Search Results (1,011)

Kelp is a high-moisture, flexible, sheet-like marine biomass whose drying behavior is strongly affected by the coupled effects of radiative heating, convective vapor removal, internal moisture migration, tissue shrinkage, curling, and material overlap. Traditional sun drying and hot-air drying remain widely used but are limited by long processing cycles, environmental dependence, high energy consumption, and inconsistent product quality. With the development of infrared heating, heat-pump dehumidification, Internet of Things (IoT)-enabled sensing, fifth-generation (5G) mobile communication, multiphysics simulation, and digital control, kelp drying is progressively shifting toward monitored, model-assisted, and intelligent processing. This review critically summarizes recent advances in kelp and related seaweed drying, with particular emphasis on infrared-assisted heat and mass transfer, drying kinetics, coupled computational fluid dynamics–finite element method (CFD–FEM) simulation, quality evaluation, and intelligent control. Representative published studies demonstrate the engineering potential of these approaches. In a suspended infrared-array kelp drying system, an infrared power density of 1.2 kW m−2 combined with an air velocity of 3 m s−1 maintained the drying temperature at approximately 55–62 °C, while relative humidity decreased from about 80% to 20–30%. Under these conditions, the Page model achieved R² = 0.987 and RMSE = 0.019, the rehydration ratio exceeded 94%, and the total color difference remained below ΔE = 6.5. A recent CFD–FEM–MATLAB workflow further reported a composite operating-condition index of J = 0.4535, with mapped mean and maximum kelp surface temperatures of 62.23 and 63.57 °C, respectively. These quantitative results indicate that the key challenge in infrared kelp drying is not simply to increase heat input, but to coordinate radiation distribution, airflow organization, internal moisture transport, structural response, and quality preservation. Future research should therefore focus on experimentally validated heat–mass-transfer models, adaptive sensing and control, multi-objective optimization, and pilot-scale verification under realistic production conditions. Full article

The power injections variability due to volatile renewable energy sources and large dynamic loads (e.g., Plug-in Electric Vehicles and Heat Pumps) may cause excessive voltage fluctuations and power system instability. To mitigate these problems, accurate load profiles are needed to support both grid operation and planning. However, real load profiles are not always readily and fully available due to technical and privacy constraints, limiting their applicability and the possibility of extrapolating consumption patterns for prospective studies. Synthetic load profile generation offers a practical alternative to address these limitations, while preserving data privacy and accessibility. This paper presents a deep learning framework that combines Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to produce artificial, but data-driven seasonal weekly load profiles (SWLP). The training process for each cluster uses separate WGANs, which implement the Wasserstein loss function together with gradient penalty to ensure stable training, while preventing mode collapse. The quality of the synthetic profiles is evaluated using statistical and distribution-based metrics. The proposed WGAN-GP achieved an average Wasserstein distance of 0.042 with a pattern correlation coefficient of about 0.98 with respect to the real load profiles derived from an Irish residential dataset. In comparison, the WGAN without gradient penalty returned am averageWasserstein distance of 0.352, while a Variational Autoencoder (VAE) used as a benchmark achieved an average Wasserstein distance of 0.108 with a pattern correlation coefficient of 0.92. Mean profile comparisons and load distribution analyses showed a good agreement between real and synthetic data across all identified consumption-pattern clusters. These results demonstrate the capability of the proposed framework to generate SWLPs preserving the statistical and temporal characteristics of real electricity consumption data. Full article

This study investigates the integration of CO₂/H₂S separation and internal power generation in power- and biomass-to-liquid (PBtL) and fuel-assisted PBtL (FAPBtL) processes to enhance carbon efficiency and reduce external electricity demand. Four configurations (PBtL, FAPBtL-recycle, FAPBtL-purge, and Purge-to-Fuel (Purge-tF)) are evaluated through detailed mass and energy balances, thermal integration analysis, and techno-economic assessment. Reintegration of separated CO₂ eliminates carbon losses in the acid gas removal unit, increasing carbon efficiencies to approximately 98% for PBtL and Purge-tF, 97% for FAPBtL-recycle, and 79% for FAPBtL-purge. Increasing the carbon efficiency from 91% to 98%, for PBTL, comes from capturing 85% of the CO₂ downstream of the acid gas removal unit. In parallel, integration of a supercritical two-step reheat Rankine cycle with preheating enables the recovery of high-temperature process heat, increasing cycle efficiency from 42% to 55% and generating up to 61 MW of internal power. Although CO₂/H₂S separation introduces additional capital and energy requirements, the combined integration of carbon recycling and heat-to-power recovery improves overall system performance. The Purge-tF configuration achieves the lowest net production cost of 2.60 €/kg_fuel (2.11 €/L_fuel). Sensitivity analysis confirms electricity price as the dominant economic driver. The results demonstrate that strategic integration of carbon recycling and advanced heat recovery can substantially improve both the carbon utilization and economic viability of biomass-based synthetic fuel production. Full article

