This study addresses the challenges of real-time spectroscopic sensing in industrial applications, where external factors such as temperature fluctuations, pressure variations, and particle size distribution significantly impact measurement accuracy. Conventional quantitative analytical methods often neglect these dynamic influences, leading to erroneous concentration estimates. To overcome these limitations, we propose an integrated modeling framework that combines a discrete-time process model with a physics-based spectroscopic sensor model, explicitly accounting for the dynamic properties of the system. A key innovation of this work is the development and application of an Adaptive Kalman Filter (AKF) to systematically correct for measurement distortions caused by external disturbances. Unlike conventional filtering techniques, the AKF dynamically adjusts to changing process conditions by leveraging real-time observability analysis, ensuring robustness even in the presence of sensor noise and environmental variability. Furthermore, to address cases where full observability is not achievable, we introduce a reduced-order Adaptive Kalman Filter (rAKF), which optimally estimates concentrations while minimizing computational complexity. A comprehensive series of simulations was conducted to assess the sensitivity of the estimation to variations in external signal type, noise levels, and initial values for parameters and states. The findings of this study demonstrate the superior performance of both AKF and rAKF in comparison to conventional filtering techniques, including the Extended Kalman Filter. The proposed approaches have been shown to enhance the reliability of spectroscopic sensor measurements, enabling more precise real-time estimations that can be used for monitoring and advanced process control strategies in industrial settings.

Many industrial processes can be approximated by low order plus dead-time models. In this work we propose a novel Generalized Predictive PI (GPPI) controller to achieve fast over-damped responses for systems with long dominant dead-times. The already established Predictive PI (PPI) control strategy has demonstrated to exceed the traditional PID controllers when they are applied to systems with long dominant dead-time, but the PPI structure limits the closed loop design options, thus also limiting the achievable performance. Here we analyze the design of the proposed GPPI controller versus the PPI and PI controllers to achieve fast over-damped responses. An expression for the achievable performance in terms of the Integral Absolute Error (IAE) is obtained for the GPPI controller and used to compare with the other controllers. Tuning rules are also proposed and simulation examples provided to offer some additional insight into the use of the GPPI controller.

Simulation has been used for decades in the mining industry to improve the design of haulage systems and optimize their operations. There are in the market many simulation environments for carrying out these tasks. Despite the existence of these advanced tools, there is a need of having more flexible environments that could be easily integrated with other tools such as optimization, advanced data analysis and real-time systems. In this work, the use of SimEventsTM is explored to build basic models of the main components of a haulage system. Using the unique abstraction mechanism of this environment, the main components are programmed as blocks that can be used to configure mining haulage systems. A hypothetical open-pit haulage system is simulated using Arena and the proposed simulation environment. Several simulation scenarios are used to compare the different features of these simulation tools and demonstrate the flexibility of the SimEventsTM models to address different operational scenarios including the integration of dispatching and maintenance operations. In addition, these models, unlike the Arena ones, have the potential to be seamlessly integrated with other MatlabTM toolboxes for optimization and analysis.

An interleaved DC/DC power cells-based topology is proposed in combination with a control method for photovoltaic plants. For this, two partial power and two interleaving methods are evaluated in terms of efficiency and performance. The selected topology consists of input-parallel-output-parallel interleaved DC/DC converters, which together with the control strategy, allows a high efficiency photovoltaic system, unlike conventional topologies. However, this type of topology is prone to present an unbalanced power distribution between the DC/DC power cells, mainly due to the natural differences between their components. This paper shows the application of a power balancing control strategy for interleaved partial power converters, which guarantees an equitable power distribution, therefore, it enables a correct sizing of the components. The simulation results show the correct operation of the power cells, in terms of overall high efficiency and power distribution among the power cells.

The copper mineral processing industry faces complex scenarios with increased demand, highly variable energy prices, falling ore grades that increase energy consumption, and increasing concern about the industry's carbon footprint. To reduce the risk imposed by these scenarios, the industry is looking at the use of renewable energy sources. Considering that the most important copper mines in Chile are situated in regions with high radiation levels, solar energy has the potential to be an attractive and sustainable source of energy. This paper provides an overview of the current solar technologies and how they have been applied to address some of the challenges faced by the copper mining industry today. It describes the use of solar thermal and solar photovoltaic technologies to produce power and heat for the copper mining processes. Indeed, solar photovoltaic technologies can be used to produce electricity for the comminution machines, electro-refineries and water pumping while solar thermal technologies are useful for electricity generation, heat production, thermal leaching and drying of copper concentrate. The review also analyzes, from a broader perspective, the potential of these technologies in the operation and design of new solar copper mineral processes. It is concluded that there several feasible options to integrate solar energy into the copper mining processes.

