AI/ML for Process Monitoring

Machine Learning can help in process monitoring in various ways. In a typical process plant there are many process variable are to be monitored. Identifying the onset of deviation by monitoring each of these variables and parameters can very overwhelming and even infeasible at times. However all the variables are not equally responsible for system's behavior, so it can be really helpful if the total number of variables to be monitored to a few important ones.

There are ML techniques which can really help in determining the minimum set variables which largely captures the process behavior. One of such techniques is Principal Component Analysis (PCA). This reduces the number of variables those needed to be monitored and more over the entire monitoring process can be reduced to a couple of statistical parameters. So by monitoring just a very few statistical parameters, the onset of process deviation can be very easily identified along with the variables which are responsible for the deviation. This really makes the monitoring process really effective and robust.