Estimating cooling production and monitoring efficiency in chillers using a soft sensor

[EN] Intensive use of heating, ventilation and air conditioning systems in buildings entails monitoring their efficiency. Moreover, cooling systems are key facilities in large buildings and can account up to 44% of the energy consumption. Therefore, monitoring efficiency in chillers is crucial and, for that reason, a sensor to measure the cooling production is required. However, manufacturers rarely install it in the chiller due to its cost. In this paper, we propose a methodology to build a soft sensor that provides an estimation of cooling production and enables monitoring the chiller efficiency. The proposed soft sensor uses independent variables (internal states of the chiller and electric power) and can take advantage of current or past observations of those independent variables. Six methods (from linear approaches to deep learning ones) are proposed to develop the model for the soft sensor, capturing relevant features on the structure of data (involving time, thermodynamic and electric variables and the number of refrigeration circuits). Our approach has been tested on two different chillers (large water-cooled and smaller air-cooled chillers) installed at the Hospital of León. The methods to implement the soft sensor are assessed according to three metrics (MAE, MAPE and R²). In addition to the comparison of methods, the results also include the estimation of cooling production (and the comparison of the true and estimated values) and monitoring the COP indicator for a period of several days and for both chillers.SIMinisterio de Ciencia e InnovaciónEuropean Regional Development Fun

Virtual sensor for probabilistic estimation of the evaporation in cooling towers

16th AIAI (Artificial Intelligence Applications and Innovations) Joint International Conference[EN] Global natural resources are affected by several causes such as climate change effects or unsustainable management strategies. Indeed, the use of water has been intensified in urban buildings because of the proliferation of HVAC (Heating, Ventilating and Air Conditioning) systems, for instance cooling towers, where an abundant amount of water is lost during the evaporation process. The measurement of the evaporation is challenging, so a virtual sensor could be used to tackle it, allowing to monitor and manage the water consumption in different scenarios and helping to plan efficient operation strategies which reduce the use of fresh water. In this paper, a deep generative approach is proposed for developing a virtual sensor for probabilistic estimation of the evaporation in cooling towers, given the surrounding conditions. It is based on a conditioned generative adversarial network (cGAN), whose generator includes a recurrent layer (GRU) that models the temporal information by learning from previous states and a densely connected layer that models the fluctuations of the conditions. The proposed deep generative approach is not only able to yield the estimated evaporation value but it also produces a whole probability distribution, considering any operating scenario, so it is possible to know the confidence interval in which the estimation is likely found. This deep generative approach is assessed and compared with other probabilistic state-of-the-art methods according to several metrics (CRPS, MAPE and RMSE) and using real data from a cooling tower located at a hospital building. The results obtained show that, to the best of our knowledge, our proposal is a noteworthy method to develop a virtual sensor, taking as input the current and last samples, since it provides an accurate estimation of the evaporation with wide enough confidence intervals, contemplating potential fluctuations of the conditions.S

A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study

This article belongs to the Special Issue Energy Performance and Indoor Climate Analysis in Buildings)[EN] Large buildings cause more than 20% of the global energy consumption in advanced countries. In buildings such as hospitals, cooling loads represent an important percentage of the overall energy demand (up to 44%) due to the intensive use of heating, ventilation and air conditioning (HVAC) systems among other key factors, so their study should be considered. In this paper, we propose a data-driven analysis for improving the efficiency in multiple-chiller plants. Coefficient of performance (COP) is used as energy efficiency indicator. Data analysis, based on aggregation operations, filtering and data projection, allows us to obtain knowledge from chillers and the whole plant, in order to define and tune management rules. The plant manager software (PMS) that implements those rules establishes when a chiller should be staged up/down and which chiller should be started/stopped according different efficiency criteria. This approach has been applied on the chiller plant at the Hospital of León.SIThis research was funded by the Spanish Ministerio de Ciencia e Innovación and the European FEDER under project CICYT DPI2015-69891-C2-1-R/2-R