In this work, the problem of designing event-triggered control strategies for disturbance rejection and reference tracking for discrete-time linear systems is addressed. Based on the Lyapunov theory, LMI-based conditions for the guarantee of perfect reference tracking/disturbance rejection under the proposed event-trigger strategy are derived. Furthermore, to avoid that the ETC strategy degenerates to a periodic one (in the case of non constant signals), a practical tracking/rejection solution considering a trade-off between the reduction of the control updates and the tolerance to a small error in steady state is proposed. The conditions are then casted in LMI-based optimization problems to compute the triggering functions aiming at reducing the control updates while ensuring the perfect or the practical tracking.

Knowledge of atmospheric turbidity and solar energy resources is essential for optimum planning and operation of solar-powered processes, cleaning of solar devices, energy saving and global warming monitoring. To determine the atmospheric turbidity and solar irradiance, scientists commonly refer to three methods, which are based on ground and/or space instruments. The drawback of these methods is that they do not provide any information about the turbidity of the atmosphere and the intensity of solar irradiance when the instruments are not available. The present study provides an alternative method to quantify the atmospheric turbidity and solar irradiance from measured data of ambient temperature and relative humidity. To overcome the complexity associated with the existing techniques, simple expressions have been proposed to estimate Precipitable Water (PW), Linke's turbidity factor (TL) and aerosols transparency coefficient (k). High quality radiometric databases, recorded at PSDA station, located in the Atacama Desert, Chile, are used to validate the new method. The proposed PW's expression has shown a Mean Bias Error (MBE) of 4.19%, better than the one used in REST2v5 solar radiation code, which has a MBE of 8.06%. Besides, the new method introduced herein, estimates TL with a MBE of 1.31%, which corresponds to a Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 7.64% and 10.22% respectively. The method has successively predicted the Direct Normal Irradiance (DNI) with a high accuracy. Indeed, the average deviation (MBE) from the measured DNI is less than 1%, which corresponds to a MAE and RMSE of 3.29% and 5.21% respectively. The careful analysis of the results reveals strong influence of the atmospheric turbidity's uncertainty on the prediction uncertainty of DNI.

The present study evaluates the performance of thirty-eight (38) parametric solar radiation models under a large range of conditions. The models are used to predict the clear-sky DNI at a 1-min time resolution. High quality radiometric databases, recorded at PSDA station, located in the Atacama Desert, Chile, are used for this analysis. The models have been arranged into seven sets according to their crucial inputs to identify the impact of each parameter on models' performance. The results show that turbidity-independent models (sets A, B and C) are not suitable for the modeling of solar direct irradiance. Besides, turbidity-dependent models (sets D, E, F, and G) perform so much better than turbidity-independent models. However, few models perform well under all conditions. Among 11 AOD-dependent models, only six models are considered "good". The performance of the models of set F decreases with the increase of beta (aerosols' turbidity), while those belonging set G perform adequately even at high turbidity. Overall, the accuracy of most models is sensitive to solar elevation and atmospheric turbidity. Besides, some models are accurate only under limited conditions. As a result of this detailed investigation, six high-performance models can be recommended: ESRA, Ineichen, Yang, MLWT1, REST and MWLT2. (C) 2019 Published by Elsevier Ltd.

In recent years, PV generation has shown a continuous growth in the south American country of Chile. This mainly due to the high solar irradiation levels available and governmental backing of the technology. However, this growth is predominantly due to large-scale utility size power plants, being distributed generation on residential and commercial installations only a small fraction of it. This reduced growth on small scale PV systems is primarily attributed to the lack of qualified designers and installers, and the low awareness among the general public on the benefits of solar generation. To help tackle this problem, the development of an integrated e-learning platform is under way, which will provide differentiated courses for each objective group. This paper describes the course structure and its implementation using Moodle as the core of the learning managing system.

Soiling represents one of the most important factors influencing the operation of solar concentrator fields. The cleaning cost represents an important percentage of the total maintenance cost for this type of installations. The estimation of soiling rates can be used to improve significantly the performance of the system by devising cost effective cleaning schedules. In this work, two adaptive observers are proposed to estimate soiling rates using available information in solar collector fields. Simulation results illustrate the main characteristic of each algorithms and their good performance under different operating conditions. Comments on how these encouraging results can be extended to deal with more complex scenarios are also provided. Keywords— Observers, solar concentrators, hyperbolic systems.

