A coordinated investigation of the combustion chemistry of diisopropyl ketone, a prototype for biofuels produced by endophytic fungi

详细信息    查看全文

• 作者：Joshua W. Allen ; Adam M. Scheer ; Connie W. Gao ; Shamel S. Merchant ; Subith S. Vasu ; Oliver Welz ; John D. Savee ; David L. Osborn ; Changyoul Lee ; Stijn Vranckx ; Zh ; ong Wang ; Fei Qi ; Ravi X. Fern ; es ; William H. Green ; Masood Z. Hadi ; Craig A. Taatjes
• 关键词：Diisopropyl ketone ; Automatic mechanism generation ; Ignition delay ; Pyrolysis ; Combustion ; Detailed kinetics modeling
• 刊名：Combustion and Flame
• 出版年：March, 2014
• 年：2014
• 卷：161
• 期：3
• 页码：711-724
• 全文大小：3420 K

文摘

Several classes of endophytic fungi have been recently identified that convert cellulosic biomass to a range of ketones and other oxygenated molecules, which are potentially viable as biofuels, but whose oxidation chemistry is not yet well understood. In this work, we present a predictive kinetics model describing the pyrolysis and oxidation of diisopropyl ketone (DIPK) that was generated automatically using the Reaction Mechanism Generator (RMG) software package. The model predictions are evaluated against three experiments that cover a range of temperatures, pressures, and oxygen concentrations: (1) Synchrotron photoionization mass spectrometry (PIMS) measurements of pyrolysis in the range 800-1340 K at 30 Torr and 760 Torr; (2) Synchrotron PIMS measurements of laser photolytic Cl-initiated oxidation from 550 K to 700 K at 8 Torr; and (3) Rapid-compression machine measurements of ignition delay between 591 K and 720 K near 10 bar. Improvements made to the model parameters, particularly in the areas of hydrogen abstraction from the initial DIPK molecule and low-temperature peroxy chemistry, are discussed. Our ability to automatically generate this model and systematically improve its parameters without fitting to the experimental results demonstrates the usefulness of the predictive chemical kinetics paradigm.