Dynamic behaviors of coking process during pyrolysis of China aviation kerosene RP-3

JP-10 is a potential endothermic hydrocarbon fuel (EHF) with a high energy density for the regenerative cooling technology of advanced aircrafts. In this work, pyrolysis and coking of JP-10 were experimentally studied using an electrically heated tube as a flowing reactor under supercritical conditions (4.5 MPa, 550–735 °C). For the supercritical pyrolysis, dicyclopentadiene, exo-TCD4e, and indane/indene were observed with relatively higher selectivity at low conversion, and the selectivities of typical products (ethene, propene, CPD, cyclopentene, and benzene) were lower compared with that under atmospheric pressure, possibly because of the enhanced bimolecular reactions. The heat sink of JP-10 was approximately 2.5 MJ/kg ascribed to the severe coke formation during the pyrolysis. Further characterizations on cokes indicated that the coke in the bulk fluid was about 70–170 times higher than that deposited on the wall, attributed to rapid formation of polycyclic aromatic hydrocarbons (PAHs) of pyrolysis products rather than the wall catalysis.

Section: Resultsmentioning

confidence: 99%

Supercritical Pyrolysis and Coking of JP-10 in Regenerative Cooling Channels

Pan

Zhang

et al. 2020

“…Similar patterns were also obtained in recent theoretical studies, in which the concentration of the coke precursor increased dramatically during passage through the tube from inlet to under high wall temperature and complicated physical and chemical conditions. 13,29,30 As for the tube with bifunctional coating, the amount of coke deposits along the tube is extremely low in general. Evidently, the coke deposits become obvious at the position of 62 cm with a value of 0.45 mg/cm 2 , which is only 4.1% of the amount of coke deposits at the same position of the bare tube.…”

Section: Energy and Fuelsmentioning

confidence: 99%

Efficient Coke Inhibition in Supercritical Thermal Cracking of Hydrocarbon Fuels by a Little Ethanol over a Bifunctional Coating

Yang

Yuan

et al. 2017

Coke inhibition is one of the key issues for hydrocarbon fuel cracking. In the work reported in this paper, controllable cracking with greatly reduced coke deposits has been realized by the addition of a little ethanol over a bifunctional coating. The coating, consisting of perovskite and phosphotungstic acid, is prepared in a nickel-based super alloy tube reactor (diam. 3 mm × 0.5 mm × 1000 mm) by the wash-coating method. Scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy, and X-ray diffraction are utilized to characterize the morphology and phase composition of the coating and cokes. The results show that BaWO4, BaCeO3, SiO2, and H3PW12O40 coexist in the 4.2 μm coating with a uniform distribution. The anti-coking tests were conducted during the supercritical thermal cracking of RP-3 Chinese jet fuel with a flow rate of 1 g/s for 30 min at 700 °C and 4 MPa. The results show that the efficiency of coke inhibition reaches up to 96%, and the stability (i.e., pressure drop of tube reactor and cooler) of the system has been effectively improved. The deposited cokes were characterized by temperature-programmed oxidation and SEM. The pyrolysis products, including gas and liquid, were also analyzed. The results indicate that the strategy based on ethanol and a bifunctional coating not only plays an important role in eliminating the coke deposits on the reactor tube walls but also reduces the amount of typical coke precursors related to the aromatic condensation cokes. A possible mechanism for the process has been proposed. In general, phosphotungstic acid in the coating is capable of catalyzing the dehydration of ethanol for the production of water. Meanwhile, the perovskite structures can remove coke deposits on the coating through carbon–steam gasification reaction.

“…The structure–chemical reactivity relationship at molecular level is useful in understanding the engine combustion reactions for combustion efficiency improvement and pollutant emission control. − RP-3 is a widely used aviation kerosene in China, which may have hundreds of fuel components mainly composed of C6–C17 hydrocarbons. − The varied molecular structures of components in real RP-3 fuel in consequence lead to the complicated evolution behavior of intermediate species generated in the combustion process that is difficult to fully characterize experimentally. Fuel surrogates containing 2–4 components are often built for constructing the detailed and reduced chemical reaction mechanisms for use in the computational fluid dynamics studies of engine combustion.…”

Section: Introductionmentioning

confidence: 99%

Refining Fuel Composition of RP-3 Chemical Surrogate Models by Reactive Molecular Dynamics and Machine Learning

Han

Guo

et al. 2020

A simple chemical surrogate fuel model may not be able to fully reproduce the chemical behavior in real fuel combustion. A structure−chemical reactivity relationship at the molecular level is believed to be useful in tuning the chemical composition of reported surrogate fuel models. This work proposes an approach to predict a component fraction of a RP-3 surrogate fuel model with a combined method of ReaxFF molecular dynamics (MD) simulations and machine learning. There are four major steps to get a refined surrogate fuel model on the basis of parent RP-3 fuel models with two, three, or four components. Step 1 helps to prepare chemical reactivity data as the input of machine learning. The chemical reactivity data are described by the oxidation reactions in terms of dynamic species concentration with time that were prepared with ReaxFF MD simulations for each derived fuel model generated from the RP-3 parent fuel model by randomly changing its component fraction.Step 2 fits a component fraction prediction model between a single surrogate component fraction and its chemical reactivity data among the common reaction space in oxidation of possible RP-3 surrogates and a 45-component RP-3 fuel with the machine-learning method. The best performance model of LightGBM was selected among the machine-learning models of linear regression, support vector regression, and LightGBM based on the training error evaluation. Each refined component fraction of RP-3 surrogate models was predicted one by one using the trained LightGBM model with the input of dynamic species concentration data of the 45-component RP-3 fuel model better representing real RP-3 fuel. Step 3 helps to select one refined surrogate model among the predicted surrogate models with two, three, or four components by comparing the chemical reactivity deviation of important species with additional ReaxFF MD simulations under the conditions of heat-up, isothermal oxidation, and isothermal pyrolysis. The optimal model of the refined threecomponent RP-3 surrogate is validated in step 4 where its predicted ignition delay time is found to be closer to the reported experimental data of the real RP-3 fuel than to its parent surrogate fuel model. This work suggests that the proposed computational approach by combining ReaxFF MD simulations and machine learning should be potentially useful in search for refined RP-3 chemical surrogate fuel models directly on the basis of chemical reactivity data in oxidation at the molecular level.