A Non-linear Autoregressive External Inputs (NARX) model for estimating the mixing volumes between batches in TRANSMIX

被引：0
|
作者：
Riverol, C. [1 ]
Harrilal, S. [1 ]
机构：
[1] Univ West Indies, Chem Engn Dept, St Augustine Campus, Trinidad, Trinidad Tobago
来源：
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER | 2018年 / 127卷
关键词：
Mixing volumes; TRANSMIX; Neural networks; NARX;
D O I：
10.1016/j.ijheatmasstransfer.2018.07.011
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
Use of a multiproduct pipeline operation inevitably produces an interface mixture or TRANSMIX which either has to be returned to refinery to be re-processed or downgraded into one of the adjacent batches, which is an additional cost as product is lost. In this paper, a Non-linear Autoregressive External Inputs (NARX) model is used for estimating the mixing volumes. Comparison with other models shows in the literature, the proposed neural network is reliable over a wide range of flow conditions and data. The model was fed using field data, 10 hidden layers and three subsets for the training were used: 50%, 65% and 85%. The NARX was capable of simulating the system and demonstrate a good performance comparable with the traditional methods and real data with a maximum error of 4% at 85% training data. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码：161 / 163
页数：3
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