The industry is moving in full speed towards ‘Industry 4.0’, where automized processes become even more efficient by using advanced technologies such as the Internet of Things and Advanced Analytics. Everybody wants to be part of this shift, but where do you start?

A large energy producer wanted to find out what value ‘Industry 4.0’ could add for one of their Gas Processing Plants. In order to determine this, we started with our Ignition package.

In two short and busy weeks, we determined that Assistive Analytics would improve the plant uptime the most. Beforehand, process engineers were manually operating a very complicated plant with many interacting subsystems. Using our analytical models, we identified correlations between different pressures or temperatures and the operational quality of the plant.

Feeding these findings back to the process engineers, we determined the desired outputs of our Ignition project. Subsequently, we extracted seasonal operating ranges and visualized important nonlinear correlations our model detected.

During the second stage of the project, we set up a local time series database. We connected our models and collaboratively designed the visualizations of the Analytics package to a dashboard, which is directly accessed by the process engineers.

During this collaborative process we identified that accessing this information proactively was key to add value. This allowed the process engineers to have enough time to intervene and control the operational quality. Initially this seemed very challenging as most predictive projects are for large plants. However, once we developed an online model, which continuously learned from data of the plant, we were able to develop an Advanced Analytics Model which accurately predicted the operational quality three hours into the future. Combining this prediction with the correlations of all subsystems gave the process engineers a valuable aid to drive their decisions!