Development of Soft Sensor with Time Difference of Process Variables Approach

Soft sensors are used to estimate the process variables that are hard to measure online
in a process unit but the predictive accuracy of the estimation will deteriorate due to
certain reasons. The reasons are usually due to the changes of plant state, catalyst
performance loss, sensor or process drift and scale deposition. In order to overcome
the degradation of the soft sensors due to process drift, time difference of process
variables is proposed to use for the predictive model. The objective of this paper is to
develop data-driven soft sensors with time difference of process variables and to
evaluate its advantages over traditional static soft sensors. The modeling technique
used for this approach is Partial Least Squares (PLS) method. Partial least squares
method is a numerical method based on multiple regression. The main purpose of
PLS is to predict a set of dependent variables from a set of independent variables or
predictors. In this paper, a binary distillation column is selected as a case study and
its virtual plant is built in Hysys enviromnent. In the simulation, the input variables
such as feed temperature, reflux flow rate, feed flow rate and steam flow rate are
varied and the output data are captured with time. In addition, different sets of data
were formed with various time differences in the variables. Those data are used to
develop the soft sensor model using PLS technique in SIMCA-P software. The
performance of the model is evaluated and compared with the conventional soft
sensor. Based on the results, the predictive ability of the developed model is higher
than the static conventional model.