Prediction of wax deposit thickness in waxy crude oil pipelines using improved GM(1,1) model

IF 2.5 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Frontiers in chemical engineering Pub Date : 2022-12-15 DOI:10.3389/fceng.2022.1024259
Shiqi Xu, C. Fan, Peijian Song, Chuanyou Liu
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Abstract

In this paper, the GM(1,1) model with function arccos x transformation and GM(1,1) model with function transformation are established by using arccosine function transformation method and a arccos x function transformation method, and the GM(1,1) model with function cos x 2 transformation is established by using function transformation theory, and GM(1,1) model with function cos x 2 + c transformation is established by using translational transformation theory on the basis of this model. The prediction accuracy of GM(1,1) model, GM(1,1) model with function arccos x transformation, GM(1,1) model with function a arccos x transformation, GM(1,1) model with function cos x 2 transformation, and GM(1,1) model with function cos x 2 + c transformation are compared by modeling with the field pipeline data and the indoor loop data. The influence of a value in GM(1,1) model with function a arccos x transformation on prediction accuracy is discussed, and the influence of c value in GM(1,1) model with function cos x 2 + c transformation on prediction accuracy is discussed. With the increase of a and c values, the average relative error show a trend of decreasing and then increasing, by comparing the average relative errors under different a and c values, the optimal a value and c value and the optimal prediction accuracy are obtained. The results show that the GM(1,1) model with function cos x 2 + c transformation in the indoor loop has an average relative error of 0.6490% when c = 0.114 , which is the minimum average relative error compared to other models and achieves the highest prediction accuracy. The GM(1,1) model with function cos x 2 + c transformation in the field pipeline has an average relative error of 1.94156% when c = − 0.555 , which is the minimum average relative error compared to other models and achieves the highest prediction accuracy. Among the five models, only the GM(1,1) model with function cos x 2 + c transformation has fitted and predicted values that are closer to the actual thickness values in the indoor loop experimental data and the field pipeline data, and the predicted values are more consistent with the actual conditions in the field pipeline. This paper verifies the feasibility of using the GM(1,1) model with function cos x 2 + c transformation to predict the wax deposition thickness of the pipe wall, and provides a reference for subsequent research on accurate prediction of wax deposition thickness.
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基于改进GM(1,1)模型的含蜡原油管道蜡层厚度预测
摘要GM(1, 1)模型和x arccos函数转换和GM(1, 1)模型和函数变换建立了利用余弦函数变换方法和arccos x函数转换方法,和GM(1, 1)模型函数cos x 2转换使用函数转换理论,建立了GM(1, 1)模型和函数因为x 2 + c变换建立了利用平移变换理论的基础上,这个模型。通过与现场管道数据和室内回路数据建模,比较了GM(1,1)模型、GM(1,1)函数arccos x变换模型、GM(1,1)函数arccos x变换模型、GM(1,1)函数cos x2变换模型、GM(1,1)函数cos x2 + c变换模型的预测精度。讨论了函数为arccosx变换的GM(1,1)模型中一个值对预测精度的影响,以及函数为cosx2 + c变换的GM(1,1)模型中c值对预测精度的影响。随着a、c值的增大,平均相对误差呈现先减小后增大的趋势,通过比较不同a、c值下的平均相对误差,得到最优a、c值和最优预测精度。结果表明,当c = 0.114时,室内回路中cos x 2 + c变换的GM(1,1)模型的平均相对误差为0.6490%,是其他模型中平均相对误差最小的模型,预测精度最高。在现场管道中进行cos x 2 + c变换的GM(1,1)模型在c =−0.555时的平均相对误差为1.94156%,与其他模型相比,平均相对误差最小,预测精度最高。在这5个模型中,只有函数cos x 2 + c变换的GM(1,1)模型的拟合预测值更接近室内环实验数据和现场管道数据的实际厚度值,预测值更符合现场管道的实际情况。验证了用函数cos x 2 + c变换的GM(1,1)模型预测管壁蜡沉积厚度的可行性,为后续准确预测蜡沉积厚度的研究提供参考。
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CiteScore
3.50
自引率
0.00%
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0
审稿时长
13 weeks
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