.png){kind=icon}
返回
Predicting wax deposition using robust machine learning techniques
.png){kind=icon}
OA
AI
DOI：10.1016/j.petlm.2021.07.005.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurate prediction of wax deposition is of vital interest in digitalized systems to avoid many issues that interrupt the flow assurance during production of hydrocarbon fluids. The present investigation aims at establishing rigorous intelligent schemes for predicting wax deposition under extensive production conditions. To do so, multilayer perceptron (MLP) optimized with Levenberg-Marquardt algorithm (MLPLMA) and Bayesian Regularization algorithm (MLP-BR) were taught using 88 experimental measurements. These latter were described by some independent variables, namely temperature (in K), specific gravity, and compositions of C1-C3, C4-C7, C8-C15, C16-C22, C23-C29 and C30 thorn . The obtained results showed that MLP-LMA achieved the best performance with an overall root mean square error of 0.2198 and a coefficient of determination (R2) of 0.9971. The performance comparison revealed that MLP-LMA outperforms the prior approaches in the literature. (c) 2021 Southwest Petroleum University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
Keyword:
Wax deposition
Multilayer perceptron
Levenberg-marquardt algorithm
Flow assurance
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
380
被引数:
1.9K
机构
暂无机构信息
引用论文
Integrative clustering of multi-level ‘omic data based on non-negative matrix factorization algorithm.png)
PLOS ONEOAAI
PLOS ONEIF0
OAAI分享收藏Determining the wax content of crude oils by using differential scanning calorimetry用差示扫描量热法测定原油的蜡含量THERMOCHIMICA ACTA
THERMOCHIMICA ACTAIF3.5
PREAI
分享收藏分享收藏
查看更多内容