摘要
本文用锌粉还原N-亚硝基二苯胺的产物直接与2-甲基-4(N,N-二苄基)氨基苯甲醛缩合合成了空穴传输材料2-甲基-4 (N,N-二苄基)氨基苯甲醛-1,1-二苯腙(CT-191),采用均匀设计制定试验方案获取原始数据,应用BP人工神经网络对合成过程中工艺参数和一次产品收率的关系建立了模型,并用遗传算法进行优化得到最佳工艺条件:原料2-甲基-4-(N,N-二苄基) 氨基苯甲醛:N-亚硝基二苯胺约为1:2．5,还原时间为1 h,缩合时间为2 h,预测收率为96．28％。验证实验的结果为 95．98％,和预测值基本吻合。为化学生产工艺的优化探索了一条新途径。
The purpose of this paper is to optimize the synthesis of CT-191.2-methyl-4（ N ,N-dibenzyl）aminobenzaldehyde-1,1-diphenylhydrazone （CT-191） as a hole-transport material has been synthesized by the reaction between 2-methly-4（ N, N-dibenzyl） amino- benzaldehyde and the reduced product of N-nitrosodiphenylamine by zinc powder. A mathematical model of the relationship between the process parameters and the yield of CT-191 is established by using BP artificial neural networks and the optimum process parameters are optimized with genetic algorithm. The result of optimization is that the ratio bewteen 2-methly-4 （N, N-dibenzyl）aminobenzaldehyde and N-nitrosodiphenylamine is 0.4, the reduction time of N-nitrosodiphenylamine is 1 h, the time of condensation reaction is 2 h. The result of the verification test is 95.98% , it is corresponded to the predicted yield 96.28%. It provide a new means for chemical technology optimization.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2005年第11期998-1000,共3页
Computers and Applied Chemistry
基金
国家863计划资助项目(2002AA325050)
关键词
CT-191
人工神经网络
遗传算法
工艺优化
CT-191, artificial neural networks, genetic algorithm, technology optimization
