Search Results (55,544)

This study focuses on optimizing hybrid photovoltaic (PV)–wind–lithium-ion battery systems, aiming to balance lifecycle cost (LCC) minimization and power supply reliability (measured by loss of power supply probability, LPSP). A multi-algorithm optimization framework was constructed to compare the performance of Particle Swarm Optimization (PSO), Moth–Flame Optimization (MFO), Grey Wolf Optimization (GWO), and Hybrid Optimizer of PSO and GWO Merits (PSO-GWO) for off-grid power supply; additionally, a PSO-GWO was proposed to address multi-objective demands of economy, environment, and reliability for remote grid-connected power supply. Combined with system architecture design, energy management strategies, and component availability analysis, the PSO-GWO reduced 25-year LCC to $2.024 million, LPSP to 0.05, and cost of energy (COE) to $0.06254/kWh. PSO-GWO further optimized carbon emissions (CEs, operational carbon emissions only) to 2750 tons/year (14.1% lower than PSO) while maintaining LCC at $1.981 million and LPSP at 0.01. Thirty independent runs of each algorithm were conducted for statistical validation, and sensitivity analysis verified the algorithms’ robustness to PV efficiency, battery cost, wind speed fluctuations, battery price volatility, and carbon tax changes. The study also expanded the analysis to multiple climatic scenarios, providing an economical, reliable, low-carbon solution with strong generalizability. Full article

Hydropower remains one of the primary sources of power generation. It can be operated as either a base-load or peak-load plant due to its rapid, easy start-up and stop-down capability. However, power plants, old or new, need to be operated and maintained optimally to meet energy demand and maximise economic returns. While the older plants without digital controls such as the Supervisory Control and Data Acquisition (SCADA) system are unable to leverage the evolving technology including big data and Artificial Intelligence (AI), the newer plants or plants that already have some form of data acquisition system have the advantage of leveraging the newer platforms for efficient operation, monitoring and fault diagnosis. Thus, an Artificial Neural Network (ANN), a machine learning (ML) algorithm, was chosen for this case study to predict the generator’s operational stator temperature by selecting six parameters that could potentially affect it. Real data from the 336 MW Chhukha Hydropower Plant (CHP) in Bhutan were used to train the ANN. The prediction of temperature using an ANN in MATLAB^® yielded an R² (correlation coefficient) of 96.8%, which is impressive but can be further improved through various optimisation and tuning methods with increased data volume and complexity. The performance of ANN prediction was validated against other regression models, and the ANN was found to outperform them. This demonstrated its capability to predict and detect generator temperature faults before failures, thereby enhancing hydropower operation and maintenance (O&M) efficiency. The model’s interpretation was also done through Shapley Additive ExPlanations (SHAP). Full article

Water quality plays a central role in determining the environmental performance of pond-based tropical aquaculture systems. This study aimed to evaluate the relative environmental performance of different tropical pond-based aquaculture systems by identifying multivariate water quality patterns that allow their discrimination and comparison under commercial production conditions. Four pond-based production systems were evaluated: an aquaponic system (APS), a recirculating aquaculture system (RAS), a conventional earthen pond system (CEP), and an integrated rice–chame system (RCS). Fourteen physicochemical water quality variables were monitored throughout the production cycle under real commercial conditions using a comparative observational design. Multivariate discriminant analysis was applied to identify the variables with the highest discriminatory power and evaluate the ability of water quality patterns to correctly classify observations among production systems. The results revealed a clear multivariate separation between technologically intensive systems (APS and RAS) and less intensive and integrated systems (CEP and RCS), reflecting distinct water quality structures and environmental functioning. Variables associated with mineralization and nutrient dynamics, including electrical conductivity, dissolved solids, turbidity, phosphates, chlorides, dissolved oxygen, nitrites, and temperature, contributed most strongly to system discrimination. The discriminant functions achieved a high overall correct classification rate, demonstrating the robustness of the multivariate approach. These findings support the use of water quality variables as consistent environmental signatures for distinguishing tropical pond-based aquaculture systems, providing an operational framework for assessing their relative environmental performance. Discriminant analysis emerges as a valuable tool for system characterization and comparative evaluation, supporting environmentally informed management and optimization of chame aquaculture under tropical conditions. Although water quality represents a robust integrative indicator, it captures only one dimension of environmental performance, and additional factors such as production efficiency, energy use, and effluent characterization should be incorporated in future studies to achieve a comprehensive sustainability assessment. Full article

