A major challenge in chemical process design is to make design decisions based on partly incomplete or imperfect design input data. Still, process engineers are expected to design safe, dependable and cost-efficient processes under these conditions. The complexity of typical process models limits intuitive engineering estimates to judge the impact of uncertain parameters on the proposed design. In this work, an approach to quantify the effect of uncertainty on a process design in order to enhance comparisons among different designs is presented. To facilitate automation, a novel relaxation-based heuristic to differentiate between numerical and physical infeasibility when simulations do not converge is introduced. It is shown how this methodology yields more details about limitations of a studied process design.
Chemical process design; Process modeling; Simulation and optimization; Uncertainty; Process performance measures; Convergence; Process Simulators
