Testing and applying machine learning techniques in monitoring and detecting operating modes and faults of a membrane cell electrolyzer online and in real time at R2
The production of Chlor-Alkli by using electrolysis of aqueous solutions of sodium chloride (or brine) is one of the largest industrial scale electro-synthesis worldwide. Plants with more than 1000 individual reactors, in which 0.2 mm thin membranes separate chlorine and hydrogen, are common. This process is quite sensitive and any wrong operating conditions can cause irreversible damages. The most common accident associated with this industry are fire, explosion and toxic gas releases that can cause fatalities and long term health impact on the exposed population. The objective of this research is to control the operating conditions by the automatic monitoring and analysis of the relevant data. With the advancement in the sensors? technology and data analytics, data that is related to the process performance is acquired on-line and in real time. This data is a source of valuable information that indicates the process? states.