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Abstract In an effort to minimise electricity consumption and greenhouse gases emissions, the heating, ventilation and air-conditioning sector has focused its attention on developing alternative solutions to electrically-driven vapour-compression cooling. Liquid desiccant air-conditioning systems represent an energy-efficient and more environmentally friendly alternative technology for dehumidification and cooling, particularly in those cases with high latent loads to maintain indoor air quality and comfort conditions. This technology is considered particularly efficient in hot and humid climates. As a matter of fact, the choice of the desiccant solution influences the overall performance of the system. The current paper reviews the working principle of liquid desiccant systems, focusing on the thermodynamic properties of the desiccant solutions and describes an evaluation of the reference thermodynamic properties of different desiccant solutions to identify which thermodynamic, physical, transport property influences the liquid desiccant process and to what extent. The comparison of these thermodynamic properties for the commonly used desiccants is conducted to estimate which fluid could perform most favourably in the system. The economic factors and the effect of different applications and climatic conditions on the system performance are also described. The paper is intended to be the first step in the evaluation of alternative desiccant fluids able to overcome the problems related to the use of the common desiccant solutions, such as crystallization and corrosion to metals. Ionic liquids seem a promising alternative working fluid in liquid desiccant air-conditioning systems and their characteristics and cost are discussed.

Thermal comfort is an important concern in the energy efficiency improvement of commercial buildings. Thermal comfort research focuses mostly on temperature control, but humidity control is an important aspect to maintain indoor comfort too. In this paper, an optimal humidity control model (OHCM) is presented. Model predictive control (MPC) strategy is applied to implement the optimal operation of the desiccant wheel during working hours of a commercial building. The OHCM is revised to apply the MPC strategy. A case is studied to illustrate the practical applications of the MPC strategy.

Abstract Urban planners, local authorities, and energy policymakers often develop strategic sustainable energy plans for the urban building stock in order to minimize overall energy consumption and emissions. Planning at such scales could be informed by building stock modeling using existing building data and Geographic Information System-based mapping. However, implementing these processes involves several issues, namely, data availability, data inconsistency, data scalability, data integration, geocoding, and data privacy. This research addresses the aforementioned information challenges by proposing a generalized integrated methodology that implements bottom-up, data-driven, and spatial modeling approaches for multi-scale Geographic Information System mapping of building energy modeling. This study uses the Irish building stock to map building energy performance at multiple scales. The generalized data-driven methodology uses approximately 650,000 Irish Energy Performance Certificates buildings data to predict more than 2 million buildings’ energy performance. In this case, the approach delivers a prediction accuracy of 88% using deep learning algorithms. These prediction results are then used for spatial modeling at multiple scales from the individual building level to a national level. Furthermore, these maps are coupled with available spatial resources (social, economic, or environmental data) for energy planning, analysis, and support decision-making. The modeling results identify clusters of buildings that have a significant potential for energy savings within any specific region. Geographic Information System-based modeling aids stakeholders in identifying priority areas for implementing energy efficiency measures. Furthermore, the stakeholders could target local communities for retrofit campaigns, which would enhance the implementation of sustainable energy policy decisions.

Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics is based on the analysis of sequences of ground-taken sky images or satellite observations. Despite encouraging results, a recurrent limitation of existing deep learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a frequent temporal lag and limited ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatio-temporal neural network architecture that models cloud motion from sky images to not only predict future irradiance levels and associated uncertainties, but also segmented images, which provide richer information on the local irradiance map. We show that ECLIPSE anticipates critical events and reduces temporal delay while generating visually realistic futures. The model characteristics and properties are investigated with an ablation study and a comparative study on the benefits and different ways to integrate auxiliary data into the modelling. The model predictions are also interpreted through an analysis of the principal spatio-temporal components learned during network training. Manuscript accepted for publication in Applied Energy

Abstract The almost two-dimensional steady-state rates of heat loss from arrays of uniformly-spaced vertical rectangular fins, extending upwards—in otherwise stagnant air—from horizontal heated bases, have been measured. (The vertical air gaps between the fins were closed at their sides, by insulated vertical end-barriers.) The effects of various combinations of height, thickness and spacing of the fins, for different base temperatures (in the range 40 to 100°C), have been studied. For the configuration considered, in a normal ambient environment (∼ 20°C), there is an optimal fin spacing (⋍ 16 mm) corresponding to the greatest steady-state rate of free convective/conductive heat loss through the air from the finned system, and this is almost independent of the temperature of the heat exchanger base (in the range 40–100°C). At this optimal spacing for base temperatures not greater than 50°C, the convective/conductive heat transfer rate from the array increases with the fin height up to about 60 mm, so that it would be uneconomic to employ taller fins if convection/conduction is dominant compared with radiation. If the radiation contribution is also considered, then the optimal spacing corresponding to the maximum total steady-state rate of heat loss through the air is somewhat less than the optimal spacing for which, under the same temperature conditions, the maximum steady-state rate of convective/conductive heat leak occurs. The greater the emissivity of the heat exchanger surfaces, the narrower the optimal uniform gaps between the fins. A two-dimensional finite-difference computer program has been composed to predict the temperature distribution throughout the heat exchanger for a stipulated ambient environmental temperature and experimentally-determined distribution of the heat transfer coefficient over the surfaces of the exchanger. This enables, for instance, any hot spots to be located prior to a proposed design being built.

