A surrogate shale oil model based on a multi-objective fusion adaptive optimization considering its pyrolysis characteristics

Abstract

A surrogate shale oil model was proposed based on model compounds with optimal proportions to reduce the complexity of Huadian shale oil to analyze its pyrolysis kinetics. There are mainly n-alkanes and n-olefins with C8–C36 in shale oil according to GC-MS results, a small amount of aromatics and alcohols, as well as trace of esters, ketones and nitriles, etc. Pyrolysis behavior of shale oil was investigated by Py-GC-MS and FTIR, including the evolution of pyrolytic products and functional groups. Here, n-eicosane, 1-octadecene, pentadecanol and nonylbenzene were selected as surrogates based on GC-MS result. To obtain the optimal surrogate model, a novel multi-objective fusion adaptive optimization function with constraints was proposed to determine the optimal weighting factor of each component from the pareto optimal set. Then, the detailed pyrolysis kinetic mechanisms containing 745 species and 19392 reactions were developed to study its thermochemical conversion. Moreover, the skeletal mechanism reduced by decoupling and directed relation graph (DRG) was used to describe the thermal flow reactivity of these four components. In sum, this work provides an integrated description of shale oil pyrolysis, and gives a new method for studying the pyrolysis of oil shale intrinsically.