Semi-Infinite Optimization with Hybrid Models

Abstract

The robust design of performance/safety-critical process systems, from a model-based perspective, remains an existing challenge. Hybrid first-principles data-driven models offer the potential to dramatically improve model prediction accuracy, stepping closer to the digital twin concept. Within this context, worst-case engineering design feasibility and reliability problems give rise to a class of semi-infinite program (SIP) formulations with hybrid models as coupling equality constraints. Reduced-space deterministic global optimization methods are exploited to solve this class of SIPs to ϵ-global optimality in finitely many iterations. This approach is demonstrated on two challenging case studies: a nitrification reactor for a wastewater treatment system to address worst-case feasibility verification of dynamical systems and a three-phase separation system plagued by numerical domain violations to demonstrate how they can be overcome using a nonsmooth SIP formulation with hybrid models and a validity constraint incorporated.

Safety Critical Variable Analysis for Process Systems

“…15 Tian 16 proposed an integrated approach to synthesize process intensification systems with guaranteed flexibility and safety performances, which utilizes flexibility tests and risk assessment as safety constraints. Considering the engineering design feasibility and reliability problems, Wang 17 employed reduced-space deterministic global optimization methods to solve the semi-infinite optimization with hybrid models for safety verification.…”