A Genetic Algorithm–Based Method for the Optimization of Reduced Kinetics Mechanisms

Abstract

This paper describes an automatic method for the optimization of reaction rate constants of reduced reaction mechanisms. The optimization technique is based on a genetic algorithm that aims at finding new reaction rate coefficients that minimize the error introduced by the preceding reduction process. The error is defined by an objective function that covers regions of interest where the reduced mechanism may deviate from the original mechanism. The mechanism's performance is assessed for homogeneous reactor or laminar-flame simulations against the results obtained from a given reference-the original mechanism, another detailed mechanism, or experimental data, if available. The overall objective function directs the search towards more accurate reduced mechanisms that are valid for a given set of operating conditions. An optional feature to the objective function is a penalty term that permits to minimize the change to the reaction coefficients, keeping them as close as possible to the original value. This means that the penalty function can be used to constrain the reaction rates modifications during the optimization if needed. It is demonstrated that the penalty function is successful and can be combined with predefined uncertainty bounds for each reaction of the mechanism. In addition, the penalty function can be modified to achieve a further reduction of the mechanism. The algorithm is demonstrated for the optimization of a previously reduced variant of the GRI-Mech 3.0, a tert-butanol combustion mechanism by Sarathy et al. (Combust. Flame, 2012, 159, 2028-2055) and a hydrogen mechanism by Konnov (Combust. Flame, 2008, 152,

Kinetic insights into the lignocellulosic biomass-based levulinic acid production by a mechanistic model

“…The accurate estimation of kinetic parameters is an important issue in the development of mechanistic models. Kinetic parameters estimated from a wide range of operating conditions would allow to reduce the uncertainty of the models under operating conditions not yet established (Sikalo et al 2015). The work of Girisuta et al (2013) addressed the gaps of models previously reported in literature.…”

“…Das et al (2014) used GAs to gain a better mechanistic understanding of the catalytic reaction involved in dilute sulfuric acid pre-treatment of water hyacinth biomass. GA was also used by Sikalo et al (2015) for optimizing the reaction mechanisms of hydrogen, methane and tert-butanol. Punnathanam et al (2016) applied GA in the integration of a biorefinery consuming the effluent Black Liquor from a plant of paper and cellulose for producing electricity and steam.…”

Development of a Joint Hydrogen and Syngas Combustion Mechanism Based on an Optimization Approach

“…However, this technique cannot be applied to a syngas combustion mechanism, as the chemistry of syngas combustion does not contain analogous reactions. Mechanism optimization was also used as a correction step after mechanism reduction .…”

OptiSMOKE++: A toolbox for optimization of chemical kinetic mechanisms

“…The approach based on the use of response surfaces can be highly efficient for mechanism optimization, but, as mentioned by Sikalo et al [27], the nature of the objective function in mechanism optimization can be highly complex, since it consists of many local minima and maxima. Therefore, Sikalo et al [27] suggest to use the Genetic Algorithm (GA) global optimization approach, which has been proven to perform very well in these conditions [27][28][29]. Indeed, Elliott and co-workers have applied GAs for optimizing kinetic mechanism for many different fuels [29][30][31][32].…”