The transition towards circular economy models is driving the transformation of wastewater treatment plants (WWTPs) from energy-intensive facilities into resource recovery systems capable of generating renewable energy. In this context, this study developed an integrated mass and energy balance methodology to assess sludge production, anaerobic digestion performance, biomethane recovery, and electricity generation in a full-scale urban WWTP. The proposed framework integrates the water treatment line, sludge processing line, and energy recovery system, combining primary and secondary sludge management with biogas upgrading and combined heat and power (CHP) generation. Representative operating parameters from the scientific literature were applied to a facility treating 204,000 m³ d⁻¹ and serving approximately 425,000 population equivalents. The results showed that primary sludge accounted for approximately 70% of the volatile solids fed to the anaerobic digester. Methane production was estimated at 1.12 × 10³ kg CH₄ d⁻¹, corresponding to a biogas production of 2.40 × 10³ m³ d⁻¹. Under two alternative valorisation scenarios, the maximum recovered biomethane flow was 1.48 × 10³ m³ d⁻¹, whereas the maximum annual electricity generation potential through CHP was 1.9 × 10⁶ kWh. These findings highlight the potential of integrated sludge valorisation strategies to enhance renewable energy recovery and support the transition of WWTPs towards energy-efficient and low-carbon resource recovery facilities. Full article

Combined cooling, heating, and power (CCHP) tri-generation systems can greatly improve comprehensive energy utilization efficiency thanks to their energy-cascade utilization concept. However, traditional fossil-fuel-based CCHP systems still suffer from intensive carbon emissions, hindering their further development in the current low-carbon scenario. To solve this issue, a new low-carbon CCHP system (LC-CCHP) integrating a calcium-looping, sorption-enhanced steam methane reforming (CL-SE-SMR) unit, a lithium bromide absorption chiller, and a hydrogen gas turbine is proposed in this work, and the corresponding system model is built to evaluate its performance. The proposed system features an innovative architecture that integrates carbon capture directly into the reforming process, which simultaneously enables a high hydrogen yield and low carbon-capture penalty. Moreover, instead of the widely used thermodynamic equilibrium assumption, a detailed kinetic model is employed for the CL-SE-SMR unit, which provides more realistic predictions and greater reference value for practical engineering applications. Then, multi-objective optimization is conducted using a particle swarm optimization algorithm to identify the optimal operating conditions. It is found that the proposed system performs best at a steam-to-carbon molar ratio of 4.37, a calcium-to-carbon mass ratio of 6.23, an air-equivalency molar ratio of 1.39 for a hydrogen gas turbine and a reaction temperature of 600 °C for SE-SMR. Under these operating conditions, the system can achieve a carbon-capture rate of 89.2%, an exergy efficiency of 45.7%, an energy efficiency of 95.4%, and a levelized cost of exergy of 0.109 $/kWh. Full article

Resilience and adaptation to extreme climate events have become an urgent necessity. As cities grow denser, increasing numbers of people are exposed to water scarcity, flooding, and power grid disruptions. Immediate action is required to safeguard human lives and property. Residential buildings exert a significant environmental impact throughout their operational phase, contributing to air, land, and water pollution. A more sustainable and proactive approach to building management is essential to reduce the consumption, processing, and disposal of natural resources. This article explores the potential for biogas production from decentralised/on-site wastewater treatment systems through the co-digestion of blackwater (BW) and kitchen waste (KW) for existing residential buildings located in densely populated urban areas using hybrid grids. It addresses the importance of wastewater source separation, the use of BW and KW blends to achieve the best biogas production, and the environmental, economic and social aspects of these systems’ implementation. An extensive literature review and state-of-the-art analysis were conducted to assess the potential, main challenges, and research directions in this field. The results indicate that decentralised anaerobic systems can be technically feasible, reducing grid energy dependence, optimising water use, and valorising digestate as fertiliser—fully aligned with the EU’s Green Deal and the UN Sustainable Development Goals regarding sustainability and circularity. However, few studies address the feasibility of BW (vacuum toilet) and KW co-digestion for combined heat and power generation in hybrid grids. Further pilot- and full-scale research is therefore needed to increase system reliability and social acceptance. Full article