One-dimensional models to simulate secondary settling tanks (SST) with cylindrical geometry are widely used in wastewater treatment. One of these models is the Bürger-Diehl model developed in [3], the respective extention to the varying crosssectional area case was recently made in [1]. For the modelling, simulation and control of SST according to this model it is necessary to know the hindered-settling velocity function, which is considered a constitutive functions of the model. The traditional way to get this function is to measure the velocity of the declining sludge blanket in a vessel with constant cross-sectional area through experiments obtained from laboratory batch tests, however this method gives only one point on the flux curve. A newly developed method in [2] shows that in a vessel with varying cross-sectional area a large part of the flux function can be estimated from a single batch test, where the largest interval can be obtained with conical vessels. The identification of the hindered-settling velocity function, a method of determination of the induction period for conical batch tests and simulations using the extended BürgerDiehl model to the varying cross-sectional area case are presented. This contribution is a joint work with Raimund Bürger (Universidad de Concepción, Chile), Stefan Diehl (Lund University, Sweden), Ryan Merckel (University of Pretoria, South Africa) and Jesús Zambrano (Mälardalen University, Sweden).

The generation of electrical and thermal energy with a single equipment provides many advantages in terms of the deployment of renewable energies for satisfying both electrical and thermal energy requirements. The maximization of the generated power for this type of equipment represents several challenges posed by its non-linear characteristics and the dynamics involved. In this work an optimizing strategy based on Lyapunov theory is proposed for solving this problem in a systematic way. The convergence of the strategy is analyzed in terms of Lyapunov functions and the dynamic characteristic of the thermal behavior. Simulation results considering a PV/T panel illustrate the effectiveness of the proposed approach as well as the requirements in terms of implementation issues. This approach can be refined to consider non-linear dynamics an uncertain description of the PV/T panel.

This paper addresses the problem of designing an event-triggered strategy for discrete-time systems with an observer-based controller. The considered system has the actuator and the sensor in different nodes separated by a network. The strategy consists in minimizing the use of the network by only transmitting data from the observer to the controller and from the controller to the actuator when a trigger is generated by an event-trigger function. Based on the Lyapunov theory, conditions for the stability of the closed-loop system in terms of linear matrix inequalities (LMIs) are derived. Convex optimization problems are provided to tune the proposed trigger functions aiming at reducing the usage of the network. Simulation examples illustrate the proposed method and compare the efficiency of the proposed optimization problems.

One possible technique to monitor sedimentation and froth flotation processes is electrical impedance tomography (EIT). When EIT is used in these applications, the measurements are usually conducted using a probe sensor that is positioned inside the process vessel. Full three-dimensional reconstruction is possible, however, it leads to time-consuming reconstructions. For simplifying the reconstruction procedure and reducing the computation time, two different reduced forward models are proposed. In the first model, the conductivity is considered to be rotationally symmetric, that is, the conductivity and the potential do not change in the angular direction. This model is accurate as far as the given assumptions are fulfilled. In the second model, the conductivity is considered to be invariant also in the radial direction. This one-dimensional (1D) forward model is highly approximative and therefore the reconstructed conductivity profile is useless with conventional reconstruction approach. The approximation error approach is applied to compensate the errors due to forward model reduction. The proposed approximative forward models are evaluated through computer simulations of phase interface detection showing feasible reconstructions and significant reduction in reconstruction time without compromising reconstruction accuracy.

Electrical Impedance Tomography sensors are non-intrusive sensors used to estimate conductivity fields. These estimates are based on a set of measured induced voltages generated by some currents injected to the process. Processing the measurements of an EIT sensor to estimate the underlying changes in the conductivity field requires the use of high dimensional models and solve a nonlinear optimization problem. In some applications the changes in the conductivity are due to changes in just a couple of factors, and therefore the sensor output can be described by a set of variables lying in a lower dimensional space. Manifold Learning Algorithms can learn these low dimensional spaces embedded in the measurement space. In this work, three popular MLA are analyzed as tools to discover manifolds in the EIT measuring space. Several simulations and experimental results show that Laplacian eigenmap algorithm is a suitable MLA for this type of applications.

This paper addresses the problem of tracking and rejection of periodic signals for linear multi-input, multi-output systems subject to control saturation. To ensure the periodic tracking/rejection, a modified state-space repetitive control structure is considered. Conditions in a “quasi” linear matrix inequality form are proposed to simultaneously compute a stabilizing state feedback gain and an anti-windup gain. Provided that the references and disturbances belong to a certain admissible set, these gains guarantee that the trajectories of the closed-loop system starting in a certain ellipsoidal set contract to the linearity region of the closed-loop system, where the presence of the repetitive controller ensures the periodic tracking/rejection.