CAJ | 학술논문

以混合二甲苯为原料, Mn(Ⅲ)为氧化剂,硫酸溶液为电解质,采用槽内式超声电合成甲基苯甲醛.探讨了选择性电合成甲基苯甲醛的可能性,通过径向基(RBF)神经网络和遗传算法(GA)对选择性电合成甲基苯甲醛3种异构体的比例、电流效率与混合二甲苯的用量、硫酸浓度和电流强度的关系建立预测模型,并运用 GA 确定模型中 RBF 神经网络的目标均方误差(Goal)和径向基函数的分布(Spread).然后根据预测模型,使用 GA 对电合成条件进行优化,分别获得了电合成产物中对位甲基苯甲醛占优、邻位和对位甲基苯甲醛占优以及电流效率最高时的电合成条件.当采用上述条件进行实验时,模型给出的预测结果分别为：对位甲基苯甲醛占优的质量分数可达90.01%,邻位和对位甲基苯甲醛占优的质量分数为80.38%,电流效率达到最高时的邻位、间位和对位甲基苯甲醛的质量分数分别为16.80%,8.43%和74.77%；而与之相对应的实际实验结果分别为90.10%和79.91%,以及17.20%,8.49%和74.31%,二者之间的最大相对误差小于±2.24%,表明所建立模型的预测值与实测值基本吻合.
이혼합이갑분위원료, Mn(Ⅲ)위양화제,류산용액위전해질,채용조내식초성전합성갑기분갑철.탐토료선택성전합성갑기분갑철적가능성,통과경향기(RBF)신경망락화유전산법(GA)대선택성전합성갑기분갑철3충이구체적비례、전류효솔여혼합이갑분적용량、류산농도화전류강도적관계건립예측모형,병운용 GA 학정모형중 RBF 신경망락적목표균방오차(Goal)화경향기함수적분포(Spread).연후근거예측모형,사용 GA 대전합성조건진행우화,분별획득료전합성산물중대위갑기분갑철점우、린위화대위갑기분갑철점우이급전류효솔최고시적전합성조건.당채용상술조건진행실험시,모형급출적예측결과분별위：대위갑기분갑철점우적질량분수가체90.01%,린위화대위갑기분갑철점우적질량분수위80.38%,전류효솔체도최고시적린위、간위화대위갑기분갑철적질량분수분별위16.80%,8.43%화74.77%；이여지상대응적실제실험결과분별위90.10%화79.91%,이급17.20%,8.49%화74.31%,이자지간적최대상대오차소우±2.24%,표명소건립모형적예측치여실측치기본문합.
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.