This article presents a novel simplified method to implement the finite set - model predictive control technique for photovoltaic generation systems connected to the ac network. This method maintains the advantages of the conventional finite set - model predictive control, such as fast response, simple implementation, and easy understanding; but it also eliminates the use of a cost function and hence the weighting factors, instead, it finds the optimal operating state directly from the model and the discrete number of valid states of the converter. Although the proposed algorithm does not compute a cost function, it is able to select the inverter state that minimizes the tracking error by using a hexagonal convergence region. The main advantage of this technique is to reduce the computational cost in 43% of the algorithm that selects the best state, presenting a simple and complete algorithm without compromising the predictive control performance. The proposed algorithm properly operates under various conditions such as changes in the network frequency and changes in the system parameters.

The leaching process for copper extraction has garnered significant attention due to its critical role in meeting the rising demand for copper, driven by global trends towards decarbonization and electrification. The accurate measurement of variables is essential for process control, prompting the development of advanced sensor technologies. This paper reviews the applications of dielectric permittivity measurements in the mining industry, focusing on their potential to enhance the monitoring and optimization of copper leaching processes. It evaluates the suitability of permittivity-based sensors, analyzing their advantages and limitations, and discusses the implications for process control and economic optimization. The study highlights the integration of permittivity measurements into existing monitoring systems, aiming to improve efficiency, reduce environmental impact, and increase ore recovery rates. This comprehensive review provides insights into the current state of permittivity measurement technologies and their future prospects in the context of copper leaching.

The rapid dynamic responses of predictive control algorithms are widely acknowledged. However, achieving accurate steady-state reference tracking hinges not just on a precise mathematical model of the system but also on its parameters. This document presents a predictive control scheme augmented with integral state feedback tailored to a photovoltaic (PV) application. In scenarios with uncertain system parameters, steady-state errors can particularly impact reactive power regulation, where the absence of an integral term in the loop exacerbates this issue. The robustness and sensitivity of both predictive control and the proposed enhanced predictive controller are thoroughly examined. Simulation and experimental results are included to validate the effectiveness of this approach.