Influence of normal and radial contributions of local current density on local electrochemical impedance spectroscopy.

A new tri-electrode probe is presented and applied to local electrochemical impedance spectroscopy (LEIS) measurements. As opposed to two-probe systems, the three-probe one allows measurement not only of normal, but also of radial contributions of local current densities to the local impedance values. The results concerning the cases of the blocking electrode and the electrode with faradaic reaction are discussed from the theoretical point of view for a disk electrode. Numerical simulations and experimental results are compared for the case of the ferri/ferrocyanide electrode reaction at the Pt working electrode disk. At the centre of the disk, the impedance taking into account both normal and radial contributions was in good agreement with the local impedance measured in terms of only the normal contribution. At the periphery of the electrode, the impedance taking into account both normal and radial contributions differed significantly from the local impedance measured in terms of only the normal contribution. The radial impedance results at the periphery of the electrode are in good agreement with the usual explanation that the associated larger current density is attributed to the geometry of the electrode, which exhibits a greater accessibility at the electrode edge

On the way to autonomous model predictive control: a distillation column simulation study

Model Predictive Control (MPC) is a powerful tool in the control of large scale chemical processes and has become the standard method for constrained multivariable control problems. Hence, the number of MPC applications is increasing steadily and it is being used in application domains other than petrochemical industries. A common observation by the industrial practitioners is that success of any MPC application requires not only efficient initial deployment but also maintenance of initial effectiveness. To this end, we propose a novel high level automated support strategy for MPC systems. Such a strategy consists of components such as performance monitoring, performance diagnosis, least costly closed loop experiment design, re-identification and autotuning. This work presents the novel technological developments in each component and demonstrates them on a distillation column case study. We show that automated support strategy restores nominal performance after a performance drop is detected and takes the right course of action depending on its cause

On the way to autonomous model predictive control:a distillation column simulation study

\u3cp\u3eModel Predictive Control (MPC) is a powerful tool in the control of large scale chemical processes and has become the standard method for constrained multivariable control problems. Hence, the number of MPC applications is increasing steadily and it is being used in application domains other than petrochemical industries. A common observation by the industrial practitioners is that success of any MPC application requires not only efficient initial deployment but also maintenance of initial effectiveness. To this end, we propose a novel high level automated support strategy for MPC systems. Such a strategy consists of components such as performance monitoring, performance diagnosis, least costly closed loop experiment design, re-identification and autotuning. This work presents the novel technological developments in each component and demonstrates them on a distillation column case study. We show that automated support strategy restores nominal performance after a performance drop is detected and takes the right course of action depending on its cause.\u3c/p\u3