A new synthesis workflow converts finite-horizon minimum-time optimality into a cooling-demand VPC architecture and safety requirements into near-boundary tuning rules, matching nominal NMPC performance while showing zero temperature-limit violations under mismatch and faults where the NMPC benchmar
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OGCVL integrates symbolic and numerical techniques to learn effective nonlinear controlled variables for scalable self-optimizing control in chemical processes.
A vectorized reformulation of global self-optimizing control makes structural causality constraints linear for batch processes and enables a shortcut method that yields simple, repetitive combination matrices for near-optimal control, shown on a fed-batch reactor.
A desensitized optimal guidance algorithm using direct collocation and mesh remapping yields tighter trajectory envelopes and smaller terminal errors under parameter uncertainties compared to standard methods.
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