改进天鹰优化器优化Elman神经网络模型预测管道蜡沉积速率

Prediction of Wax Deposition Rate in Pipeline by Optimizing Elman Neural Network Model with Improved Aquila Optimizer

管道结蜡一直是困扰含蜡原油输送的棘手问题,建立准确的蜡沉积速率预测模型对于保障管道的安全运行具有重要的实际意义。考虑到Elman神经网络(Elman neural network,ENN)模型的不足(易陷入极小点、泛化能力弱),基于蜡沉积速率的主要影响因素,提出了一种基于改进天鹰优化器(引入鲸鱼优化算法的狩猎策略对天鹰优化器的局部搜索能力进行改进)的ENN模型,并基于两组室内实验数据对比分析了所建新模型和其他模型预测精度的差异。结果表明,改进新模型的平均绝对百分比误差分别为0.7603%、1.2452%,其预测精度明显高于传统ENN模型;采用改进天鹰优化器建立的ENN模型可对初始权值和阈值进行寻优处理,极大提高了泛化能力,因此具有预测精度高的优点。

Pipeline wax deposition has always been a thorny problem perplexing the transportation of waxy crude oil.It is of great practical significance to establish an accurate wax deposition rate prediction model for ensuring the safe operation of the pipeline.Considering the shortcomings of Elman neural network(ENN)model(easy to fall into minimum points and weak generalization ability).Based on the main influencing factors of wax deposition rate,an ENN model based on the improved Aquila optimizer(the hunting strategy with whale optimization algorithm improves the local search ability of Aquila optimizer)was proposed.Based on two groups of indoor experimental data,the difference of prediction accuracy between the new model and other models was compared and analyzed.The results show that the mean absolute percentage error of the improved new model is 0.7603%and 1.2452%respectively,and its prediction accuracy is obviously higher than that of the traditional ENN model.The ENN model established by the improved Aquila optimizer can optimize the initial weights and thresholds,which greatly improves the generalization ability,so it has the advantage of high prediction accuracy.