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Mathematical model of n-heptane pyrolysis under thermal cracking and steam cracking conditions was developed. The objective was achieving good generalization by combining two different data sources for model identification. The data sources included experimental pyrolysis data for many structurally different hydrocarbons measured at reference conditions as well as the data for n-heptane measured in different reactors at different reaction conditions. The model was developed using the automated reaction network generation. The generated network included hydrogen abstraction, β-scission, radical isomerization and recombination reactions, the kinetic parameters of which were expressed as functions of 26 group contribution factors. Molecular reactions and radical additions were substituted by 12 formal shortcut molecular reactions. The initial values of kinetic parameters and group contribution factors were adopted from previous study, where they were obtained by regression based on the experimental data involving wide range of linear, branched and/or cyclic hydrocarbons. More experimental data on n-heptane pyrolysis measured under different conditions, including the use of two different reactors, were added to the data set used for the parameter optimization. The model simulations showed good agreement with the experimental data even for the validation samples. Thus, the proposed approach proved the ability to produce models achieving good generalization. [ABSTRACT FROM AUTHOR]

Abstract: Pyrolysis experiments aimed at measuring product yields and conversion of various hydrocarbons were carried out in a micro-pyrolysis laboratory reactor. Standard pyrolysis conditions were used for all samples—810°C, 400kPa and 0.2–0.4s residence time in the reaction zone. Analysis of products was performed by on-line GC/FID/TCD. All pyrolysis products from hydrogen to pyrolysis oil were detected either individually or by fractions. Detailed compositions of the pyrolysis products of 56 hydrocarbons were obtained and are included completely in the article. The study involved hydrocarbons, which represent the wide structural variability, including linear, branched, cyclic, aromatic and unsaturated. Measured data were used to identify important relationships between structural parameters and product yields. Trends of important product yields, depending on the carbon number, molecule saturation, substitution, ring presence and substitution or a substituent size are presented. [Copyright &y& Elsevier]

Abstract: Processing of atmospheric gas oils (AGOs) in the petroleum industry by pyrolysis is an important task. In order to improve control of production and to predict the pyrolysis product yields, it is necessary to use novel approaches. The combination of selected analytical as well as modelling methods described in this contribution seems to be very promising and usable also for other types of pyrolysis feedstocks. Employment of an artificial neural network (ANN) model as the prediction tool is the crucial point. The main problem is the treatment of complex mixtures. While analytical methods used here give the picture of global characteristics and group composition, the method used to characterise the complex mixture by a well-defined substitute mixture of real components provides the input information for the ANN model. Unfortunately, the required measured data, typically distillation and other curves, are often incomplete and it is impossible to obtain directly the ‘phase portraits’ needed to establish the substitute mixture. Possibilities of how to solve this particular problem utilising other information about the mixture, e.g. bulk properties, are shown and applied to concrete AGO feedstocks with satisfactory results. [Copyright &y& Elsevier]