Energy Conversion and Management-X最新文献

Marine macroalgae is a biomass resource for the manufacture of fuels and chemicals, which can be sustainably harvested from seaweed farms or from man-made structures where it accumulates as a biofouling organism. However, in temperate regions farmed macroalgae can only be harvested between late Spring and early Summer, limiting year-round availability. Here we show that a conventional grass ensilage procedure preserves Saccharina latissima dominated biomass on the tonne scale for 30 months, enabling year-round use of this biomass. Following processing, the resulting dried and pelletised ensiled macroalgae material was subject to Thermo-Catalytic Reforming™, comprising sequential pyrolysis (450 °C) and either dry or steam catalytic reforming (700 °C) processes. Both processing methods produced a mixture of bio-oil (1.6–1.9 wt%) and hydrogen-rich permanent gases (30.9–31.1 wt%) with higher heating values of 34.8–35.4 MJ/kg and 18.0–24.2 MJ/m³, respectively, together with char (45.5–48.5 % wt). The permanent gases can be used directly for heat generation, while hydro-treatment of the bio-oil would afford a material that can be blended with traditional transport fuels. This work demonstrates that if operated at scale, the combined harvesting, ensilaging and Thermo-Catalytic Reforming™ of preserved macroalgal biomass offers a year-round decentralised energy resource.

Managing agricultural waste by burning it in the fields is a straightforward method, but leads to significant pollution. One promising alternative is to convert agricultural waste into solid fuel, such as charcoal, to support renewable energy from biomass. The quality of barbecue charcoal depends upon selecting suitable materials and employing heating methods to ensure efficient transformation. This research aims to study the charcoal conversion process from agricultural waste using two types of kilns: 1) direct heating (gasification kiln: GK) and 2) indirect heating (pyrolysis kiln: PK) designed to recirculate syngas from wood as fuel for the pyrolysis process. The study tested three types of agricultural waste materials, including coconut shells (CS), cassava rhizome (CR), and acacia wood (AW), to examine the differences in charcoal produced by the two heating methods. The tests revealed that the maximum temperatures inside the kilns were 792.45 ± 127.18 °C, 907.67 ± 37.3 °C, and 980.07 ± 110.56 °C for the GK, and 921.88 ± 57.84 °C, 801.93 ± 10.16 °C, and 937.82 ± 95.85 °C for the PK. The charcoal from the PK exhibited higher calorific values than the GK, with 7474.68 ± 36.62, 6429.04 ± 72.22, and 7268.33 ± 52.86 calories per gram. The charcoal yield was also higher in the PK, at 31.29 ± 4.39, 34.33 ± 3.39, and 17.58 ± 2.09 percent for coconut shells charcoal (CSC), cassava rhizome charcoal (CRC), and acacia wood charcoal (AWC), respectively. However, the PK required more fuel and longer ignition times. The resulting charcoal from the slow pyrolysis process in the PK is suitable as barbecue fuel due to its size, which is similar to the original material. In contrast, the charcoal from the GK, which tends to shrink or break into smaller pieces, is more suitable for grinding into briquettes. This study provides a guideline for producing high-quality barbecue charcoal, offering commercial benefits including the gasification and pyrolysis processes that improve combustion efficiency and reduce pollution by producing high-quality gas for fuel, unlike traditional kilns that emit a large amount of CO during the conversion of wood to charcoal and enabling the selection of appropriate raw materials for different heating methods to maximise the utility of the products. This approach adds value to agricultural raw materials and helps effectively manage agricultural waste (zero waste) for further utilisation and development.

