Flowsheet simulation, an important building block in chemical process development and design, generally requires the solution of a nonlinear system equations. Such simulation can fail if either no exists for chosen specifications or initial values solver are unfavorably. In this work, to overcome such convergence failures, AI-based surrogate models trained unit operations flowsheet then interco...

The objective of this study is to develop a new modified Bayesian Kriging (MBKG) surrogate modeling method that can be used to carry out confidence-based reliability-based design optimization (RBDO) for problems in which simulation analyses are inherently noisy and standard Kriging approaches fail. The formulation of the MBKG surrogate modeling method is presented, and the full conditional dist...

A typical approach in surrogate-based modeling is to assess the performance of alternative surrogate models and select the model that performs the best. In this paper, we extend the utility of an ensemble of surrogates to: i) identify regions of high uncertainties at locations where predictions of surrogates widely differ, and ii) provide a more robust approximation approach. We explore the pos...

Physics-informed neural networks (PINNs) were developed to overcome the limitations associated with acquisition of large training data sets that are commonly encountered when using purely data-driven machine learning methods. This paper proposes a PINN surrogate modeling methodology for steady-state integrated thermofluid systems based on mass, energy, and momentum balance equations, combined r...