Deep Learning Technique Predicts Gas Quality During Chemical Production Process

We've already started to see effective use of emerging technologies, such as Industrial IoT (IIoT)-enabled remote condition monitoring and Big Data analytics for predictive maintenance and similar offline applications; but process engineers are interested in knowing if and how emerging technologies can be used to improve the actual production process and product quality. In a recent pilot program, Mitsui Chemicals, Inc. and NTT Communications Corporation (NTT Com), the industrial control technology (ICT) solutions and international communications business within NTT Group, have successfully created a Deep Learning technique that accurately predicts the quality of gas products during production; 20 minutes before the final product is created. As we learned in a recent press release from NTT Com, the technique is based on modeling the relationship between the different data sets sourced from raw materials feeding into the reactor; reactor conditions; and the trace gas impurities that represent gas product quality, expressed here as "X-gas." The goal of this joint project between NTT Com and Mitsui Chemicals is to improve the accuracy of detecting abnormalities in product quality to improve operational efficiencies and product quality. The two companies initiated the pilot project at one of Mitsui Chemicals' gas production plants in 2015.