Adsorption cooling systems (ACS) powered by low-temperature heat offer an energy-efficient and environmentally friendly alternative to traditional vapor-compression systems. The effectiveness of ACS is significantly influenced by the alignment of the adsorbent properties with the operating conditions of the cycle. Metal-Organic Frameworks (MOFs) are considered the next generation of water harvesting and ACS. Many MOFs are synthesized and tested for water harvesting systems, one of these MOFs is MOF-303 which was reported to have very rapid water sorption dynamics under atmospheric conditions. However, MOF-303 has never been tested under the same conditions as ACS (under vacuum). In this study, the isotherms and kinetics of water adsorption on MOF-303, as an efficient adsorbent of water vapor, is experimentally investigated for the ACS using the linear driving force model. The diffusion coefficients across a wide range of relative pressures under two different temperatures were estimated. The study compares the adsorption process of MOF-303 with traditional silica gel (SG) in the context of diffusion kinetics relevant to ACS. Based on the output and at a constant temperature of 25 °C and across all relative pressure ranges, MOF-303 exhibited an average increase of approximately eight times in diffusion kinetics compared to SG. Specifically, within the relative pressure range of 10–30 %, which is optimal for ACS, MOF-303 demonstrated a seven-fold increase in diffusion kinetics over SG. The diffusion values for SG display a clear upward trend with increasing temperature. In contrast, the diffusion values for MOF-303 are subject to fluctuations with temperature changes under investigation. Notably, the isotherm for MOF-303 shows an inflection point at relative pressures between 10–15 %, causing a significant reduction in diffusion at these specific relative pressures compared to other relative pressure values. The findings in this study highlight the potential use of MOF-303 as a highly efficient water adsorbent for the ACS which will enable scientists and engineers to develop sustainable low-grade energy systems.

This research puts forward the modeling of a Hydrogen Fuel Cell Electric Vehicle (HFCEV) and its validation, in comparison with battery electrical vehicle (BEV) based on a postal vehicle and its subsystems. The main investigation parameters are the amount of hydrogen consumed, and the change in the state of charging of the battery. For the profile of the same speed route, an HFCEV model and a BEV model, consisting of multiple subsystems, were developed, and simulated in the MATLAB® Simulink environment. We make use of various sources of drive cycles to obtain our outcomes such as the New European Diving Cycle (NEDC). By running simulations for different stages, we are able to generate simulation results. The discrepancies in the graphs and the visual demonstrations guided us to variable conclusions on how different factors affect an electric vehicle’s performance and efficiency. The simulation result shows that the (BEV) is 30% more effective for NEDC drive cycle comparing with (HFCEV).

This article conducted a comprehensive study on a three-story residential building in Yazd, Iran, which was meticulously modeled using DesignBuilder. The primary objective was to investigate the impact of BioPCMs (Bio Phase Change Materials), which are environmentally friendly materials, on the building’s thermal performance. For this purpose, BioPCM® M182/Q21 was selected due to its effectiveness in enhancing energy efficiency. The study involved a practical comparison of the building’s energy consumption under three scenarios. The first scenario represented the baseline condition where no PCM was used. The second scenario incorporated PCM into the external walls, while the third scenario extended the use of PCM to both the external and internal walls. In the second scenario, where PCM was applied only to the external walls, there was a 9% reduction in annual energy consumption. The third scenario, which utilized PCM in both the external and internal walls, resulted in a reduction of 15.5% in annual energy consumption. Additionally, when PCM was used in both the external and internal walls, the total energy consumption for cooling and heating the building decreased by 5.4% and 18.9%, respectively.

With the increasing importance of energy efficiency and sustainability, the demand for high-performance district heating networks is also on the rise. As traditional engineering methods were often no longer sufficient, several packages for numerical simulation have evolved. Many of them offer high accuracy at the cost of considerable manual preparation and computational effort. However, there is still no user-friendly software that is equally suitable for simplified studies in the early project phase, for the rapid optimisation of multiple concepts and for subsequent detailed planning and dimensioning. For this reason and in cooperation with some companies from the energy sector, we had conceived a novel, highly flexible modular approach, the so-called “quantum networks”, where all parts of the district heating network are appropriately abstracted into quantum elements. Now we present our recent implementation of this model in an object-oriented C++ library. Starting from a generalised base class and making use of the concepts of inheritance and polymorphism, the idea of different levels of detail for the same element type is directly realised and can always be further refined. In addition, as all elements are derived from the same base class, they all share the same outer appearance and can thus be easily combined or interchanged. Based on these prerequisites, a dedicated thermo-hydraulic solver has then been developed. Thanks to its recursive design requiring neither outer iterations nor matrix inversions, it proves to be extremely fast and is therefore suitable for rapid design or optimisation studies in which a vast number of configurations has to be computed. To conclude this part of the work, the developed C++ library was benchmarked on two use cases covering a full year of operation that can now be computed in approximately one second of runtime.

