Feasibility in Real-Time Optimization using Lipschitz bounds: Robust optimization Vs. Adaptive filtering

A.G. Marchetti

doi:10.1016/j.compchemeng.2025.109316

Feasibility in Real-Time Optimization using Lipschitz bounds: Robust optimization Vs. Adaptive filtering

2025-08-08

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A.G. Marchetti *

DOI：10.1016/j.compchemeng.2025.109316

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• Two RTO strategies compared using Lipschitz-based constraint bounding functions. • Strategy 1 embeds bounds in the RTO problem; Strategy 2 uses adaptive filtering. • Strategy 1 ensures feasibility and better convergence near constraint boundaries. • Constraint error bounds improve CA; gradient error bounds suit MA schemes. • Adaptive filtering often leads to premature convergence and sub-optimality.

Keywords:

RTO

Lipschitz bounds

adaptive filtering

constraint handling

convergence

Feasibility in Real-Time Optimization using Lipschitz bounds: Robust optimization Vs. Adaptive filtering

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