Hot-air drying is widely adopted for herbs because it is robust and easy to control, yet it is often energy-intensive and may operate far from optimal conditions when industrial dryers rely on fixed airflow paths and large air recirculation rates. This work investigates a conventional basket-type, adiabatic hot-air dryer through an instrumented 30 h drying campaign and a psychrometric energy analysis. The hot-air drier is designed to reduce the relative humidity of herbs from the environmental value (highly variable as a function of the species, the weather conditions, and, mostly, the seasonality) to 20%. Temperature and relative humidity were measured at four positions to characterize the shelf-by-shelf drying sequence and to identify process phases. A mass balance indicated that approximately 3.8 t of water was removed during the trial. Based on the measured thermodynamic states of the moist air and estimated airflow rates (35,000–53,000 m³/h), the baseline configuration was analyzed and an upgrade strategy was proposed to improve dehumidification and overall efficiency while preserving the conventional hot-air-drying concept. The alternative solution integrates a refrigeration-based dehumidification loop (heat pump) to decouple moisture removal from sensible heating; three plant layouts and seasonal boundary conditions (summer/winter) were simulated. For the most favorable configurations, the specific final–primary energy demand and the associated CO₂-equivalent emissions were reduced by about 70–85% compared with the baseline, depending on the airflow rate and recirculation strategy. The results highlight practical retrofit options for existing herb dryers and provide a transparent framework for translating measured psychrometric states into energy and emission indicators. The results, achieved and discussed in this study, were used to optimize the utilization of an already existing and operative hot-air dryer. Based on the proposed working configuration, the dryer now allows achieving the fixed target for herb mixtures of the previous configuration and, at the same time, reducing the energy consumption and associated equivalent CO₂ emitted, as well as achieving process completion in less time. Full article

The objective of this study was to evaluate the impact of seasonal changes in the chemical and structural composition of giant miscanthus (Miscanthus × giganteus) biomass on the performance, kinetics, and efficiency of anaerobic digestion (AD), as well as on the overall energy and techno-economic balance of the conversion chain. The AD performance was assessed using batch biochemical methane potential (BMP) assays conducted for eight harvest dates (June–January). Comprehensive characterization included fundamental physicochemical properties of the biomass, lignocellulosic fraction composition, AD kinetics, and methane production yield. A statistically significant (p < 0.05) increase in structural fiber fractions was observed with advancing plant maturity, accompanied by a progressive decline in specific methane yield from 281 ± 32 mL CH₄/g VS in June to 170 ± 11–172 ± 13 mL CH₄/g VS in winter harvests. Despite a relatively stable theoretical biochemical methane potential (TBMP) ranging from 425 to 443 mL CH₄/g VS, the conversion efficiency (BMP/TBMP) decreased from approximately 66% to below 40%, indicating increasing structural and kinetic limitations to substrate biodegradability. Kinetic parameters deteriorated systematically in late harvests, as reflected by a reduction in the first-order rate constant k_CH₄ from 0.115 to approximately 0.072 1/d and an extension of the lag phase λ from 2.19 to over 4 days. Regression analysis revealed strong negative correlations between lignocellulosic complex content and both BMP and k_CH₄, whereas the C/N ratio exhibited a positive association with process performance under the experimental conditions applied. The highest methane production per hectare (3904 ± 720 m³CH₄/ha) and the most favorable economic outcome (1979 ± 465 EUR/ha) were achieved for the September harvest. The results demonstrate that harvest timing constitutes a critical optimization parameter in lignocellulosic biogas systems, governing not only methane yield and process kinetics but also the overall energy output and economic viability of the bioenergy production chain. Full article

