• Energy Research

Thermal energy storage is a key technology to increase the global energy share of renewables—by matching energy availability and demand—and to improve the fuel economy of energy systems—by recovery and reutilization of waste heat. In particular, the negligible heat losses of sorption technologies during the storing period make them ideal for applications where long-term storage is required. Current technologies are typically based on the sorption of vapor sorbates on solid sorbents, requiring cumbersome reactors and components operating at below ambient pressure. In this work, we report the experimental characterization of working pairs made of various liquid sorbates (distilled water, ethanol and their mixture) and a 13X zeolite sorbent at ambient pressure. The sorbent hydration by liquid sorbates shows lower heat storage performance than vapor hydration; yet, it provides similar heat storage density to that obtainable by latent heat storage (40–50 kWh/m3) at comparable costs, robustness and simplicity of the system, while gaining the long-term storage capabilities of sorption-based technologies. As a representative application example of long-term storage, we verify the feasibility of a sorption heat storage system with liquid sorbate, which could be used to improve the cold-start of stand-by generators driven by internal combustion engines. This example shows that liquid hydration may be adopted as a simple and low-cost alternative to more efficient—yet more expensive—techniques for long-term energy storage.

AbstractWith the advent of Industry 4.0, Artificial Intelligence (AI) has created a favorable environment for the digitalization of manufacturing and processing, helping industries to automate and optimize operations. In this work, we focus on a practical case study of a brake caliper quality control operation, which is usually accomplished by human inspection and requires a dedicated handling system, with a slow production rate and thus inefficient energy usage. We report on a developed Machine Learning (ML) methodology, based on Deep Convolutional Neural Networks (D-CNNs), to automatically extract information from images, to automate the process. A complete workflow has been developed on the target industrial test case. In order to find the best compromise between accuracy and computational demand of the model, several D-CNNs architectures have been tested. The results show that, a judicious choice of the ML model with a proper training, allows a fast and accurate quality control; thus, the proposed workflow could be implemented for an ML-powered version of the considered problem. This would eventually enable a better management of the available resources, in terms of time consumption and energy usage.

Abstract More than two billion people live in areas affected by water stress. In some coastal regions, freshwater supply has been progressively improved by large-scale desalination systems, which are nowadays mostly driven by non-renewable energy sources. Here we discuss, and experimentally investigate, the use of small-scale desalination devices for freshwater production powered by waste heat from electric power generators. The water purification technology relies on a passive, multi-stage and thermally-driven membrane distillation device, recently proposed by some of the authors of this work. The distiller is powered by low-grade (temperature lower than 80 °C) waste heat, recovered from the coolant circuit of small diesel engines for electricity production. Field experiments show that, for the tested engine, up to 1.12 kW m - 2 can be recovered in standard operating conditions, which yield a nearly 2.61 L m - 2 h - 1 freshwater production from seawater. A lumped parameter model, validated by experiments, shows that this productivity could be eventually enhanced by tuning the number of distillation stages. Utilization with exhaust gases, and thus higher feeding working temperatures, is also discussed. The proposed solution may provide a sustainable, simple, inexpensive and efficient means for freshwater production from recovered waste heat, which would otherwise be wasted to the ambient. Therefore it could be particularly effective, for instance, for field hospitals in remote or impoverished areas, especially in emergency situations.

In recent years, various online tools and databases have been developed to assess the potential energy output of photovoltaic (PV) installations in different geographical areas. However, these tools generally provide a spatial resolution of a few kilometers and, for a systematic analysis at large scale, they require continuous querying of their online databases. In this article, we present a methodology for fast estimation of the yearly sum of global solar irradiation and PV energy yield over large-scale territories. The proposed method relies on a multiple-regression model including only well-known geodata, such as latitude, altitude above sea level and average ambient temperature. Therefore, it is particularly suitable for a fast, preliminary, offline estimation of solar PV output and to analyze possible investments in new installations. Application of the method to a random set of 80 geographical locations throughout Europe and Africa yields a mean absolute percent error of 4.4% for the estimate of solar irradiation (13.6% maximum percent error) and of 4.3% for the prediction of photovoltaic electricity production (14.8% maximum percent error for free-standing installations; 15.4% for building-integrated ones), which are consistent with the general accuracy provided by the reference tools for this application. Besides photovoltaic potentials, the proposed method could also find application in a wider range of installation assessments, such as in solar thermal energy or desalination plants.

