A data‐driven approach for flow corrosion characteristic parameters prediction in an air cooler

Author:

Jin Haozhe1ORCID,Wu Xiangyao1ORCID,Liu Xiaofei1,Zhang Lin2,Gu Yong1,Quan Jianxun1

Affiliation:

1. Faculty of Mechanical Engineering & Automation Zhejiang Sci‐Tech University Hangzhou China

2. Equipment Engineering Department, Wuhan Petrochemical Co. Ltd. Sinopec Wuhan China

Abstract

AbstractA data‐driven soft measurement method based on a multiunit back propagation neural network (MBPNN) is presented in this study. This model aims to estimate the characteristic parameters that can reflect the flow corrosion of the reactor effluent air cooler (REAC). Flow corrosion failure during the hydrogenation process presents a serious concern to the petrochemical industry. In this paper, a safety evaluation of flow corrosion failure for a petrochemical diesel hydrogenation unit is first carried out. During the investigation, it is found that there is a risk of NH4Cl crystallization at around 187°C. Then, considering flow‐induced corrosion and ammonium salt deposition, main characteristic parameters are determined, including NH4Cl crystallization temperature (TC), air cooler tube bundle minimum and maximum flow rate (VminandVmax), air cooler inlet liquid water content (CW), and NH4HS concentration (CA). Finally, the data‐driven model based on a multiunit back propagation neural network (MBPNN) is constructed. An improved particle swarm optimization (PSO) approach is employed to initialize the main parameters of the model. Compared with a multioutput back propagation neural network (BPNN) model and MBPNN model without an optimization algorithm, the presented data‐driven model is proved to have high accuracy, a fast convergence rate, and high reliability.

Funder

National Natural Science Foundation of China

Publisher

Wiley

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.7亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2025 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3