Browsing by Author "Torres, Mario"

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    A Cyber-Physical System for Real-Time Physiological Data Monitoring and Analysis
    (2024) Huanca, Fernando; Torres, Mario; Rodriguez-Fernandez, Maria; Nunez, Felipe
    In the pursuit of a personalized healthcare experience, data-driven decision-making has become increasingly relevant. In this context, there is a growing need for technological systems specifically tailored to efficiently manage vast amounts of healthcare data. To address this need, in this work, we contribute by presenting a cyber-physical solution for monitoring and analyzing physiological data obtained from wearable devices. The proposed system is designed following a service-oriented architecture, which promotes modularity and enables efficient data access and analysis. The system is capable of ingesting and consolidating wearable data produced by a variety of commercial devices, scaling to accommodate a large number of data producers, and accepting queries from a multitude of consumers through various mechanisms. Performance tests conducted in various scenarios using real data demonstrate the system's effectiveness in maintaining real-time data access.
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    Automatic Synthesis of Containerized Industrial Cyber-Physical Systems: A Case Study
    (2023) Biskupovic, Angel; Torres, Mario; Nunez, Felipe
    Industrial cyber-physical systems (ICPSs) are widely regarded as the next generation industrial control systems and as one of the core technologies of the ongoing fourth industrial revolution. Despite its advantages, ICPSs are heavily dependent on the underlying physical process and their synthesis is a customized effort, demanding in terms of resources, which if not conducted carefully may impact the performance of the system. This work proposes a methodology to tackle ICPS synthesis in a systematic way, by using a set of industrial agents that take as input and standardized process description file and automatically deploy a modular ICPS from predesigned functional containers. Concrete examples on a tanks system and an industrial paste thickener are presented to illustrate the potential of the proposed methodology.
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    Early-Warning System for Supervision of Urban Water Services: Case Study of Coquimbo, Chile
    (2023) Aguirre, Paula; Bravo, Marilyn; Torres, Mario; Langarica, Saul; Oyarzun, Muriel; Nunez, Felipe
    The combination of rapid urbanization, population growth, and the hydric stress due to climate change effects demand innovative, optimized approaches to the operation and supervision of urban water services. In Chile, the Superintendency of Sanitary Services has underscored the need for automatized data integration and analysis tools that foster an evidence-based, preventive approach to the supervision of urban water systems. This motivates the development of a pilot supervision and early-warning system, conceived as a cybernetic entity whose objective is to enable efficient access, analysis, and predictive modeling of the data provided by water service companies, so as to identify risks and inefficiencies in water services, monitor their evolution, and anticipate possible failures. This article discusses the development and implementation of a prototype system that provides tools for visualization, statistical and temporal analysis of georeferenced data on water pressures, network disruptions, and client complaints and deploys machine learning capabilities for predicting the quality of service indicators at different locations. The initial implementation in one region of Chile has been shown to expedite the exploitation of data on urban water services, reduce time lags in the detection of service disruptions, and generate evidence for the planning and execution of supervision activities. Based on this successful pilot, a roadmap for geographical and technological expansion is formulated, including other technological, organizational, and regulatory gaps that must be addressed to establish a data-driven framework for the supervision of urban water services.
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    Neural Network-Based Model Predictive Control of a Paste Thickener Over an Industrial Internet Platform
    (2020) Núñez Retamal, Felipe Eduardo; Langarica Chavira, Saúl Alberto; Díaz Titelman, Pablo; Torres, Mario; Salas, Juan Carlos
    This article presents a real implementation of a neural network-based model predictive control scheme (NNMPC) to control an industrial paste thickener. The implementation is done over an Industrial Internet of Things (IIoT) platform designed using the seven layer reference model for IIoT systems. Modeling is achieved using an encoder-decoder with attention recurrent neural network, while MPC search is done using particle swarm optimization. An industrial evaluation is presented, which highlights the set-point tracking and disturbance rejection capabilities of the proposed NNMPC technique.
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    Real-Time Electrical Conductivity Monitoring and Correlation with Sulfate Release and Acid Mine Drainage Potential from a Gold/Silver Paste Tailing Storage
    (2021) Leiva, Eduardo; Cayazzo, Maria; Torres, Mario
    Safe disposal of tailings as high-density thickened tailings or paste tailings can reduce the environmental risks of conventional tailings deposits, reduce water use, minimize tailings storage facility footprints, reduce the potential for acid mine drainage (AMD), and minimize risks of failure, among other advantages. In the dewatering process, the addition of flocculants is key to improving the sedimentation of the tailings and the formation of a compact paste. Despite the environmental and operational advantages of using paste tailings, it is not clear how the chemical nature of coagulants and flocculants could influence the discharge of toxic elements (salts and metals) from tailings after storage. In this study, we show the results of the real-time evaluation of the release of polluting runoffs from a paste tailings deposit. To do this, we analyzed paste tailing samples for AMD potential through static and kinetic tests and monitored the electrical conductivity and real-time pH, evaluating the correlation with the sulfate in the thickener and downstream from the tailings deposit. Tailing samples have low sulfur content (<2%) and low acid-generating potential. Moreover, there is no evidence of a significant positive correlation (Pearson's coefficient r < 0.8) between the sulfate concentrations with the pH or EC. Thus, the chemical nature of the paste tailings prior to discharge has no direct impact on the release of sulfate-rich runoffs from the tailings after its storage. This indicates that the tailings paste at the evaluated site is chemically stable in the short term.