生物燃油/柴油混合乳化燃料制取参数的优化研究
|
摘要
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。
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.
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.
引文
[1]曾其良,王述洋,徐凯宏.典型生物质快速热解工艺流程及其性能评价[J].森林工程,2008(3):47-50.
[2]吴娟,柏雪源.生物质油/柴油乳化燃料的研究进展[J].科技信息:科学教研,2007,24:325-326.
[3]韩旭东,黄勇成,易延洪,等.玉米秸秆生物油对直喷式柴油机燃烧与排放的影响[J].燃烧科学与技术,2012(3):248-255.
[4]齐国利,董芃,范夙博,等.生物质热解油及乳化油特性的研究[J].哈尔滨工业大学学报,2010,42(1):92-94.
[5]刘荣厚,黄彩霞,蔡均猛,等.生物质热裂解生物油精制的研究进展[J].农业工程学报,2008,24(3):308-312.
[6]孙书生,张健,李文志,等.生物油/柴油乳化燃料用于柴油机的试验研究[J].太阳能学报,2009,30(12):1718-1722.
[7]Chiaramonti D,Bonini M,Fratini E,et al.Development of emulsions from biomass pyrolysis liquid and diesel and their use in engines-Part 1:emulsion production[J].Biomass and Bioenergy,2003,25(1):85-89.
[8]Ikura M,Stanciulescu M,Hogan E.Emulsification of pyrolysis derived bio-oil in diesel fuel[J].Biomass and Bioenergy,2003,24(3):221-232.
[9]魏晓莉,王述洋,王卓.超声乳化生物质柴油稳定性研究[J].东北农业大学学报,2013,44(5):102-106.
[10]王丽红,吴娟,易维明,等.玉米秸秆粉热解生物油的分析及乳化[J].农业工程学报,2009,25(10):204-209.
[11]王黎明,王述详.生物燃油/柴油乳化燃油的理化特性研究[J].太阳能学报,2008,5(5):536-538.
[12]史峰,王小川,郁磊,等.MATLAB神经网络30个案例分析[M].北京:北京航空航天大学出版社,2010:9-10.
[2]吴娟,柏雪源.生物质油/柴油乳化燃料的研究进展[J].科技信息:科学教研,2007,24:325-326.
[3]韩旭东,黄勇成,易延洪,等.玉米秸秆生物油对直喷式柴油机燃烧与排放的影响[J].燃烧科学与技术,2012(3):248-255.
[4]齐国利,董芃,范夙博,等.生物质热解油及乳化油特性的研究[J].哈尔滨工业大学学报,2010,42(1):92-94.
[5]刘荣厚,黄彩霞,蔡均猛,等.生物质热裂解生物油精制的研究进展[J].农业工程学报,2008,24(3):308-312.
[6]孙书生,张健,李文志,等.生物油/柴油乳化燃料用于柴油机的试验研究[J].太阳能学报,2009,30(12):1718-1722.
[7]Chiaramonti D,Bonini M,Fratini E,et al.Development of emulsions from biomass pyrolysis liquid and diesel and their use in engines-Part 1:emulsion production[J].Biomass and Bioenergy,2003,25(1):85-89.
[8]Ikura M,Stanciulescu M,Hogan E.Emulsification of pyrolysis derived bio-oil in diesel fuel[J].Biomass and Bioenergy,2003,24(3):221-232.
[9]魏晓莉,王述洋,王卓.超声乳化生物质柴油稳定性研究[J].东北农业大学学报,2013,44(5):102-106.
[10]王丽红,吴娟,易维明,等.玉米秸秆粉热解生物油的分析及乳化[J].农业工程学报,2009,25(10):204-209.
[11]王黎明,王述详.生物燃油/柴油乳化燃油的理化特性研究[J].太阳能学报,2008,5(5):536-538.
[12]史峰,王小川,郁磊,等.MATLAB神经网络30个案例分析[M].北京:北京航空航天大学出版社,2010:9-10.