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

Interactive visualization for NILM in large buildings using non-negative matrix factorization

[EN] Non-intrusive load monitoring (NILM) techniques have recently attracted much interest, since they allow to obtain latent patterns from power demand data in buildings, revealing useful information to the expert user. Unsupervised methods are specially attractive, since they do not require labeled datasets. Particularly, non-negative matrix factorization (NMF) methods decompose a single power demand measurement over a certain time period into a set of components or “parts” that are sparse, non-negative and sum up the original measured quantity. Such components reveal hidden temporal patterns which may be difficult to interpret in complex systems such as large buildings. We suggest to integrate the knowledge of the user into the analysis in order to recognize the real events inside the electric network behind the learnt patterns. In this paper, we integrate the available domain knowledge of the user by means of a visual analytics web application in which an expert user can interact in a fluid way with the NMF outcome through visual approaches such as barcharts, heatmaps or calendars. Our approach is tested with real electric power demand data from a hospital complex, showing how the interpretation of the decomposition is improved by means of interactive data cube visualizations, in which the user can insightfully relate the NMF components to characteristic demand patterns of the hospital such as those derived from human activity, as well as to inefficient behaviors of the largest systems in the hospital.SIMinisterio de Economía y fondos europeos FEDE

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

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

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

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

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

Depth Dependent Relationships between Temperature and Ocean Heterotrophic Prokaryotic Production

9 páginas, 2 figuras, 1 tabla.-- This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these termsCorrigendum: Depth Dependent Relationships between Temperature and Ocean Heterotrophic Prokaryotic Production, Frontiers in Marine Science 4: 91 (2017) https://doi.org/10.3389/fmars.2017.00091Marine prokaryotes play a key role in cycling of organic matter and nutrients in the ocean. Using a unique dataset (>14,500 samples), we applied a space-for-time substitution analysis to assess the temperature dependence of prokaryotic heterotrophic production (PHP) in epi- (0–200 m), meso- (201–1000 m) and bathypelagic waters (1001–4000 m) of the global ocean. Here, we show that the temperature dependence of PHP is fundamentally different between these major oceanic depth layers, with an estimated ecosystem-level activation energy (Ea) of 36 ± 7 kJ mol−1 for the epipelagic, 72 ± 15 kJ mol−1 for the mesopelagic and 274 ± 65 kJ mol−1 for the bathypelagic realm. We suggest that the increasing temperature dependence with depth is related to the parallel vertical gradient in the proportion of recalcitrant organic compounds. These Ea predict an increased PHP of about 5, 12, and 55% in the epi-, meso-, and bathypelagic ocean, respectively, in response to a water temperature increase by 1°C. Hence, there is indication that a major thus far underestimated feedback mechanism exists between future bathypelagic ocean warming and heterotrophic prokaryotic activityFinancial support for this project was provided by the Australian Institute of Marine Science (AIMS) and a grant from the Carlsberg Foundation to CL. XA, XM and JG were funded by the Malaspina expedition 2010 (grant n° CSD2008–00077) and HOTMIX (grant n° CTM2011–30010–C02–02) projects. TR was supported by the PADOM project (Austrian Science Fund grant n° P23221-B11). GH was funded by the Austrian Science Fund (FWF) project I486-B09 and by the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement No. 268595 (MEDEA project).Peer reviewe

Origen de la calidad del agua del acuífero colgado y su relación con los cambios de uso de suelo en el Valle de San Luis Potosí