The growing integration of variable renewable energy sources (VRES) challenges power system flexibility and may cause curtailment due to excess capacity, grid constraints, or operational and market factors. Power-to-Heat (PtH) technology can mitigate these issues by coupling electricity and district heating sectors, providing additional flexibility and supporting decarbonisation. This study develops a long-term generation capacity expansion model that integrates PtH and district heating system (DHS) operation to achieve balanced VRES penetration. The model includes DHS heat demand balances and links electricity and heat via thermal power plants, combined heat and power (CHP) plants, and PtH units. The methodology is applied to Ukraine’s Integrated Power System and district heating demand through 2040, employing typical daily load profiles discretised into six four-hour segments. Results demonstrate the feasibility of deploying PtH electric boilers during the non-heating season, when high RES and base load nuclear generation create surplus electricity. These boilers convert excess wind and solar power into thermal energy for district heating, displacing natural gas-fired technologies and simultaneously decarbonising electricity and heat supply. Full article

Residential buildings are central to decarbonisation because existing dwellings combine long service lives, high heating demand and heterogeneous constraints for retrofitting. This study investigates some retrofit strategies for a terraced house in Oxford, UK, to achieve the winter indoor air temperature set-point while reducing energy use, costs and CO₂ emissions. A calibrated dynamic simulation model was developed from on-site inspections, monitored temperatures, occupant schedules and energy-bill data. The analysis compares baseline configuration with scenarios including radiator power upgrading, envelope insulation, increased internal thermal mass and replacement of the condensing boiler with a high-temperature ground-source heat pump (GSHP). The results show that radiator upgrading enables the most critical rooms to reach the 20 °C set-point, while envelope insulation reduces heating energy and costs. Increased thermal mass improves night-time temperature stability, although its effect on annual energy demand is limited. The GSHP provides the largest primary energy reduction, lowering operational primary energy by 66.3% compared to the reference case and by 70.4% when combined with envelope and thermal-mass measures. Operational CO₂ emissions are reduced by 35.0–84.3%. The study highlights the need to evaluate the capacity of heat emitters, building envelope performance, thermal inertia and heat generator efficiency within a dynamic framework. Full article

The sizing of rooftop solar energy systems is commonly based on the most visible load or on generic end-use allocations, leading to an inadequate distribution of the limited rooftop area between heat and electricity. This study formalizes the energy audit within a three-level deterministic framework that selects and sizes solar technologies by energy vector: demand is first decomposed by vector; the technology for the thermal vector is then selected through a levelized cost of heat selection ratio $\psi$, while the photovoltaic system of the electrical vector is sized for self-consumption; and the rooftop area is finally allocated among vectors according to marginal value per unit area. In a 75-bed university residence in Cienfuegos, Cuba, air conditioning is the dominant energy end-use in terms of installed power (accounting for $77\%$ of the connected load), whereas the thermal vector dominates annual energy consumption (domestic hot water: 127,440 versus 76,818 kWh/year for electricity; thermal-to-electric ratio $1.66$). Solar thermal technology has been selected for the thermal vector ($0.018$ versus $0.088$ USD/kWh_th; $\psi =0.21$, a robust value according to the sensitivity analysis), and the marginal value (≈111 versus ≈32 USD/(m²·year)) allocates 104 m² to solar thermal collectors and 134 m² to photovoltaic energy, thereby reversing the original design that prioritized photovoltaic energy. The resulting portfolio achieves an annual solar fraction close to $100\%$ in both vectors on an energy balance basis, avoids $86.5$ t of operational CO₂ emissions per year, and combines a simple payback of $1.1$ years (solar thermal) with a net present value of 55,327 USD and an internal rate of return of $28\%$ (photovoltaics). The sizing decision is shown to be robust to the choice of statistical design criterion (median, mean, P90, maximum), and none of the three framework decisions is reversed under $\pm 30\%$ parameter variations. By replacing the subjective weightings of multi-criteria methods with observable economic criteria, the framework provides a replicable and auditable design protocol. Full article