Design of Platforms for Experimentation in Industrial Cybersecurity

[EN] The connectivity advances in industrial control systems have also increased the possibility of cyberattacks in industry. Thus, security becomes crucial in critical infrastructures, whose services are considered essential in fields such as manufacturing, energy or public health. Although theoretical and formal approaches are often proposed to advance in the field of industrial cybersecurity, more experimental efforts in realistic scenarios are needed to understand the impact of incidents, assess security technologies or provide training. In this paper, an approach for cybersecurity experimentation is proposed for several industrial areas. Aiming at a high degree of flexibility, the Critical Infrastructure Cybersecurity Laboratory (CICLab) is designed to integrate both real physical equipment with computing and networking infrastructure. It provides a platform for performing security experiments in control systems of diverse sectors such as industry, energy and building management. They allow researchers to perform security experimentation in realistic environments using a wide variety of technologies that are common in these control systems, as well as in the protection or security analysis of industrial networks. Furthermore, educational developments can be made to meet the growing demand of security-related professionals.SIMinisterio de Economía y Competitividad Spain UNLE13-3E-157

Guidelines to develop demonstration models on industry 4.0 for engineering training

[EN] Industrial digitization is currently a great challenge which involves continuous advances in tech-nologies such as automation, robotics, internet of things, cloud computing, big data, virtual and augmented reality or cybersecurity. Only those companies able to adapt and with qualified work-ers will be competitive. Therefore, it is necessary to design new environments to train students and workers in these enabling technologies. In this paper, a set of guidelines is proposed to develop a demonstration model on Industry 4.0. Following these guidelines, an existing manufacturing industrial system, based on an electro-pneumatic cell for classifying pieces, is updated to the Industry 4.0 paradigm. The result is an Industry 4.0 demonstration model where enabling tech-nologies are added in an integrated way. In this manner, students do not only train in each technology, but also understand the interactions between them. In the academic year 2020/21, this demonstration model has been used by engineering students in a subject of a master’s degree. Impressions and comments from students about the structure and management of the environ-ment, as well as the influence on their learning process are collected and discussed.SIThis work was supported by the Spanish State Research Agency, MCIN/AEI/10.13039/501100011033 under Grant PID2020-117890RB-I00. The work of José Ramón Rodríguez- Ossorio was supported by a grant of the Research Programme of the Universidad de León 2020. The work of Guzmán González-Mateos was supported by a grant of the Research Programme of the University of León 202

A Deep Learning Approach for Fusing Sensor Data from Screw Compressors

[EN] Chillers are commonly used for thermal regulation to maintain indoor comfort in medium and large buildings. However, inefficiencies in this process produce significant losses, and optimization tasks are limited because of accessibility to the system. Data analysis techniques transform measurements coming from several sensors into useful information. Recent deep learning approaches have achieved excellent results in many applications. These techniques can be used for computing new data representations that provide comprehensive information from the device. This allows real-time monitoring, where information can be checked with current working operation to detect any type of anomaly in the process. In this work, a model based on a 1D convolutional neural network is proposed for fusing data in order to predict four different control stages of a screw compressor in a chiller. The evaluation of the method was performed using real data from a chiller in a hospital building. Results show a satisfactory performance and acceptable training time in comparison with other recent methods. In addition, the model is capable of predicting control states of other screw compressors different than the one used in the training. Furthermore, two failure cases are simulated, providing an early alarm detection when a continuous wrong classification is performed by the model.SIThis research was funded by the Spanish Ministry of Science and Innovation and the European Regional Development Fund under project DPI2015-69891-C2-1-R/2-R.Ministerio de Economía y Competitivida

Postoperative administration of ketorolac compared to other drugs for pain control after third molar surgery: A meta-analysis of double-blind, randomized, clinical trials

Aims: The aim of this study was to evaluate the analgesic effectiveness and adverse reactions of ketorolac in comparison with other drugs when administered postoperatively after third molar surgery. Methods: PubMed and Google Scholar were utilized to search for articles comparing the efficacy and safety of ketorolac and other analgesic agents after third molar surgery. Data from papers with a lower risk of bias were recorded. The overall evaluation of analgesia onset, general and subgroup evaluation of the number of patients requiring rescue analgesic medication, general and subgroup assessment of the study medication (satisfaction on the study drugs), and the overall estimation of adverse effects were performed using the Review Manager Software 5.3 to analyse the data and obtain the meta-analysis plot. Results: The subgroup evaluation of the study medication showed that patients who received ketorolac 30 mg were more satisfied than those who were given parecoxib 1 mg (odds ratio [OR] = 8.57, 95% confidence interval [CI] = 3.66–20.08, P = .00001), parecoxib 2 mg (OR = 7.17, 95% CI = 2.88–17.86, P = .0001), parecoxib 5 mg (OR = 3.03, 95% CI = 1.69–5.41, P = .0002), and parecoxib 10 mg (OR = 2.42, 95% CI = 1.36–4.32, P = .003). Moreover, patients who received ketorolac reported fewer adverse reactions compared with those who had received opioid analgesics (OR = 0.14, 95% CI = 0.32–1.76, P = .0001). Conclusions: The data from this study demonstrates that the postoperative administration of ketorolac 30 mg presents better results on patient satisfaction when compared to parecoxib 1 mg to 10 mg, and presents a similar satisfaction to parecoxib 20 mg following third molar removal

