Grup de Recerca en Energia i Intel·ligència Artificial (GREiA) (INSPIRES)

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The GREiA research group (Research group in energy and artificial intelligence) is born from the union of the research group in energy GREA and the research group in artificial intelligence IA. The collaboration of these two groups begins in 2014. The general line of research that defines the activity of the group is to provide answers and solutions related to the fields of energy engineering, industrial and construction design, sustainability and intelligence artificial. [Més informació]


Recent Submissions

Now showing 1 - 5 of 505
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    Optimizing the discharge process of a seasonal sorption storage system by means of design and control approach
    (Elsevier, 2023) Crespo, Alicia; Frazzica, Andrea; Fernàndez Camon, César; Gracia Cuesta, Alvaro de
    Sorption thermal energy storage systems have higher energy densities and low long-term thermal losses compared to traditional energy storage technologies, which makes them very attractive for seasonal heat storage application. Although they have a lot of potential at material level, its operation and system implementation for residential application requires further study. The performance of a seasonal sorption thermal energy storage system strongly depends on the discharging process during the cold season. The present study analysed through numerical simulations different scenarios to enhance the thermal performance of a solar-driven seasonal water-based sorption storage, which supplied space heating and domestic hot water to a single-family house in a cold climate region. All studied scenarios were analysed under optimal control policy. The results indicated that the sorption storage could increase by 9 % its energy density if conservative and constant discharging temperature set points are considered, due to fewer interruptions during the discharge. The energy density of the sorption storage driven by solar energy was highly impacted by the weather conditions, and by the type and availability of low-temperature heat source. Indeed, the energy density of the sorption storage increased by 22 % using a water tank to assist the evaporator of the sorption storage, instead of a latent storage tank. The use of a dry-heater to assist the evaporator with environmental heat was not suitable for the climate studied due to the low hours of operation. The sorption storage system composed of 20 modules of LiCl-silica gel could obtain an energy density and a COP of 139 kWh/m3 and 0.39, respectively, if a constant low-temperature heat source (i.e, geothermal or waste energy) was available.
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    Open Access
    Optimal control of a solar-driven seasonal sorption storage system through deep reinforcement learning
    (Elsevier, 2023) Crespo, Alicia; Gibert Llauradó, Daniel; Gracia Cuesta, Alvaro de; Fernàndez Camon, César
    Deep reinforcement learning (DRL) has demonstrated its effectiveness in the control of energy systems, although it has not yet been applied to sorption thermal energy storage (TES) systems. The operation of sorption TES systems is notably more complicated compared to other TES variants. The discharge of a sorption TES occurs at a particular desorption and evaporation temperature. Achieving a continuous and efficient discharge of a sorption TES is a challenging control task if heat required at the evaporator is obtained from the sun or the environment. Its operation is especially complicated during winter, because of the limited availability of solar irradiation and low ambient temperatures. Thus, this study analyzes for first time in the literature the competitiveness of deep reinforcement learning to control a solar-driven seasonal sorption TES system and compares it against traditional optimized rule-based control strategy. The system, located in Central Europe, supplied domestic hot water and space heating to a single-family house. Two DRL models were developed and trained to operate the system under two different sets of data: 120 winter consecutive days and 60 winter non-consecutive days. The results showed that the DRL control strategy reduced the system operational costs by 28% in a 60 winter days scenario. For a 120 winter days scenario, the operational cost savings decreased to 13% because the smart control performed worst once the sorption TES was fully discharged. These results were derived from a four-year validation data set, bolstering their robustness. The study demonstrates the successful application of DRL in controlling a solar-driven seasonal sorption TES system, yielding considerable economic savings compared to an RBC strategy. Subsequent work will consist of implementing the smart control strategy at prototype level to assess its performance.
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    Open Access
    Experimental evaluation of different macro-encapsulation designs for PCM storages for cooling applications
    (Elsevier, 2023) Rehman, Omais Abdur; Palomba, Valeria; Vérez, David; Borri, Emiliano; Frazzica, Andrea; Brancato, Vincenza; Botargues, Teresa; Ure, Zafer; Cabeza, Luisa F.