CO₂ photocatalytic reduction is a potential and promising technology to reduce the level of the greenhouse gas in the atmosphere but also as an alternative and renewable fuel resource. However, the products yield of the reaction is still low and the identification of the optimal operating conditions that affect the process are still needed to be determined. This study investigates the impact of key operational parameters, specifically photocatalyst concentration and stirring speed, on the photocatalytic reduction of CO₂ in a slurry batch photoreactor utilizing synthesized TiO₂. A simplified photocatalytic kinetic model, incorporating the radiation field within the photoreactor, was developed, considering mass transfer from liquid to gas phase for the primary detected reaction products (CO, CH₄, and H₂). The proposed models elucidate the influence of different operating conditions on product yields. Stirring speed, controlled by a magnetic stirrer, impacts the gas–liquid mass transfer rate. Increased liquid phase stirring speed ensures faster species transport to the gas phase, with a diminishing effect beyond 900 rpm. TiO₂ photocatalyst mass concentration influences the available total active surface and irradiation absorbance in the photoreactor volume. Optimal product yields were observed at the lowest tested photocatalyst concentration (0.5 g · L^-1), indicating improved irradiation distribution and reduced particle agglomeration, resulting in higher available active surface for the reaction. The calculation model successfully predicted product yields even with lower photocatalyst concentration of 0.25 g · L^-1, with marginal increases in predicted yields. These findings provide valuable insights for scaling up and optimizing the CO₂ photocatalytic reduction process, offering a foundation for future research.

Tidal barrage power plants utilise the tidal range variation to generate clean electricity. Although there are several operating tidal barrage schemes around the globe, there is still potential to expand the installed capacity. Given their inherent storage and the high predictability of the tides, tidal barrages can be operated with more flexibility than many other renewables. This means that the control objective of a barrage operation can vary from energy maximisation to constant power output, or demand-matching objectives. The operation of a barrage also influences its impact on the environment and economic activity of the site where it is located, which is a major cause for the slow deployment of such power plants. The aim of this study is to provide a comprehensive and critical analysis of the different strategies considered to date to optimise the operation of tidal barrages, with a focus on an in-depth analysis of the optimisation schemes employed, the barrage models utilised, and opportunities for further improvement.

The utilization of solar energy is picking up speed to counter climate change. New large-scale photovoltaic power stations are being constructed to increase solar utilization in the energy mix. A critical input of site selection for solar farms is the solar energy generation potential at a given location. Various physical and satellite-based inversion models are proposed to estimate the solar irradiation reaching the ground at potential locations, based on the meteorological features. However, the meteorological features generally contain outlier observations that distract the solar irradiation estimation models. To address this challenge, this study employs a robust fuzzy regression functions framework against the outliers to estimate the global horizontal irradiance (GHI) in Australia. Our framework is benchmarked with support vector machines, deep neural networks, and an adaptive network-based fuzzy inference system, and better GHI estimation performance is observed. The proposed framework provides 24 %, 18 %, and 23 % gain over the second-best method in terms of the rescaled mean absolute error, absolute percentage bias and rescaled root-mean-squared error. Monthly and annual GHI maps are created for Australia and compared to those from NASA POWER GHI estimates and Solargis annual GHI estimates. Our framework has an error range between 0.075 % and 2.9 % when validated against ground measurements. It provides at least an average of 40% lower monthly and annual error rates than POWER. This rate of gain rises to 69% when compared to Solargis. Our maps are not impacted by terrestrial characteristics and clear-sky conditions. This study’s results are beneficial in site selection and construction of high-precision GHI estimation models for practitioners.

Wind energy plays a pivotal role in the ongoing effort to reduce carbon emissions in the energy sector. With the increasing evidence of climate change, there is a growing concern regarding the planning and operation of wind energy resources. Accurate forecasts are essential to understand the frequency distribution of wind speed data in a given area and, consequently, to estimate energy production. This paper aims to analyze the wind resources under climate change, assess their potential, and create zoning maps for wind energy production in the island of Ireland. For this objective, wind speed data from 31 general circulation models (GCMs) and two climate change scenarios were utilized for both hindcast and forecast periods in 1981–2010 and 2021–2050, respectively. The GCM outputs were first bias-corrected and then post-processed using various (non–)parametric statistical distributions and 3 Copula families. The results indicate an expected decrease in the average wind speed in the region up to ∼ 21 % by 2050, contingent on the climate scenarios under consideration and the target point. Ultimately, this study concludes by presenting wind power density maps specifically to the study region, offering valuable insights for sustainable energy planning.