Abstract Clean water scarcity plagues several hundred million people worldwide, representing a major global problem. Nearly half of the total population lacking access to safe and drinkable water lives in Africa. Nonetheless, the African continent has a remarkable yet untapped potential in terms of renewable energy production, which may serve to produce clean water from contaminated or salty resources and for water extraction and distribution. In this view, the analysis of possible scenarios relies on data-driven approaches due to the scale of the problem and the general lack of comprehensive, direct on-site experience. In this work, we aim to systematically review and map the renewable potentials against the freshwater shortage in Africa to gain insight on perspective possible policies and provide a readily usable and well-structured framework and database for further analyses. All reported datasets are critically discussed, organized in tables, and classified by a few metadata to facilitate their usability in further analyses. The accompanying discussion focuses on regions that, in the near future, are expected to significantly exploit their renewable energy potentials, and on the reasons at the basis of the local water shortage, including technological and distribution problems.

Water pasteurization has the potential to overcome some of the drawbacks of more conventional disinfection techniques such as chlorination, ozonation and ultraviolet radiation treatment. However, the high throughput of community water systems requires energy-intensive processes, and renewable energy sources have the potential to improve the sustainability of water pasteurization plants. In case of water pasteurization by solar thermal treatment, the continuity of operation is limited by the intermittent availability of the solar irradiance. Here we show that this problem can be addressed by a proper design of the plant layout, which includes a thermal energy storage system and an auxiliary gas boiler. Based on a target pasteurization protocol validated by experiments, a complete lumped-component model of the plant is developed and used to determine the operating parameters and size of the components for a given delivery flow rate. Finally, we report an economic analysis of the proposed plant layout, which allows its optimization for different scenarios based on two design variables, namely the solar multiple and the duration of the thermal energy storage. Based on the analyzed cases, it is found that the proposed plant layouts may yield a unit cost of water treatment ranging from ≈32 EUR-cents m−3 to ≈25 EUR-cents m−3.

The design of thermal energy storage (TES) tank is the key part that can limit charging and discharging process. Most research findings highlight that the use of fins augments the heat transfer rate. This work experimentally investigates the use of aligned copper wools as fillers to enhance the thermal performance of a lab-scale shelland- tube TES tank filled with phase change material (PCM). Two copper wools with different fibre thicknesses were chosen and discretely laid around the TES tank tubes in two design patterns. Accordingly, five shell-andtube TES tank configurations were obtained, including the reference, for performance evaluation. The TES tank was loaded with n-octadecane as PCM for all the cases studied. The results showed up to a 16 % reduction in melting time with the inclusion of copper wool. The TES tank significantly increased the mean power during charging (53 %) and discharging (205 %). The addition of metal wool into the TES tank enables the PCM to release the heat at a constant temperature during the entire phase transition process. And the overall efficiency of the TES tank was found to get improved. Therefore, a copper wool integrated TES tank would be a beneficial addition to thermal energy storage systems. This project was funded by the European Union's Horizon Europe Research and Innovation Programme under grant agreement 101084182 (HYBRIDplus). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor the granting authority can be held responsible for them. This work was partially funded by the Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación (AEI) (PID2021-123511OB-C31- MCIN/AEI/10.13039/501100011033/FEDER, UE and RED2022-134219-T). This work is partially supported by ICREA under the ICREA Academia programme. The authors would like to thank the Department de Recerca i Universitats of the Catalan Government for the quality accreditation given to their research group (2021 SGR 01615). GREiA is certified agent TECNIO in the category of technology developers from the Government of Catalonia.