个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:清华大学
学位:博士
所在单位:化工学院
学科:化学工程
办公地点:西部校区化工实验楼D203
电子邮箱:keleiz@dlut.edu.cn
基于Dragon描述符与改进的决策树-遗传算法的反应溶剂设计方法
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论文类型:期刊论文
发表时间:2022-06-27
发表刊物:化工学报
卷号:70
期号:2
页面范围:533-540
ISSN号:0438-1157
摘要:Reaction solvents have been widely used in liquid-liquid homogeneous organic synthesis. They have significant impacts on reaction rates and selectivity, which have contributed to the development of new process route for green synthesis. A computer-aided molecular design (CAMD) reaction solvent design method based on Dragon descriptor and SMILES (simplified molecular-input line-entry system) coding is proposed. First, a reaction kinetic model was constructed to make quantitative predictions for reaction rate constants k by the decision tree-genetic algorithm (DT-GA). Then, through SMILES code techniques and Dragon software, computer-aided molecular design (CAMD) method was integrated with the DT-GA to establish a mixed integer nonlinear programming (MINLP) model consists of objective functions and constraint equations. Afterwards, a decomposition-based algorithm was employed to solve this MINLP optimization problem, which achieves the objective of reaction solvent design. Finally, an example of Diels-Alder reaction was adapted to demonstrate the feasibility and effectiveness of this method. ? All Right Reserved.
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