Soft sensors driven by data are very common in industrial plants to perform indirect measurements
of difficult to measure critical variables by using other variables that are relatively easier to obtain.
The use of soft sensors implies some challenges, such as the colinearity of the predictor variables,
the time-varying and possible non-linear nature of the industrial process. To deal with the first
challenge, the partial least square (PLS) regression has been employed in many applications to model
the linear relations between process variables, with noisy and highly correlated data. However, the
PLS model needs to deal with the other two issues: the non-linear and time-varying characteristics
of the processes. In this work, a new knowledge-based methodology for a recursive non-linear PLS
algorithm (RNPLS) is systematized to deal with these issues. Here, the non-linear PLS algorithm is set
up by carrying out the PLS regression over the augmented input matrix, which includes knowledge
based non-linear transformations of some of the variables. This transformation depends on the system’s
nature, and takes into account the available knowledge about the process, which is provided by expert
knowledge or emulated using software tools. Then, the recursive exponential weighted PLS is used to
modify and adapt the model according to the process changes. This RNPLS algorithm has been tested
using two case studies according to the available knowledge, a real industrial evaporation station of
the sugar industry, where the expert knowledge about the process permits the formulation of the
relationships, and a simulated wastewater treatment plant, where the necessary knowledge about the
process is obtained by a software tool. The results show that the methodology involving knowledge
regarding the process is able to adjust the process changes, providing highly accurate predictions.