Under extreme high-temperature conditions, heat accumulation in rooftop photovoltaic (PV) arrays can substantially reduce PV conversion efficiency and output power. To improve the thermal and power-generation performance of rooftop PV systems, this study developed a computational fluid dynamics (CFD) model for a rooftop PV array and validated it using field measurements from Chongqing, China. The relative root mean square errors (rRMSEs) between simulated and measured backsheet temperatures at the three measurement points were 6.31%, 7.52%, and 8.45%, respectively, indicating acceptable model accuracy. The effects of mounting height, tilt angle, and front-to-rear row spacing on PV backsheet temperature, conversion efficiency, and output power were then investigated. A central composite design (CCD) within response surface methodology (RSM) was used to establish regression models linking the design variables to the objective functions. Finally, an NSGA-III-based multi-objective optimization framework combined with TOPSIS was used to identify the optimal configuration. For the rooftop PV array studied under extreme summer conditions in Chongqing, the TOPSIS-selected compromise solution corresponded to a mounting height of 0.90 m, a tilt angle of 15.63°, and a front-to-rear row spacing of 2.96 m. Compared with the original configuration, the optimized passive installation geometry reduced the peak PV backsheet temperature by 2.3 °C without active cooling, water consumption, or additional energy input. Under the same meteorological and irradiance conditions, this temperature reduction increased conversion efficiency by 0.5% and output power by 0.4%. Detailed inter-row short-wave shading and electrical mismatch were not explicitly modeled. Therefore, the row-spacing effect mainly reflects changes in ventilation and module temperature under the same irradiance input. The proposed framework provides a practical reference for installing and optimizing rooftop PV arrays in hot-climate regions. Full article

Interior permanent magnet synchronous motors have high torque density and a high salient pole effect, combine low-speed high torque with constant-power wide speed regulation, and are increasingly favored by more and more car companies and widely used in electric vehicles. With the development of interior permanent magnet synchronous motors for electric vehicle towards high speed and large capacity, the eddy current loss generated inside the permanent magnet increases rapidly when the magnetic field alternates. Simulation results show that the excessive eddy current loss can raise the permanent magnet temperature of the I2V-type rotor up to 112 °C under rated operating conditions. Such a high temperature far exceeds the stable working temperature range of conventional NdFeB materials and greatly increases the risk of irreversible demagnetization. NdFeB permanent magnet materials have high electrical conductivity but weak heat-resistant capacity, so the temperature rise of permanent magnet is more serious, and even irreversible demagnetization occurs, which is fatal for the safe operation of motors. Therefore, it is necessary to analyze and study the eddy current loss of permanent magnets, explore methods to reduce magnet loss, and design reasonable and efficient cooling systems. Firstly, this paper selects three different rotor topologies as research objects, establishes two-dimensional parameterized finite element analysis models, and analyzes and compares magnet loss and the hysteresis loss, eddy loss, and copper loss of the stator. Secondly, to solve the problem that the I2V-type rotor generates higher magnet loss than the other two structures under all working conditions, magnetic isolation holes are arranged on each rotor pole to optimize the internal magnetic circuit. Simulation analysis results show that this method can effectively reduce magnet loss and stator hysteresis losses. Finally, the temperature of the shaft, magnet and stator winding are studied; aiming at characteristics of high torque density with small size, large torque, and high magnet temperature, a cooling method combining housing cooling and shaft cooling is proposed. Simulation results indicate that the new cooling method can greatly suppress the magnet temperature rise, which reduces the maximum permanent magnet temperature from 112 °C to 80 °C under rated operating conditions and can further improve the torque density and operating reliability of interior permanent magnet synchronous motors. This provides a feasible design reference for high-reliability vehicle interior permanent magnet synchronous motors. Full article

Traditional fossil-fuel-based industries, including electric power generation and petrochemical production, are plagued by their inherent carbon dioxide emissions. Current efforts to minimize CO₂ discharge tend to focus on costly capture and sequestration rather than utilizing the process energy and products to produce profitable chemicals. This work describes a concept and design strategy for a comprehensive facility to convert natural gas and air to carbon monoxide, which could be further used to synthesize desirable compounds and fuels. The basic components of the process are an improved supercritical carbon dioxide (sCO₂) electric power plant for driving solid-oxide electrolysis cell (SOEC) reduction in CO₂ to CO. This study examines opportunities for integrating a sCO₂ process with an associated air separation unit (ASU) and an SOEC, including material recycling and heat integration. A Life Cycle Assessment (LCA) is expected to show more positive results than for separate processes. Preliminary economic evaluations provide a profitable process and product route for the combined process, suggesting that carbon emission reduction could be good business. Full article

