基于数字孪生技术的往复式空气压缩机效率预测方法研究  

Research on efficiency prediction method of reciprocating air compressor based on digital twin technology

作　　者：余建平[1] 胡爽 刘兴旺[1] 田有文 仇宏伟 AKOTO Emmanuel YU Jian-ping;HU Shuang;LIU Xing-wang;TIAN You-wen;QIU Hong-wei;AKOTO Emmauel(College of Petrochemical Engineering,Lanzhou Univ.of Tech.,Lanzhou 730050,China)

机构地区：[1]兰州理工大学石油化工学院,甘肃兰州730050

出　　处：《兰州理工大学学报》2024年第1期48-52,共5页Journal of Lanzhou University of Technology

基　　金：高端压缩机及系统技术全国重点实验室基金(SKL-YSJ202110)。

摘　　要：通过建立往复式空气压缩机数字孪生体模型,实现压缩机效率预测和参数寻优的方法,具有灵活、成本低、通用性好的优势.但是在多变量条件下,传统的基于BP神经网络孪生模型训练时间长、工作量大,寻优过程易陷入局部最优解,不易实现全局最优.针对传统孪生体模型存在的问题,提出了基于CIWOA-BPNN算法的孪生体模型构建方法,通过主成分分析法确定孪生体模型关键指标,在BPNN模型基础之上引入改进的鲸鱼优化算法.研究表明,基于CIWOA-BPNN算法的孪生体模型有效避免了BPNN模型陷入局部最优解.用CIWOA-BPNN算法预测压缩机效率相对误差小于0.6%,决定系数为0.99775,与传统模型相比大幅提升了预测精度.The method for compressor efficiency prediction and parameter optimization by establishing the reciprocating air compressor digital twin model has the advantage of flexibility,low cost,and good versatility.However,the traditional twin model based on the BP neural network(BPNN)has lots of shortcomings,such as longer training time to establish a module,easily falling into the local optimal solution,and difficulty in achieving the global optimal solution.To solve these problems,a novel digital twin model based on the CIWOA-BPNN algorithm is put forward to determine the key indexes by the principal component analysis method,in which a CIWOA algorithm is introduced to improve the BPNN’s performance.The results show that the new CIWOA-BPNN twin model effectively avoids falling the local optimal problem.The relative error of CIWOA-BPNN is less than 0.6%,and the coefficient of determination is 0.99775,which greatly improves the prediction accuracy compared with the traditional model.

关 键 词：往复式空气压缩机 效率 BP神经网络 改进的鲸鱼优化算法 

分 类 号：TH12[机械工程—机械设计及理论]