Classification and authentication of operating conditions in different processes using Partial Least Squares

Abstract Partial Least Squares (PLS) is a supervised multivariate statistical/machine learning technique, which is used for classification and identification/authentication of a variety of operating conditions in tomato juice concentrator/evaporator, yeast fermentation bioreactor and fluid catalytic cracking process plants. Data for the three processes were generated pertaining to different operating conditions (for each of them) including faulty ones by simulating their mechanistic models over 25 h. The simulated data at transient conditions were chosen for further processing. They were divided into training and testing data pools. After training, the developed PLS model could classify various process operating conditions 100 % accurately and identify unknown process operating conditions (simulated using training pool with certain degree of variations in them) pertaining to the processes.