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

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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.