    Extensive research has been conducted on utilizing phase change materials for cooling applications, making it one of the most explored techniques in this domain. This research paper presents a comprehensive performance evaluation of a latent heat thermal energy storage unit featuring three distinct macro-encapsulation designs for phase change materials. The study aims to assess the thermal performance, efficiency, and practical applicability of these macro-encapsulation designs in a storage system. The PCM macro-encapsulation designs under investigation include cylindrical and rectangular shapes, each possessing different geometry. Two different configurations have been considered in this study. One configuration contains same PCM mass in order to have similar storage capacity while the other configuration has maximum PCM mass that can be inserted inside the tank. The used phase change material is a salt hydrate with melting temperature of 17 °C. The experimental setup consists of a controlled test rig that simulates real-world conditions and enables the comparative analysis of the three designs. Key performance parameters such as the charging and discharging time, temperature profiles, heat transfer rate, and energy storage/retrieval rates are measured and analysed. The results obtained from the experimental study provide valuable insights into the thermal behaviour, energy storage capacity, and overall effectiveness of the three macro-encapsulation designs. It is important to mention that use of an encapsulation design is highly dependent on application. The findings of this study contribute to the understanding of the impact of different macro-encapsulation designs on performance of thermal energy storage units. The results serve as a basis for optimizing macro-encapsulation designs, improving the efficiency and reliability of latent heat storage systems, and promoting their wider adoption in various energy management applications.
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    Open Access
    Analysis of a heat pump-based energy system exploiting a low GWP refrigerant in different European climates
    (EDP Sciences, 2023-08-25) Rehman, Omais Abdur; Palomba, Valeria; Frazzica, Andrea; Cabeza, Luisa F.
    The objective of this research is to assess the operation of a heat pump (HP) under varying climatic conditions in Europe. To achieve this, a Dymola model is developed for a solar-assisted reversible water-to-water HP that utilizes a low global warming potential (GWP) refrigerant, R1234ze(E), and includes thermal and electrical energy storage systems. Experimental data is used to validate the primary components of the model. Simulations are conducted for both summer and winter seasons to determine the system’s overall annual performance. The analysis covers energy exchange between the system and the grid and utilizes key performance indicators such as self-sufficiency and self-consumption index. Furthermore, a techno-economic analysis is conducted to determine the payback period of the heating and cooling energy system based on the components’ capital expenditure and available incentives.
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    Open Access
    Lactic acid production from cow manure: Technoeconomic evaluation and sensitivity analysis
    (MDPI, 2023) Garrido, Ricard; Cabeza, Luisa F.; Falguera Pascual, Víctor
    Recently, the industrial focus has shifted to renewable raw materials due to the exhaustion and rising pressures about environmental and political issues. Lignocellulosic biowaste can be derived from a range of sources, such as animal manure, forestry waste, and agricultural waste, and it can be transformed into lactic acid through a biochemical process. There are 942.63 million cattle in the world and annually generate 3.7 billion tons of manure, which could be used to produce lactic acid. The economic viability of a lactic acid plant from cow manure has not yet been determined and is, thus, considered in this study. Using the modeling program Aspen Plus data and other sources, as well as collecting all economic inputs, the feasibility analysis of a lactic acid plant handling cow manure is assessed in this paper. Three scenarios are calculated to check the feasibility depending on the plant size: scenario I handles 1,579,328 t·year-1, scenario II handles 789,664 t·year-1, and scenario III handles 315,865 t·year-1. The results demonstrate that treating the tested lignocellulosic biomass for the manufacture of lactic acid is economically feasible because the economic analysis shows positive net present values for scenarios I, II, and III. The technoeconomic analysis reveals that the minimum lactic acid selling price for scenario I is 0.945 EUR·kg-1, which is comparable to the cost of commercial lactic acid produced from starch feedstock. Scenario II achieves a minimum selling price of 1.070 EUR·kg-1, and scenario III 1.289 EUR·kg-1. The sensitivity analysis carried out reveals that the factor with the biggest impact on the NPV is the yield. Moreover, this study provides a model of industrial application and technoeconomic evaluation for lactic acid production from cow manure.