Computer system and method for building and deploying predictive inferential models online to predict behavior of industrial processes

Abstract

Embodiments are directed to systems that build and deploy inferential models for generating predictions of a plant process. The systems select input variables and an output variable for the plant process. The systems load continuous measurements for the selected input variables. For the selected output variable, the systems load measurements of type: continuous from the subject plant process, intermittent from an online analyzer, or intermittent from lab data. If continuous or analyzer measurements are loaded, the systems build a FIR model with a subspace ID technique using continuous output measurements. From intermittent analyzer measurements, the systems generate continuous output measurements using interpolation. If lab data is loaded, the systems build a hybrid FIR model with subspace ID and PLS techniques, using continuous measurements of a reference variable correlated to the selected output variable. The systems deploy the built model to generate continuous key performance indicators for predicting the plant process.