A Novel attempt to reduce engineering effort in modeling non-linear chemical systems for Operator Training Simulators

Abstract

Operator Training Simulator (OTS) applications have become the norm of the industry in training operators to achieve efficient process operations. First principles based modeling approach in OTS packages achieves realistic simulations of chemical processes. However modeling the kinetics and thermodynamics accurately require considerable engineering efforts and may involve experimental studies to match the plant behavior. Hybrid models also known as grey-box models replace the unknown/complex equations in first principles models with empirical relationship using functional approximators such as neural networks, polynomials, etc. In this work we explore the use of Kernel Principal Component Analysis (K-PCA) as an approximation technique for certain nonlinear thermodynamics or kinetic functions parameterized using available plant archived data. Simulation results on a complex binary distillation column demonstrate the applicability of the proposed novel approach.