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

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

Environment for Education on Industry 4.0

[EN] A new industrial production model based on digitalization, system interconnection, virtualization and data exploitation, has emerged. Upgrade of production processes towards this Industry 4.0 model is one of the critical challenges for the industrial sector and, consequently, the training of students and professionals has to address these new demands. To carry out this task, it is essential to develop educational tools that allow students to interact with real equipment that implements, in an integrated way, new enabling technologies, such as connectivity with standard protocols, storage and data processing in the cloud, machine learning, digital twins and industrial cybersecurity measures. For that reason, in this work, we present an educational environment on Industry 4.0 that incorporates these technologies reproducing realistic industrial conditions. This environment includes cutting-edge industrial control system technologies, such as an industrial firewall and a virtual private network (VPN) to strengthen cybersecurity, an Industrial Internet of Things (IIoT) gateway to transfer process information to the cloud, where it can be stored and analyzed, and a digital twin that virtually reproduces the system. A set of hands-on tasks for an introductory automation course have been proposed, so that students acquire a practical understanding of the enabling technologies of Industry 4.0 and of its function in a real automation. This course has been taught in a master’s degree and students have assessed its usefulness by means of an anonymous survey. The results of the educational experience have been useful both from the students’ and faculty’s viewpoint.SIAgencia estatal de investigación MCIN/AEI/ 10.13039/501100011033Comité español de Automática y Siemens a través del premio ‘Automatización y Digitalización. Industria 4.0

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

Flow virtual sensor based on deep learning techniques

[ES] En el contexto de la digitalización de la industria, los sensores virtuales resultan muy útiles tanto para construir gemelos digitales, que permiten simular comportamientos que ayudan a optimizar el proceso productivo y prevenir errores, como para ser utilizados en las situaciones en las que un sensor real es muy costoso o directamente no puede ser instalado. En este artículo se propone un método para implementar sensores virtuales utilizando tres de las técnicas de deep learning más comunes: redes convolucionales, redes neuronales densas y redes recurrentes. El método se ha utilizado para construir un sensor virtual de caudal en una maqueta de control de procesos que dispone de instrumentación industrial real.[EN] In the context of industry digitalization, virtual sensors are very useful both to develop digital twins, which simulate behaviors that help us to optimize the process and prevent faults, such as to be used on the cases where a real sensor is very expensive or cannot be installed. In this paper, it is proposed a method to develop virtual sensors using three of the most common deep learning techniques: convolutional networks, dense neural networks and recurrent neural networks. The method has been used to develop a flow virtual sensor for a pilot plant that has real industrial instrumentation

Data Mining tool for Academic Data Exploitation : Literature review and first arquitecture proposal

This document aims to reflect the necessary aspects implied into the characterisation of a student profile: pedagogical characteristics, teaching learning attitudes, description of the different situations that may reflect problems regarding a normal progress. Also the characterisation the scenario that concerns the application of educational data mining techniques. The data that a student generates while progressing on his/her studies will be synthesised and related to potential profile features. As per the SPEET project concern, the definition of an IT architecture that is aimed at dealing with such student profile characterisation is also outlined

Data Mining Tool for Academic Data Exploitation : Publication Report on Engineering Students Profiles

This document aims to reflect the results obtained at SPEET project by means of the application of the deployed data mining tools. More specifically, the application of the student performance tools on the engineering degrees by the participating institutions. The analysis can be used twofold: Example on the use of the different performance analysis tools developed on the project and accessible at the project web tool. Example on the interpretation of the clustering and analysis tools in order to delimit a student profile. The provided analysis also extends the interpretations aiming at showing similarities country wise, so, finally we can conclude that the tools developed in this project can offer some significant information in detecting different profiles and the relationship between these profiles and categorical variables such as age, admission score, sex, previous studies

Data Mining Tool for Academic Data Exploitation : Graphical Data analysis and Visualization

This document aims to reflect the results obtained at SPEET project under the development of the data mining tools are presented. More specifically, two mechanisms have been developed: a clustering/classification scheme of students in terms of academic performance and a drop-out prediction system. The students' clustering and classification schemes are presented in detail. More specifically, a description of the considered machine learning algorithms can be found. Results show how groups of clusters can be automatically identified and how new students can be classified into existing groups with a high accuracy. Finally, the implemented drop-out prediction system is considered by presenting several algorithms alternatives. In this case, the evaluation of the dropout mechanism is focused on one institution, showing a prediction accuracy around 91 %. Algorithms presented at this document are available at repositories or inline code format, as accordingly indicated