生物燃油/柴油混合乳化燃料制取参数的优化研究

为方便快速的寻找生物燃油/柴油混合乳化燃料的最佳超声乳化制备参数,将超声乳化燃料制取的实验数据作为样本数据,构建乳化燃料稳定性的人工神经网络预测模型。利用建立的非线性神经网络预测模型作为种群个体的适应度函数,建立混合乳化燃料制备参数的遗传算法寻优模型,通过遗传算法模拟自然进化过程随机、自适应搜索的方式确定乳化燃料生产制取的最佳乳化超声参数(超声波频率、功率、作用时间和矩形脉冲占空比)。按该参数进行试验,其试验数值与计算数值相符合。结果表明,运用混合乳化燃料预测模型及遗传算法优化模型能够准确设计出稳定性能较好混合乳化燃料制取的超声乳化参数,为混合乳化燃料的制备提供了一种新的优选方式。

Parameter optimization for the stability of bio-oil / diesel emulsion fuel

In order to optimize bio-oil / diesel mixing ultrasound parameters for the emulsion fuel emulsification, an emulsion fuel artificial neural network prediction model was established using the neural network training sample data obtained from experiments. The best fuel ultrasonic emulsification parameters (ultrasonic frequency, power duration of action and incentive wave form) were identified using the genetic algorithm that simulated a natural evolutionary process using randomized adaptive search method. A test was conducted determine the consistence of the parameters between the experimental and calculated numerical data. The results showed that use of the mixed emulsion fuel prediction model and the genetic algorithm optimization model can accurately design ultrasonic emulsification parameters with a good mix stability for fuel preparation, which could provide a new preferred mode for preparing the mixed emulsion fuel.