Automatic fault and performance loss detection of industrial chlorine electrolysis reactors at R2

R2 工业氯电解反应器的自动故障和性能损失检测

基本信息

批准号：
515838-2017
负责人：
Yacout, Soumaya
金额：
$ 1.82万
依托单位：
École Polytechnique de Montréal
依托单位国家：
加拿大
项目类别：
Engage Grants Program
财政年份：
2017
资助国家：
加拿大
起止时间：
2017-01-01 至 2018-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=639420
关键词：
Automatic fault performance loss detection

项目摘要

The production of Chlor-Alkli by using electrolysis of aqueous solutions of sodium chloride (or brine) is one ofthe largest industrial scale electro-synthesis worldwide. Plants with more than 1000 individual reactors, inwhich 0.2 mm thin membranes separate chlorine and hydrogen, are common. The Chlor-Alkali manufacturingprocess must be fully controlled in order to avoid any wrong operation which can cause explosions, highlytoxic gas releases, irreversible damages of very expensive cell components and dramatic maintenance costs andproduction loss.At R2, which is a leading Canadian enterprise in this field, expert systems are combined to well-knownmachine learning techniques in order to monitor and to detect any abnormal performance and operatingconditions. This combined system faces some scientific challenges due to the nature of the acquired data. Thus,the objective of this project is to address these challenges and to propose some solutions that are based on thelatest advancement in the field of machine learning.In this research, we seek to study these challenges and to propose solutions, in order to construct a warning anddecision support system by using machine-learning techniques. This system will detect and predict anyabnormal operating conditions, and will advise the operator of the best possible action that must be taken.

通过使用电解的氯化钠水溶液（或盐水）的电解是全球最大的工业尺度电量溶液的产生。具有1000多个单个反应器的植物，这些植物在0.2 mm薄膜中分开的氯和氢很常见。为了避免任何可能引起爆炸，高度毒性的气体发行，非常昂贵的细胞组件的不可逆损害以及巨大的维护成本和生产损失的不可逆转的损害，R2是R2，这是该领域的领先企业，该系统的效果和良好的效果，可以指示任何良好的效果，以确保任何错误的效果，因此，甲基藻的制造业必须得到充分控制，以避免任何可能引起爆炸，高毒性气体发行，非常昂贵的损害损失，急剧的效果，以供应到良好的效果，并将其组合起来。操作条件。由于获得的数据的性质，该组合系统面临一些科学挑战。因此，该项目的目的是应对这些挑战，并提出一些基于机器学习领域最高进步的解决方案。在这项研究中，我们试图研究这些挑战并提出解决方案，以通过使用机器学习技术来构建警告和decission支持系统。该系统将检测并预测Anyabnormal的操作条件，并将建议操作员必须采取的最佳操作。