Ni–Co γ/γ′-strengthened superalloys are key structural materials for modern energy and flow turbomachinery systems due to their exceptional high-temperature strength, creep resistance, as well as hydrogen and corrosion stability. However, operation in gaseous hydrogen environments typical of hydrogen-cooled generators, cooled gas-turbine blades, and emerging hydrogen-energy technologies can significantly affect their microstructural stability and fracture behavior. This study presents a comprehensive multiscale review of hydrogen-induced nanoscale degradation and crack initiation mechanisms in Ni–Co superalloys produced by wrought, powder metallurgy, and additive manufacturing routes. Transmission electron microscopy combined with quantitative morphometric analysis was employed to characterize the size, morphology, and spatial distribution of γ′ precipitates, revealing a dense population of coherent particles predominantly in the 40–120 nm range, governed by a log-normal distribution. Correlations between precipitate size, aspect ratio, and circularity indicate the onset of partial loss of coherency and coarsening for particles exceeding ~80 nm, creating favorable sites for hydrogen localization. The presence of TCP phases (η, σ, μ, Laves) and carbides at grain boundaries and within grains was shown to enhance microstructural heterogeneity and act as effective hydrogen traps, promoting interfacial decohesion and microcrack initiation. To support microstructural interpretation, convolutional neural network analysis with Grad-CAM visualization was applied to SEM images, enabling the identification of the structural regions most sensitive to hydrogen-assisted damage, particularly γ/γ′ interfaces and defect clusters. The results demonstrate that hydrogen-induced degradation in Ni–Co superalloys is governed by the coupled interactions among microstructure, hydrogen distribution, and local stress state. The findings provide a physically grounded basis for optimizing alloy chemistry, heat treatment, and additive manufacturing parameters, as well as for developing AI-assisted predictive models for the durability of critical components in hydrogen-energy and high-temperature power-generation systems to increase hydrogen safety. Full article

To address the possible limitations of conventional energy-efficiency indicators in terms of restricted evaluation boundaries and the insufficient characterization of energy coupling and reuse processes in complex energy-utilization systems, this paper proposes a modified energy-efficiency-evaluation method based on unutilized-energy decomposition. First, a unified representation of basic energy-utilization units is established based on the concept of minimum functional units. Typical structural mappings, including series, feedback, delay, and auxiliary structures, are then introduced to describe complex energy interaction paths in a unified manner. Second, unutilized energy is distinguished from actual loss and is further decomposed into inevitable unutilized energy and potentially recoverable unutilized energy, so as to reveal the internal differences and reuse potential of energy that does not form useful output. On this basis, modified energy-efficiency indicators for downstream-utilization, feedback-recirculation, and auxiliary structures are developed, together with supporting indicators such as recovery efficiency gain, auxiliary efficiency gain, and the actual loss rate, thereby forming a comprehensive evaluation framework for complex energy-utilization systems. Finally, a gas engine combined heat and power (CHP) system is used as a case study. The results show that conventional power-generation efficiency cannot distinguish system performance differences under different heat-utilization conditions, whereas the proposed modified energy efficiency and actual loss rate can effectively reveal the effects of waste-heat utilization, thermal-load matching, and thermal-storage shifting on overall system performance. This study can provide a reference for the comprehensive energy-efficiency evaluation of combined heat and power systems, as well as other chain-type and multi-energy coupled energy-utilization systems. Full article

The high energy demand driven by industrial development has transformed the power system from a single energy source to multiple energy systems (MESs). These systems, which involve thermal generators, combined heat-and-power (CHP) units, electrolyzers, fuel cells, etc., with realistic forecast uncertainty, are very operationally challenged. This paper proposes a Distributionally Robust Optimization (DRO) based on a Wasserstein-metric ambiguity set, which simultaneously optimizes the annual maintenance schedules and short-term operational dispatch across MESs. The ambiguity set is constructed using joint samples of forecast errors for the three carriers’ demand, allowing for a data-driven worst-case distribution approach that mitigates the excessive conservatism typically associated with conventional robust optimization (CRO). The penalties are explicitly enforced for load and renewable energy curtailments across each of the MESs with source-specific value-of-lost-load coefficients. The Wasserstein radius is improved by sensitivity analysis, obtaining a θ value of 0.20 as the cost reduction radius for a 40% RES penetration. Five RES penetration levels are implemented here on the IEEE 39-bus New England network, with CHP, electrolyzer, fuel cell, thermal storage, and hydrogen storage. The DRO reduces the total annual system cost by 56% compared to CRO, while reducing the unbalanced energy. Full article