This paper develops a structured analytical framework and a robust numerical methodology for the spectral stability of time-varying bilateral quaternion differential equations of the form $\dot{q}=A\left(t\right)q+qB\left(t\right)$. By systematically extending classical real matrix theory to non-commutative dynamical systems via exact isometric real representations, this study utilizes the Kronecker product of real adjoint matrices to rigorously elucidate the underlying tensor structure of the bilateral evolution operator. This tensor-based reformulation proves that the Floquet multipliers of the bilaterally coupled system can be strictly decoupled into the product of the spectra corresponding to the left and right unilateral subsystems. Second, a “Scalar-Vector Stability Separation Principle” based on logarithmic norms is proposed, demonstrating that the transient energy evolution of the system is governed exclusively by the Hermitian real parts of the coefficient matrices, remaining entirely independent of the anti-Hermitian imaginary parts (rotation terms). Furthermore, for constant-coefficient and slowly varying systems, the Riesz projection from holomorphic functional calculus is introduced to establish algebraic criteria for exponential dichotomies, thereby revealing a cubic scaling law that relates the robustness threshold to the spectral gap (${\epsilon }_0\propto {\beta }^3$). Numerically, a Quaternion Chebyshev Spectral Collocation Method (Q-CSCM) is embedded within this exact vectorization framework to ensure that the algebraic symmetries of the bilateral system are strictly preserved through the isomorphic mapping. By explicitly constructing the fully discrete Kronecker product matrix via the exact real vectorization isomorphism, discrete energy estimates are utilized to rigorously prove that the numerical scheme successfully inherits the intrinsic spectral accuracy of the Chebyshev approximation. Comprehensive numerical experiments demonstrate that, within the low-dimensional regime, this methodology exhibits substantial temporal approximation efficiency advantages and superior numerical robustness compared to an alternative Legendre spectral baseline, as well as traditional explicit and state-of-the-art implicit symplectic Runge–Kutta methods, particularly when solving stiff and critically stable problems such as nonlinear Riccati oscillators. Full article

Activated sludge treatment is plagued by high secondary pollution risks, and ship antifouling paint particles (APPs) as hazardous heavy metal-rich solid wastes generated from hull derusting wastewater, pose severe environmental threats and intractable disposal dilemmas. This study developed a novel pilot-scale activated sludge reduction process coupling APPs-derived carbon-based catalysts with ultrasound-Fe²⁺/peroxydisulfate (PDS) advanced oxidation. Columnar catalysts were fabricated via direct carbonization-molding using waste APPs from an 82,000 deadweight bulk carrier were used as the sole raw material to prepare columnar catalysts via direct carbonization-molding; single-factor and orthogonal experiments optimized process parameters, Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS) and X-ray Photoelectron Spectroscopy (XPS) characterized catalyst and sludge properties, free radical quenching experiments elucidated reaction mechanisms and a 90-day continuous pilot run assessed catalytic stability. The process achieved a 43.5% sludge removal rate under optimal conditions, accompanied by 100% toluene and 92.3% phenolic compound degradation, as well as efficient total phosphorus (TP) and total nitrogen (TN) removal. Mechanistic studies via characterization and quenching experiments confirmed the catalyst enhanced PDS activation through free/non-free radical synergy and accelerated Fe²⁺/Fe³⁺ redox cycling. A 90-day continuous pilot operation demonstrated excellent long-term catalytic stability, with sludge removal rate remaining above 38%. This “waste treating waste” technology realizes high-value APPs resource utilization, provides a low-carbon sludge disposal pathway, and offers a scalable solution for collaborative pollution control in the wastewater treatment and shipping industries. Full article

Membrane technology demonstrates broad prospects in the field of methane capture and purification due to its high efficiency and low energy consumption characteristics. This paper systematically reviews the research progress in membrane technology for methane separation in recent years, focusing on the design and optimization of membrane material systems, in-depth analysis of mass transfer mechanisms, and practical applications in areas such as biogas upgrading and natural gas decarbonization. Researchers have significantly enhanced membrane separation performance for CO₂/CH₄, CH₄/N₂, and other systems by developing novel material systems such as polymer membranes, inorganic membranes, and mixed matrix membranes (MMMs), combined with strategies like pore structure regulation, interface optimization, and functionalization. Although membrane technology has shown good economic feasibility and application potential in some scenarios, challenges such as long-term material stability, anti-plasticization capability, and large-scale manufacturing remain the main current obstacles. Future research should further focus on the development of novel membrane materials, process integration optimization, and intelligent process control to promote a greater role for membrane technology in the efficient utilization of methane resources and energy structure transformation. Full article

