• Energy Research

Insulation of thermal energy storage tanks is fundamental to reduce heat losses and to achieve high energy storage efficiency. Although water tanks were extensively studied in the literature, the enhancement of the insulation quality is often overlooked. The use of vacuum insulation has the potential to significantly reduce heat losses without affecting the dimension of the storage system. This paper shows for the first time the results of the heat losses tests done for a 0.535 m3 water tank for residential building applications built with a double wall vacuum insulation. The different tests show that the rate of heat losses strictly depends on the temperature distribution inside the tank at the beginning of the experiment. Compared to a conventional water tank insulated with conventional materials, the U-value of the lateral surface was reduced by almost three times (from 1.05 W/K·m2 to 0.38 W/K·m2) using vacuum insulation. However, the bottom part, which is usually used to place the support parts and the piping, is the critical design part of those tanks acting as a thermal bridge with the ambient and enhancing heat losses.

Insulation of thermal energy storage tanks is fundamental to reduce heat losses and to achieve high energy storage efficiency. Although water tanks were extensively studied in the literature, the enhancement of the insulation quality is often overlooked. The use of vacuum insulation has the potential to significantly reduce heat losses without affecting the dimension of the storage system. This paper shows for the first time the results of the heat losses tests done for a 0.535 m3 water tank for residential building applications built with a double wall vacuum insulation. The different tests show that the rate of heat losses strictly depends on the temperature distribution inside the tank at the beginning of the experiment. Compared to a conventional water tank insulated with conventional materials, the U-value of the lateral surface was reduced by almost three times (from 1.05 W/K·m2 to 0.38 W/K·m2) using vacuum insulation. However, the bottom part, which is usually used to place the support parts and the piping, is the critical design part of those tanks acting as a thermal bridge with the ambient and enhancing heat losses.

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.

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.

The present research has been carried out to provide a failure prediction tool for a Chilean concentrated juice company. The thermal energy required in the company’s processes is supplied through a liquefied petroleum gas (LPG) boiler, whose feed water is preheated by a parabolic trough solar collector. According to monitored data and computer simulations carried out during 2017, it was possible to identify a low performance of the solar field. For the low performance of the solar field, it has been possible to identify the following faults: inaccuracies of the solar tracking system, low cleaning frequency of the solar field, low effectiveness of the heat exchanger between the solar field and the processes feed water circuit, among others. A condition-based maintenance tool was developed to detect failures in the solar field using machine learning techniques. The tool uses a set of four machine learning models to detect and identify the existence and source of faults such as soiling factors in the solar field, problems with the solar tracking system, problems with pumps and faults in the heat exchanger. For faults greater than or equal to 20%, the tool can identify the source of the fault 80% of the time if it comes from the solar field or heat exchanger, however, if the fault comes from one of the pumps the performance is lower. The tool generates false positives 21% of the time when it is used the model to detect faults at a global level of the solar thermal plant. This tool could be used to optimally manage the solar plant and maximize the cost savings.

The present research has been carried out to provide a failure prediction tool for a Chilean concentrated juice company. The thermal energy required in the company’s processes is supplied through a liquefied petroleum gas (LPG) boiler, whose feed water is preheated by a parabolic trough solar collector. According to monitored data and computer simulations carried out during 2017, it was possible to identify a low performance of the solar field. For the low performance of the solar field, it has been possible to identify the following faults: inaccuracies of the solar tracking system, low cleaning frequency of the solar field, low effectiveness of the heat exchanger between the solar field and the processes feed water circuit, among others. A condition-based maintenance tool was developed to detect failures in the solar field using machine learning techniques. The tool uses a set of four machine learning models to detect and identify the existence and source of faults such as soiling factors in the solar field, problems with the solar tracking system, problems with pumps and faults in the heat exchanger. For faults greater than or equal to 20%, the tool can identify the source of the fault 80% of the time if it comes from the solar field or heat exchanger, however, if the fault comes from one of the pumps the performance is lower. The tool generates false positives 21% of the time when it is used the model to detect faults at a global level of the solar thermal plant. This tool could be used to optimally manage the solar plant and maximize the cost savings.

The use of latent heat thermal energy storage is an effective way to increase the efficiency of energy systems due to its high energy density compared with sensible heat storage systems. The design of the storage material encapsulation is one of the key parameters that critically affect the heat transfer in charging/discharging of the storage system. To fill the gap found in the literature, this paper experimentally investigates the effect of the macro-encapsulation design on the performance of a lab-scale thermal energy storage tank. Two rectangular slabs with the same length and width but different thickness (35 mm and 17 mm) filled with commercial phase change material were used. The results show that using thinner slabs achieved a higher power, leading to a reduction in the charging and discharging time of 14% and 30%, respectively, compared with the thicker slabs. Moreover, the variation of the heat transfer fluid flow rate has a deeper impact on the temperature distribution and the energy charged/released when thicker slabs were used. The macro-encapsulation design did not have a significant impact on the discharging efficiency of the tank, which was around 85% for the operating thresholds considered in this study.