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

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

Artículo publicado en abierto mediante APC Elsevier Open AccessNon-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 hospita

Innovative approach for the protection of recycled concrete by biogenic silica biodeposition

[EN] Over the past few years, the construction industry has sought to be more sustainable through use of more economically responsible materials and the use of environmentally friendly techniques such as bio-remediation. One promising area in this regard is that of surface treatments, particularly bio-repair techniques, to reduce the deterioration suffered by cement-based materials as a result of environmental conditions. This study presents original work on the use of silicaceous biodeposition by diatoms as a waterproofing surface treatment for recycled concrete. A recycled concrete mix containing a 50% substitution of recycled aggregates (RA) was used as a test substrate and the effectiveness of the bio-treatment was assessed using four different tests: capillary absorption, high-pressure water penetration, low-pressure water absorption and also characterised the biodeposited layer using SEM. Results demonstrate reductions of up to 33% in the capillary absorption test, while high-pressure water penetration decreased by 54.7%, compared to controls. In addition, Karsten tube tests showed low-pressure water absorption was delayed by up to 436 times relative to control samples. In combination these tests confirm the efficacy of diatom biodeposition as a protective surface treatment for cement-based construction materials.S

Microstructural, durability and colorimetric properties of concrete coated with a controlled application of graphene oxide

[EN]The present work analyzes how an aqueous suspension of graphene oxide (GO), as a surface treatment, can influence the properties of conventional concrete. The results show that the application of a GO surface coating on concrete improves its resistance to carbonation and chloride ion penetration, and also increases its electrical resistivity. In the best case, the GO coating can reduce carbonation by 40% and chloride ion diffusion by 75%. An increase of up to 75% in concrete resistivity was also achieved. The application of GO promotes the hydration process and densifies the microstructure of the concrete surface, and this is verified by scanning electron microscopy analysis. In addition, no color modification occurred after application of the treatment.SIPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

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

Surface protection of recycled concrete from different biogenic silica bio-deposition techniques: A sustainable approach

[EN] The increasing generation of construction and demolition waste poses an environmental challenge. In this study, the use of recycled concretes is proposed as a possible solution, reducing the extraction of natural resources and minimising the accumulation of waste in landfills. To ensure the durability and strength of this type of recycled concrete, two diatom culture techniques were developed in controlled environments to promote the bio-deposition of biogenic silica on the surface concrete. Through the resulting protective biofilm, diatoms decreased the capillary absorption and improved the impermeability of concrete to water and gases, such as CO2. Furthermore, these contributed to an increased mechanical strength of the concrete and a positive morphological modification of its surface by densifying and sealing surface pores. These results support the potential of diatoms as an effective solution to improve the properties and durability of recycled concreteSIThis work has been supported by the Junta de Castilla y León through the grants to finance the pre-doctoral hiring of research personnel, co-financed by the European Social Fund and resulting ORDEN EDU/875/2021 and ORDEN EDU/601/202

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