Armario para la formación en automatización y control de subestaciones eléctricas de tracción

Publicado por: Comité Español de Automática Universidad de La Rioja[ES] En este trabajo, se propone el diseño de un armario para la formación en automatización y control de subestaciones eléctricas de tracción mediante el estándar IEC 61850. Este armario incorpora diversos dispositivos electrónicos inteligentes, comunicados mediante protocolos como MMS y GOOSE, con el propósito de supervisar y controlar de forma local y remota las maniobras, así como el estado de las líneas de entrada y salida de la subestación. Los equipos son configurados para comunicarse en una red redundante, demostrando ser capaces de realizar las distintas maniobras en la subestación y asegurando en todo momento la alimentación a la catenaria, si se produce un fallo en cualquiera de las líneas. Además, se proponen un conjunto de tareas prácticas para la formación en el ámbito de la automatización y control de subestaciones de tracción, que los alumnos pueden realizar con el armario propuesto.SIMCIN/AEI/10.13039/501100011033/ y el proyecto UNLE15-EE-2943 financiado por MINECO

Local Tramadol Improves the Anesthetic Success in Patients with Symptomatic Irreversible Pulpitis: A Meta-Analysis

Symptomatic irreversible pulpitis is a painful clinical condition with a broad inflammatory component. Dental anesthesia in these patients is affected by the inflammatory process, reporting a high incidence of anesthesia failure. The aim of this systematic review and meta-analytical evaluation was to determine the effect of pre-treatment with tramadol in patients with symptomatic irreversible pulpitis, as well as for pain control and adverse effects. This study was registered in PROSPERO (ID: CRD42021279262). PubMed was consulted to identify clinical investigations comparing tramadol and placebo/local anesthetics in patients with symptomatic irreversible pulpitis. Data about the anesthesia, pain control, and adverse effects were extracted. Both the anesthetic success index and the adverse effects of local tramadol and placebo were compared with the Mantel–Haenszel test and odds ratio. Data analysis showed that the local administration of tramadol increased the anesthetic success rate when compared to placebo in patients with symptomatic irreversible pulpitis (n = 228; I2 = 0; OR = 2.2; 95% CIs: 1.30 to 3.79; p < 0.004). However, local administration of tramadol increased the risk of adverse effects when compared to placebo/local anesthetics (n = 288; I2 = 0; OR = 7.72; 95% CIs: 1.37 to 43.46; p < 0.02). In conclusion, this study shows that the local administration of tramadol increases the anesthetic success index when compared to placebo in patients with symptomatic irreversible pulpitis

Digital twin of an electro-pneumatic classification cell

[Resumen] Actualmente se está produciendo una transformaci´on digital en la industria gracias a la incorporaci´on de diversas tecnologías habilitadoras como la automatización, robótica, computación en la nube, ciberseguridad industrial, integración de sistemas o gemelos digitales, entre otras. Esta última está generando un gran interés por el valor añadido que supone incorporar simulaciones realistas y completamente operativas de los procesos. En este trabajo, se propone una metodología para desarrollar gemelos digitales, en los que se incorporan, además, otras tecnologías habilitadoras como la integración de sistemas o la computación en la nube. Adicionalmente, se presenta una aplicación desarrollada con Unity3D donde se emplea dicha metodología para obtener un gemelo digital de una célula electro-neumática robotizada.[Abstract] A digital transformation is currently taking place in industry thanks to the incorporation of various enabling technologies, such as automation, robotics, cloud computing, industrial cybersecurity, systems integration or digital twins, among others. The latter is generating great interest due to the added value of incorporating realistic and fully operational simulations of the processes. This paper proposes a methodology for developing digital twins, which also incorporates other enabling technologies such as systems integration or cloud computing. In addition, an application developed with Unity3D is presented where this methodology is used to obtain a digital twin of a robotic electro-pneumatic cell.Ministerio de Ciencia e Innovación; PID2020-117890RBI0