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.

通过电解氯化钠水溶液（或盐水）生产氯碱是世界上最大的工业规模电合成之一。拥有 1000 多个独立反应器的工厂很常见，其中 0.2 毫米的薄膜将氯和氢分开。氯碱制造过程必须受到完全控制，以避免任何可能导致爆炸、剧毒气体释放、非常昂贵的电池组件不可逆转的损坏以及巨大的维护成本和生产损失的错误操作。 R2 是加拿大在这方面的领先企业。在该领域，专家系统与众所周知的机器学习技术相结合，以监视和检测任何异常性能和操作条件。由于所获取数据的性质，该组合系统面临一些科学挑战。因此，该项目的目标是应对这些挑战，并根据机器学习领域的最新进展提出一些解决方案。在本研究中，我们试图研究这些挑战并提出解决方案，以便构建一个使用机器学习技术的警告和决策支持系统。该系统将检测和预测任何异常操作条件，并建议操作员必须采取的最佳可能措施。