Present and Future of Model Uncertainty Quantification in Process Systems Engineering

Abstract

This contribution investigates the impact of model uncertainty quantification techniques in different areas of process systems engineering (PSE), namely dynamic optimization, predictive maintenance, soft-sensor systems and risk assessment, using three case studies inspired by typical chemical and pharmaceutical engineering problems. Our analyses confirm that the systematic use of model uncertainty quantification in the solution of PSE problems may often increase the effectiveness of models and extend their application domain. Therefore, model uncertainty quantification is expected to become one of the backbones of process systems engineering in the near future.