摘要
针对苯氯化反应精馏过程中测量参数存在的高维度、非线性以及噪声干扰的问题,将遗传算法引入到加权邻域保持嵌入(WNPE)的参数选择中,提出了一种故障检测方法。该方法利用改进的遗传算法优选邻域个数和约减维数的参数,再利用WNPE对原始数据进行非线性降维,提取低维流形特征,建立监控统计模型进行故障检测。用Aspen Plus建立苯氯化反应精馏模型,并设置4种故障进行仿真研究,仿真结果表明,所提算法能够有效地检测出故障的发生,其检测精度明显优于PCA、KPCA方法。
Aiming at high dimension, nonlinearity and noise existing in benzene chlorination reactive distillation process, Genetic Algo- rithm （GA） was introduced into the parameter selection of Weighted Neighborhood Preserving Embedding （WNPE）, and fault detection method is proposed. In this approach, the numbers of neighborhoods and simplified dimensions were selected by the improved genetic algorithm. The WNPE is used to reduce the nonlinear dimension of original data and extract the low dimensional manifold features, and then the fault detection is carried out. Aspen Plus, a chemical process simulation software, was used to establish the Benzene Chlorina- tion reactive distillation model, and four kinds of fault were set up and simulated. The simulation results indicate that the proposed method can detect fault effectively and have a superior accuracy to PCA and KPCA method.
出处
《计算机与应用化学》
CAS
2016年第11期1153-1159,共7页
Computers and Applied Chemistry
基金
电子信息产业发展基金<石化
冶金行业生产控制软件研发及产业化-能源过程配置优化软件研发及产业化>
浙江省博士后科研择优资助项目(BSH1502058)
关键词
苯氯化反应精馏
故障检测
邻域保持嵌入
遗传算法
benzene chlorination reactive distillation
fault detection
neighborhood preserving embedding
genetic algorithm
