Combining machine learning with domain knowledge and first principles for modeling in the process industries

Abstract

Computer-based process modeling and simulation methods and systems combine first principles models and machine learning models to benefit where either model is lacking. In one example, input values (measurements) are adjusted by first principles techniques. A machine learning model of the chemical process of interest is trained on the adjusted values. In another example, a machine learning model represents the residual (delta) between a first principles model prediction and empirical data. Residual machine learning models correct physical phenomena predictions in a first principles model of the chemical process. In another example, a first principles simulation model uses the process input data and predictions of the machine learning model to generate simulated results of the chemical process. The hybrid models enable a process engineer to troubleshoot the chemical process, enable debottlenecking the chemical process, enable optimizing performance of the chemical process at the subject industrial plant, and enable automated process control.