The new method not only lowered the requirements to initial values, but also insured the convergence stability in using it to predict the parameters of the rate constants of light cycle oil in FCC 10 lumping kinetic model. 该方法应用于催化裂化十集总动力学模型轻循环油(LCO)反应网络动力学参数估计,降低了搜索算法对初值和实验误差的要求,保证了收敛稳定性。
Firstly, we took light cycle oil ( LCO ) as a sample and discussed the separation characteristics of GC × GC and optimization of the conditions for separation of a sample with a wide distillation range. 首先以轻质循环油和系列正构烷烃为例,讨论了GC×GC的分离特性及用于宽馏程样品分离条件最佳化的方法。
Development of a genetic network mode for predicting the solidifying point of catalytic cracking light cycle oil 催化裂化轻柴油凝点遗传网络模型开发