"La historia de la ciudad San Luis Potosí se remonta al siglo XVI. Con el descubrimiento de yacimientos de oro y plata y la presencia de cuerpos de agua en el valle, fue fundada la ciudad San Luis Minas del Potosí, dando lugar a los dos primeros usos de suelo, urbano y minero. A partir del siglo XVII, el uso de suelo agrícola se desarrolló en huertos y fue relegado a la periferia de la zona urbana en el transcurso del tiempo. Finalmente el uso de suelo industrial surgió de manera importante en la segunda mitad del siglo XX. En la actualidad los tres usos de suelo existentes dentro del Valle de San Luis Potosí son el urbano, agrícola e industrial. A través de una campaña de muestreo hidrogeoquímico en octubre de 2008, con 44 muestras de norias y 3 de manantiales dentro del valle, se evaluaron parámetros físico-químicos, cationes, aniones y elementos traza. En los tres usos de suelo en la zona de estudio fueron detectados niveles importantes de nitratos, sulfatos, cloruros, conductividad eléctrica, coliformes totales y fecales; sin embargo, en la zona urbana existen anomalías puntuales de metales pesados principalmente de mercurio, bario, estroncio, cadmio, plomo, fósforo y plata, relacionadas a las antiguas actividades mineras y a la industria activa en la zona. Mientras que en la zona agrícola, la presencia de metales está asociada a los canales a cielo abierto que también reciben agua del Tanque Tenorio y éste a su vez de la zona industrial. En la zona industrial se detectaron grandes anomalías de tipo puntual en casi todos los metales pesados analizados; la principal fuente de estos contaminantes corresponden a un terreno industrial activo. Este trabajo está enfocado a evaluar el impacto que ha generado la actividad antropogénica sobre el acuífero colgado del Valle de San Luis Potosí desde inicios de la fundación de la ciudad hasta la actualidad, utilizando la calidad del agua como herramienta de análisis.""The history of San Luis Potosi City dates back to the sixteenth century. With the discovery of gold and silver deposits and the presence of water bodies in the valley, the city of San Luis Minas Potosí was founded, leading to the first two uses of land: urban and mining. From the seventeenth century, agricultural land developed in orchards and, over time, was relegated to the periphery of the urban area. Finally, industrial land use emerged significantly in the second half of the twentieth century. Currently the three existing land uses within the Valley of San Luis Potosi are urban, agricultural and industrial. Through a hydrogeochemical sampling campaign in October 2008 with 44 samples from wells and 3 from springs within the valley, we assessed physical and chemical parameters, cations, anions and trace elements. In the three land uses within the study area, we detected significant levels of nitrates, sulphates, chlorides, electrical conductivity, total and fecal coliforms; but in urban areas there are punctual anomalies of heavy metals, mainly mercury, barium, strontium, cadmium, lead, phosphorus and silver related to former mining and active industry in the area. However, in the agricultural zone, the presence of metals is associated with open channels, which also receive water from the Tanque Tenorio and this in turn from the industrial area. In the industrial area, puntual anomalies were detected in almost all heavy analyzed metals; the main source of these pollutants corresponds to an active industrial area. This work aims to evaluate the impact of anthropogenic activity in the perched aquifer of the Valley of San Luis Potosí since the city's foundation to the present, using water quality as an analytical tool.

Host-guest interactions between cyclodextrins and surfactants with functional groups at the end of the hydrophobic tail

The aim of this work was to investigate the influence of the incorporation of substituents at the end of the hydrophobic tail on the binding of cationic surfactants to α-, β-, and -cyclodextrins. The equilibrium binding constants of the 1:1 inclusion complexes formed follow the trend K1(α-CD)>K1(β-CD)>>K1(-CD), which can be explained by considering the influence of the CD cavity volume on the host-guest interactions. From the comparison of the K1 values obtained for dodecyltriethylammonium bromide, DTEAB, to those estimated for the surfactants with the substituents, it was found that the incorporation of a phenoxy group at the end of the hydrocarbon tail does not affect K1, and the inclusion of a naphthoxy group has some influence on the association process, slightly diminishing K1. This makes evident the importance of the contribution of hydrophobic interactions to the binding, the length of the hydrophobic chain being the key factor determining K1. However, the presence of the aromatic rings does influence the location of the host and the guest in the inclusion complexes. The observed NOE interactions between the aromatic protons and the CD protons indicate that the aromatic rings are partially inserted within the host cavity, with the cyclodextrin remaining close to the aromatic rings, which could be partially intercalated in the host cavity. To the authors´ knowledge this is the first study on the association of cyclodextrins with monomeric surfactants incorporating substituents at the end of the hydrophobic tai