work, a quasi-dimensional multi-zone HCCI model integrated with 1D commercial software is developed and validated. It is based on the control mass Lagrangian approach and computes the mixture chemistry evolution through offline tabulation of chemical kinetics (tabulated kinetic of ignition). Thus, the simulation can predict mixture auto-ignition with reduced computational effort and high accuracy. Multi-zone schematization mimics the typical thermal stratification of HCCI engines, controlling the combustion evolution. The model is coupled to sub-models for pollutant emissions estimation. Initially, the tabulated chemistry approach is validated against a chemical kinetics solver applied to a constant-volume homogeneous reactor, considering various fuel blends. The model is then used to simulate the operations of four engines using different fuels (hydrogen, methane, n-heptane, and n-heptane/toluene/ethanol blend), under various boundary conditions. The model predictivity is demonstrated against pressure traces, heat release rate, and noxious emissions. The numerical results showed to adequately agree with measured counterparts (average relative error of 1.3% on in-cylinder pressure peak, average absolute error of 0.95 CAD on pressure peak angle, average relative error of 8.4% on uHCs emissions, absolute error below 1 ppm on NOx emissions) only adapting the thermal stratification to the engines under study. The methodology proved to be a reliable tool to investigate the operation of an HCCI engine, applicable in the development of new engine architecture.

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“…The single-zone model does not require high performance computing process as it neglects the temperature and the inhomogeneity of the composition inside the cylinder. The combustion duration is underestimated, while the pressure peak is overestimated [22]. Therefore, it cannot predict accurately a number of extreme conditions such as high Exhaust Gas Recirculation (EGR) or very lean mixture, owing to the neglection of the temperature and the inhomogeneity of the mixtures.…”

Section: Introductionmentioning

confidence: 99%

Simulation of predictive kinetic combustion of single cylinder HCCI engine

Veza¹,

Said²,

Latiff³

et al. 2020

Preprint

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Homogeneous Charge Compression Ignition (HCCI) engine has attracted great attention due to its improved performance and emissions compared to conventional engines. It can reduce both Nitrogen Oxides (NOx) and Particulate Matter (PM) emissions simultaneously without sacrificing the engine performance. However, controlling its combustion phasing remains a major challenge due to the absence of direct control mechanism. The start of combustion is entirely initiated by the chemical reactions inside the combustion chamber, resulted from the compression of its homogeneous mixtures. Varying some critical engine parameters can play a significant role to control the combustion phasing of HCCI engine. This paper investigates the characteristics of HCCI combustion fuelled with n-heptane (C7H16) using single-zone model computational software. The model enabled the combustion object to vary from cycle to cycle. Detailed simulations were conducted to evaluate the effects of air fuel ratio (AFR), compression ratio (CR) and intake air temperature on the in-cylinder pressure and heat release rate. The simulation results showed that the single-zone model was able to predict the two-stage kinetic combustion of HCCI engine; the Low Temperature Heat Release (LTHR) and the High Temperature Heat Release (HTHR) regions. It was found that minor changes in AFR, CR and inlet air temperature led to major changes in the HCCI combustion phasing.

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Developing a multi-zone model for a HCCI engine to obtain optimal conditions using genetic algorithm

Cited by 22 publications

References 30 publications

Predicting the reaction efficiency of ginkgo biloba residues pyrolysis by using artificial intelligent algorithms under the background of Carbon Neutrality

Predicting the reaction efficiency of ginkgo biloba residues pyrolysis by using artificial intelligent algorithms under the background of Carbon Neutrality

A Tabulated Chemistry Multi-Zone Combustion Model of HCCI Engines Supplied with Pure Fuel and Fuel Blends

Simulation of predictive kinetic combustion of single cylinder HCCI engine

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