Gabriel Patron

I received an BASc in chemical engineering at the University of Toronto in 2017 and an MSc in process systems engineering at Imperial College London in 2018. My research aims to design algorithms for the real-time economic optimization of process systems under uncertainty.

Real-time optimization (RTO) and modifier adaptation (MA) are the approaches used in the cases of parametric and structural model uncertainty, respectively, for online steady-state economic optimization. The two-step RTO approach estimates and updates uncertain parameters prior to solving an economic optimization problem. In contrast, the MA approach uses bias and gradient-correction terms to modify structurally uncertain process models. For the two-step approach, we have  developed a scheme for accurate estimation in noisy environments. For the MA approach, we have developed a partial adaptation algorithm that has improved computational efficiency as to provide economic benefits to frequently-disturbed systems.

I am also broadly interested in sustainability and the use of economic incentives to operate sustainable processes efficiently. Through the use of online economic optimization, we have developed such schemes for post-combustion carbon capture and recirculating aquaculture systems.

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