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 versa-tility.However,the traditional twin model based on the BP neural network(BPNN)has lots of shortcom-ings,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 compo-nent 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 prob-lem.The relative error of CIWOA-BPNN is less than 0.6％,and the coefficient of determination is 0.997 75,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
Key words
reciprocating air compressor/efficiency/BP neural network/improved whale optimization al-gorithm
维普
万方数据