高等学校化学学报Issue(6)：1199-1203,5.DOI:10.7503/cjcu20131043
基于径向基神经网络和遗传算法的槽内式选择性超声电合成甲基苯甲醛
In-cell Selective Ultrasonic Electrosynthesis of Methyl Benzaldehyde Based on RBF Neural Network and Genetic Algorithm
摘要
Abstract
Methyl benzaldehyde was synthesized via in-cell ultrasonic electrosynthesis with xylene mixture as raw material, Mn(Ⅲ) as oxidant and sulfuric acid as the electrolyte. The feasibility of the selective elec-trosynthesis of methyl benzaldehyde was discussed. The relation between experimental results ( i. e. three methyl benzaldehyde isomer ratio of selective synthesis, current efficiency) and experimental conditions(i. e. xylene mixture concentration, sulfuric acid concentration and the current strength) were explored using radial basis function ( RBF ) neural network and genetic algorithm ( GA ) in the electrosynthesis process, and moreover, the prediction model was established. The mean squared error goal(Goal) and the spread of radial basis functions values(Spread) of the RBF neural network in prediction model were optimized by GA. Then electrochemical synthesis conditions, whenever 4-methyl benzaldehyde dominated, 2-methyl benzaldehyde and 4-methyl benzaldehyde dominated, or the current efficiency reached highest, were optimized by GA according to prediction model. In accordance with these conditions, the prediction results of model were given as follow:first, the percent content of 4-methyl benzaldehyde dominated was 90. 01% ; second, the percent content of 2-methyl benzaldehyde and 4-methyl benzaldehyde dominated was 80. 38% ; third, the percentage of 2-methyl benzaldehyde, 3-methyl benzaldehyde and 4-methyl benzaldehyde were 16. 80% , 8. 43% and 74. 77% , re-spectively when the current efficiency reached the highest. The corresponding actual experiment results were 90. 10% , 79. 91% and 17. 20% , 8. 49% , 74. 31% , respectively. The maximum relative error between pre-diction results and experiment results was less than ±2. 24% . It showed that the model’ s prediction results were in agreement with experimental results.关键词
甲基苯甲醛/超声电合成/选择性电合成/人工神经网络/遗传算法Key words
Methyl benzaldehyde/Ultrasonic electrosynthesis/Selective electrosynthesis/Artificialneural net-work/Genetic algorithm分类
化学化工引用本文复制引用
张汇,何玉韩,唐铎,李彦威..基于径向基神经网络和遗传算法的槽内式选择性超声电合成甲基苯甲醛[J].高等学校化学学报,2014,(6):1199-1203,5.基金项目
山西省自然科学基金(批准号:2011011010-1,2004-1022)资助@@@@Supported by the Natural Science Foundation of Shanxi Province, China(Nos.2011011010-1,2004-1022) （批准号:2011011010-1,2004-1022）
