(670f) Modeling the Pyrolysis of Methyldecenoate and Methyldecanoate

This work presents the application of a group additive method for the simulation of the pyrolysis of the biodiesel model components methyldecanoate and methyldecenoate.  The present work has focused on pyrolysis because of the limited attention on this aspect in current kinetic databases, in particular for combustion. The processes involved in pyrolysis are of key importance for the treatment of Soot/PAH formation, since the products of such reactions are their precursors. Moreover from an experimental point of view it is difficult to study oxidation at high temperatures without a thorough understanding of the competing pyrolysis reactions.[1] Therefore experiments with methyldecanoate and methyldecanoate are used to address the main challenges of modeling combustion and pyrolysis of biodiesel. These experiments have been performed on a bench scale pyrolysis set-up.[2-3] The analysis section of the pyrolysis set-up enables on-line qualification and quantification of the entire product stream, i.e. a wide boiling mixture containing H₂, CO, CO₂, alcohols (methanol, ethanol and heavier), aldehydes and ketones (formaldehyde, acetaldehyde, acetone, etc.), esters, and hydrocarbons ranging from methane to polyaromatic hydrocarbons (PAH). The enormous boiling range of the product constituents makes a complete and accurate analysis of pyrolysis reactor effluents a difficult task. Three different gas chromatographs are required: a refinery gas analyzer (RGA,17), a light oxygenates analyzer (LOA, 10) and the GC×GC-FID/TOF-MS (11) described above. The analytical equipment is positioned at different positions on the reactor effluent line. The GC×GC setup, has been discussed previously. [4] approximately 200 different components could be identified because of reduced peak overlap thanks to the increased separation power of the GC×GC.

The reaction network is generated automatically. Starting from an initial pool of species all reaction possibilities are identified. For every forward reaction introduced in the network the corresponding reverse reaction is also incorporated in the network. These reactions result in a number of formed radicals and molecules. The new radicals are added to the radical pool and the molecules are added to the molecule pool. In the next iteration the new species react with each other and with other species of the radical and molecule pool and the network is constructed gradually. Because almost no products with 12 or more carbon atoms are identified in the methyldecanoate pyrolysis experiments the maximum carbon number species is set to 11. The resulting reaction network consists over 4000 reactions between more than 1000 species.

A group additive method is applied that links the thermodynamics and kinetics for larger species to mostly high-accuracy ab initio data for smaller species. In previous work, consistent group additive models have been constructed for the prediction of thermochemistry and kinetics for the radical gas phase chemistry of hydrocarbons.[5-7] These models are based on the ab initio calculation of a consistent set of thermodynamic and kinetic data for the most important reaction families involved in radical hydrocarbon chemistry. The methodology is based on Benson group additivity and has proven its reliability for hydrocarbon thermochemistry and the kinetics of radical additions and hydrogen abstractions. In the current work this group additive method has been extended to include group additive values for the thermochemistry of  oxygenates relevant to our case study and a group additive method for new reaction families decarbonylation and decarboxylation. The group additive values for these last two reaction families have been determined based on literature data and a limited number of ab initio calculations.

The experimental data obtained on the bench scale set-up have been used for validating the automatically generated reaction network and other mechanisms published for methyldecanoate. Comparison between the experimentally determined product yields and reactor simulations over a wide range of process conditions show that a good agreement can be obtained between simulated and experimentally determined product yields for the major and minor components with our new reaction network. A sensitivity analysis and rate of production analysis allows to identify the dominant reaction pathways in the radical reaction network. Also commonly used assumptions such as the µ-hypothesis and the quasi steady state approximation (QSSA) for µ-radicals are verified.

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