Abstract The present work focuses on the numerical simulation of Moderate or Intense Low oxygen Dilution combustion condition, using the Partially-Stirred Reactor model for turbulence-chemistry interactions. The Partially-Stirred Reactor model assumes that reactions are confined in a specific region of the computational cell, whose mass fraction depends both on the mixing and the chemical time scales. Therefore, the appropriate choice of mixing and chemical time scales becomes crucial to ensure the accuracy of the numerical simulation prediction. Results show that the most appropriate choice for mixing time scale in Moderate or Intense Low oxygen Dilution combustion regime is to use a dynamic evaluation, in which the ratio between the variance of mixture fraction and its dissipation rate is adopted, rather than global estimations based on Kolmogorov or integral mixing scales. This is supported by the validation of the numerical results against experimental profiles of temperature and species mass fractions, available from measurements on the Adelaide Jet in Hot Co-flow burner. Different approaches for chemical time scale evaluation are also compared, using the species formation rates, the reaction rates and the eigenvalues of the formation rate Jacobian matrix. Different co-flow oxygen dilution levels and Reynolds numbers are considered in the validation work, to evaluate the applicability of Partially-Stirred Reactor approach over a wide range of operating conditions. Moreover, the influence of specifying uniform and non-uniform boundary conditions for the chemical scalars is assessed. The present work sheds light on the key mechanisms of turbulence-chemistry interactions in advanced combustion regimes. At the same time, it provides essential information to advance the predictive nature of computational tools used by scientists and engineers, to support the development of new technologies.

In-situ biomethanisation reduces the CO2 in biogas to CH4 via direct H2 injection into an anaerobic digester, but volumetric methane production (VMP) is limited by organic loading. Ex-situ biomethanisation, where gaseous substrates are fed to pure or mixed cultures of hydrogenotrophic methanogens, offers higher VMP but requires an additional reactor and supply of essential nutrients. This work combined the two approaches in a novel hybrid application achieving simultaneous in-situ and ex-situ biomethanisation within an organically-loaded anaerobic digester receiving supplementary biogas. Conventional stirred-tank digesters were first acclimated to H2 addition, increasing biogas methane content from 50% to 95% and VMP from 0.86 to 1.51 L L-1 day-1 at a moderate loading rate of 3 g organic chemical oxygen demand per L per day (g CODorg L-1 day-1). Externally-produced biogas was then added to demonstrate simultaneous biomethanisation of endogenous and imported CO2. This further increased VMP to 2.76 L L-1 day-1 without affecting organic substrate degradation. In-situ CO2 reduction can alter digester pH by reducing bicarbonate buffering: the combined process operated stably at around pH 8.0 with 3-5% CO2 in the headspace. Microbial community analysis indicated the process was mediated by bacterial syntrophic acetate oxidation and highly enriched hydrogenotrophic methanogenic archaea (up to 97% of the archaeal population). This approach presents the opportunity to retrofit a single digester for H2 injection to convert and upgrade biogas from several others, minimising capital and operating costs by utilising both existing infrastructure and waste-derived feedstock nutrients for simultaneous biogas upgrading and power-to-methane.

This special edition to be published in Applied Energy brings together a range of papers that explore the complex, multi-dimensional and inter-related issues associated with the supply or value chains that make up energy systems and how a focus on them can bring new insights for energy security in a low carbon transition.\ud \ud Dealing with the trilemma of maintaining energy security, reducing greenhouse gas emissions and maintaining affordability for economies and end users are key issues for all countries, but there are synergies and trade-offs in simultaneously dealing with these different objectives. Currently, industrialised energy systems are dominated by supply chains based on fossil fuels and these, for the most part, have been effective in enabling energy security and affordability. However, they are increasingly struggling to do this, particularly in respect to efforts to tackle climate change, given that the energy sector is responsible for around two-thirds of the global greenhouse gas emissions [1]. A key challenge is therefore how to decarbonise energy systems, whilst also ensuring energy security and affordability. This special issue, through a focus on supply chains, particularly considers the interactions and relationships between energy security and decarbonisation.\ud \ud Energy security is a property of energy systems and their ability to withstand short-term shocks and longer-term stresses depends on other important system properties including resilience, robustness, flexibility and stability [2]. Energy systems are essentially a supply chain comprising of multiple and interrelated sub-chains based around different fuels, technologies, infrastructures, and actors, operating at different scales and locations – from extraction/imports and conversion through to end use [3]. These supply chains have become increasingly globalised and are influenced by the on-going shifts in global supply and demand. Thus the aim of this special issue is to explore and discuss how to enable the development of a secure and sustainable energy system through a better understanding of both existing and emerging low carbon energy supply chains as well as of new approaches to the design and management of energy systems. In part, because moving from a system dominated by fossil fuels to one based on low carbon creates a new set of risks and uncertainties for energy security as well as new opportunities.\ud \ud A large number of submissions from over 18 countries were received for this special edition and 16 papers were accepted after peer review. These address a variety of issues and we have chosen to discuss the findings under two key themes, although many of the papers cut across these: (1) Insights from, and for, supply chain analysis. (2) Insights for energy security and its management. We then provide in (3) a summary of insights and research gaps. Table 1 provides a snapshot of the areas covered by the papers showing: theme (s); empirical domains; and geographical coverage.