Todos convidados para o 10° SEMINÁRIO DE CONTROLE LADES-GIMSCOP

Palestrante: Dinesh Krishnamoorthy, PhD, NTNU

Link para a apresentação: meet.google.com/qnk-ijxt-nqs

Abstract (Resumo)
Typically, real-time optimization (RTO) involves solving a numerical optimization problem using rigorous nonlinear process models to compute the optimal setpoints. One of the challenges that impede practical implementation of traditional real-time optimization is the need to solve numerical optimization problems online. In order to circumvent this issue, there is an increasing interest in a class of methods for real-time optimization, known as “feedback-optimizing control” or “direct-input adaptation”. Here the objective is to indirectly move the optimization into the control layer, thereby converting the optimization problem into a feedback control problem. When converting the optimization problem into a feedback control problem, one of the most important question that arises is “What to control?” In this talk, we will explore some of these ideas in the context of RTO and present a generalized framework for selecting the controlled variables.

Short Bio (Minibiografia)
Dinesh Krishnamoorthy is a post-doctoral researcher at the Norwegian University of Science and Technology (NTNU). He has a Master’s degree in Control Systems (2012) from Imperial College London, and a PhD in Chemical Engineering (2019) from the Norwegian University of Science and Technology. In addition, he also has more than four years of industrial research experience from Equinor Research center, Norway (2012-2016). His research interest lies in the development of online process optimization under uncertainty, including new approaches to real-time optimization, machine learning based optimization approaches, and model predictive control.