Sridharakumar Narasimhan

In this paper, we address the economic performance of Model Predictive Control (MPC) while operating at a backed-off operating point. Operating the plant at a constrained optimal point will often cause constraint violations due to uncertainties such as disturbances and measurement errors, etc. To ensure dynamic feasibility, the concept of economic back-off is used. In this work, we select the set point as the economic back-off point such that the dynamic operating region should have the least variability in the active constrained variables while ensuring the feasibility of other variables. In other words, the dynamic operating region is oriented by the proper design of a controller such that variability in active constrained variables is as low as possible. This controller design can be transformed into equivalent objective function weights of the MPC controller. In this study, we demonstrate that the determined back-off point is optimal for both linear controller and MPC controller when there are no unconstrained degrees of freedom. For the case with unconstrained degrees of freedom, the back-off point determined using the presented approach is optimal only for a linear controller but suboptimal for an MPC controller. Demonstrative case studies are presented to illustrate the economic performance of the MPC controller at the determined economic back-off point.

Graph Neural networks have shown the potential to solve large-scale combinatorial optimisation problems. The following work demonstrates how the graph neural networks can be utilised to solve the sensor placement problem, a combinatorial optimisation problem. This paper focuses on the problem of placing pressure sensors optimally in a Water Distribution Network (WDN). The problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) or Ising model, a combinatorial optimisation problem. The paper outlines the QUBO and Ising formulations for the sensor placement problem, starting from the network topology and other relevant features. A detailed procedure is presented for solving the problem by minimising its Hamiltonian using PyQUBO, an open-source Python Library. Finally, the proposed methods are applied to a real Water Distribution Network for evaluation.