Search

Abstract: Reduced chemical kinetic models are often used in place of a detailed mechanism because of the computational expense of solving the complete set of equations describing the reacting system. Mathematical methods for model reduction are usually associated with a nominal set of reaction conditions for which the model is reduced. The important effects of variability in these nominal conditions are often ignored because there is no convenient way to deal with them. In this work, we introduce a method to identify rigorous valid ranges for reduced models; i.e., the reduced models are guaranteed to replicate the full model to within an error tolerance under all conditions in the identified valid range. Previous methods have estimated valid ranges using a limited set of variables (usually temperature and a few species compositions) and cannot guarantee that the reduced model is accurate at all points in the estimated range. The new method is demonstrated by identifying valid ranges for models reduced from the GRI-Mech 3.0 mechanism with 53 species and 325 reactions, and a truncated propane mechanism with 94 species and 505 reactions based on the comprehensive mechanism of Marinov et al. A library of reduced models is also generated for several prespecified ranges composing a desired state space. The use of these reduced models with error control in reacting flow simulations is demonstrated through an Adaptive Chemistry example. By using the reduced models in the simulation only when they are valid the Adaptive Chemistry solution matches the solution obtained using the detailed mechanism. [Copyright &y& Elsevier]

A new optimization-based approach to kinetic model reduction is presented. The reaction-elimination problem is formulated as a linear integer program which can be solved to guaranteed global optimality. This formulation ensures that the solution to the integer program is the smallest possible reduced model consistent with the user-set tolerances. The method is applied to generate optimally-reduced models for isobaric, adiabatic homogeneous combustion. The computational cost and accuracy of the reduced models are compared to those of the full mechanism. Results are shown for GRImech 3.0 and the Lawrence Livermore n-heptane combustion mechanism. The accuracy of the integer programming approach is compared to existing reaction elimination methods. The method is also applied to generate a library of reduced kinetic models for an adaptive chemistry simulation of a 2-D laminar, partially-premixed methane burner flame. Preliminary results are presented comparing the computational cost of the full GRImech 3.0 chemistry to that of the reduced model library. [Copyright &y& Elsevier]

Amyris is a fermentation product company that leverages synthetic biology and has been bringing novel fermentation products to the market since 2009. Driven by breakthroughs in genome editing, strain construction and testing, analytics, automation, data science, and process development, Amyris has commercialized nine separate fermentation products over the last decade. This has been accomplished by partnering with the teams at 17 different manufacturing sites around the world. This paper begins with the technology that drives Amyris, describes some key lessons learned from early scale-up experiences, and summarizes the technology transfer procedures and systems that have been built to enable moving more products to market faster. Finally, the breadth of the Amyris product portfolio continues to expand; thus the steps being taken to overcome current challenges (e.g. automated strain engineering can now outpace the rest of the product commercialization timeline) are described. [ABSTRACT FROM AUTHOR]