Prediction and application of wax thickness on tube wall based on improved DGM (1,1) model

Changkun Cheng, Menglong Zhao, Hui Shen, Benquan Li, Yanping Liu, Pei Yang
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Abstract

In order to study the change of wax deposition thickness with time, an improved DGM (1,1) model was established based on the grey theory model. Taking indoor loop experiment and field pipeline data as examples, the weakening buffer operator and translation transform were introduced to improve the smoothness of the original sequence, and the different models were tested and compared. The results show that the average relative error of the traditional DGM (1,1) model is large, and the error of the improved model is greatly reduced after the weakening buffer operator treatment, and the d2 operator is more suitable for predicting the wax thickness of short sequence and wide spacing sequence. Translation transformation of the original sequence can improve the smoothness of the sequence, and different original sequences have selectivity on the offset of translation transformation function. The effect of weakening buffer operator on model improvement is much better than that of translational transformation. It is feasible to use the modified GM (1,1) model to predict the wax thickness of tube wall, and this method has certain popularization value.
基于改进的 DGM (1,1) 模型的管壁蜡厚度预测与应用
为了研究蜡沉积厚度随时间的变化,在灰色理论模型的基础上建立了改进的 DGM (1,1) 模型。以室内环路实验和现场管线数据为例,引入弱化缓冲算子和平移变换来提高原始序列的平滑度,并对不同模型进行了测试和比较。结果表明,传统 DGM (1,1) 模型的平均相对误差较大,而经过削弱缓冲算子处理后的改进模型误差大大减小,且 d2 算子更适合预测短序列和宽间距序列的蜡厚度。对原始序列进行平移变换可以提高序列的平滑度,不同的原始序列对平移变换函数的偏移量具有选择性。弱化缓冲算子对模型改进的效果远远好于平移变换。利用改进的 GM(1,1)模型预测管壁蜡厚度是可行的,该方法具有一定的推广价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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