To address the challenges of coordinated optimization in building integrated energy systems (IES) under the dual-carbon targets—characterized by strong multi-energy coupling, significant uncertainty in renewable generation, and stringent safety constraints—a novel safe deep reinforcement learning algorithm, Safe-DDPG, is proposed. Traditional deep reinforcement learning methods often suffer from high constraint-violation risk and limited policy reliability due to coupled objectives in building IES optimization. To overcome these limitations, a dual-channel critic architecture is designed to independently evaluate and decouple economic and safety objectives. In addition, a dynamic safety–penalty mechanism based on logarithmic barrier functions is introduced, together with an adaptive exploration strategy, enabling dynamic balancing between economic cost and constraint satisfaction according to system states during training. Experimental results demonstrate that, compared with mainstream algorithms, Safe-DDPG achieves substantial improvements across multiple key performance indicators: safety violations are reduced by up to 96.7%, average daily operating costs decrease by 18.5%, and cumulative rewards increase by more than 30%. Ablation studies further confirm the effectiveness and necessity of each core component. Two DRL methods from reference papers are reproduced, and their performance is compared with the proposed method in the existing experimental results, showing that the proposed method has significant advantages in reward value and economic cost. This work provides a safe, reliable, and efficient reinforcement-learning-based approach for optimization and scheduling of building energy systems under complex operational constraints. Full article

Ultrasound-assisted vacuum drying (USVD) has emerged as an increasingly studied food drying approach to overcome mass and energy transfer limitations associated with conventional vacuum drying. This study aims to clarify the behavior of the USVD process by synthesizing findings from product- and condition-specific studies. This review critically examines 38 core USVD studies published between 2014 and 2025, complemented by related comparative research, to assess the effects of USVD on drying efficiency, product quality, and key process parameters across diverse food matrices. The reviewed literature consistently demonstrates that USVD enhances drying kinetics, with increases in drying rate reaching approximately 94%, depending on product characteristics and operating conditions. Due to shorter drying times, USVD also provides potential economic advantages through reduced energy costs, equipment utilization and overall process costs. Furthermore, research has found that USVD retains quality attributes including color and bioactivity of a wide range of foods. USVD-dried products commonly exhibit improved microstructural integrity and enhanced porosity, which imparts superior rehydration. In conclusion, this study highlights the strong potential of USVD to enhance drying efficiency while preserving product quality. Full article

To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrated solution combining a neighborhood feature aggregation-based graph attention network (NFA-GAT) and multi-agent deep deterministic policy gradient (MADDPG). First, the D-RTPD problem is modeled as a decentralized partially observable Markov decision process (Dec-POMDP), which effectively captures the stochastic game characteristics of multi-regional agents and the partial observability of grid states. Second, the NFA-GAT is designed to enhance agents’ perception of grid operating states: by introducing a spatial discount factor, it realizes rational aggregation of multi-order neighborhood information while modeling the attenuation of electrical quantity influence with topological distance. Third, a prior-guided mechanism is integrated into the MADDPG framework to eliminate constraint-violating actions by setting their actor logits to negative infinity, improving training efficiency and strategy reliability. Simulation validations on the IEEE 118-bus test system (75.2% RES installed capacity ratio) show that the proposed method achieves efficient training convergence. Compared with the multi-layer perceptron (MLP) structure, it attains higher cumulative reward values and scenario win rates. When compared with traditional model-driven (ADMM) and data-driven (Q-MIX) methods, the proposed method balances solution efficiency, operational safety (98.7% maximum line load rate, zero power flow violation rate), and economic performance ($12,845 daily dispatch cost), providing a reliable technical support for D-RTPD under high-proportion RES integration. Full article

This article presents a performance assessment of an electrical power and cooling system powered by a parabolic dish collector and using refrigerants with low global warming potential. The study was conducted using energy and mass balances for each component and system. The simulation includes various parameters, such as solar radiation, the focal temperature of the solar collector, the ambient temperature, the power cycle pressure ratio, and the chiller’s evaporation temperature. The results show that the efficiency of the organic Rankine cycle with the refrigerant R1233zd(E) is similar to that of the refrigerants R123 and R245fa and is up to 11 and 50 times lower than with R290 and R744, respectively. The solar absorption chiller using the refrigerant R717 can achieve cooling with a supply temperature up to 5 °C lower than that of R718. The dynamic simulation results show that the energy efficiency of the proposed solar-powered generator–chiller is 14% higher than that of a standard solar-powered absorption chiller. Furthermore, the same solar-powered generator–chiller reduces the primary energy required by a conventional system by 60% (PESr = 0.60). The presented results may be useful for the design of sustainable generator–chillers for rural areas or for autonomous housing in tropical climates. Full article

Extended-solvent steam-assisted gravity drainage (ES-SAGD) has emerged as a promising advancement over conventional SAGD for improving the efficiency and sustainability of in situ heavy oil and bitumen recovery. By co-injecting light hydrocarbon or alternative solvents with steam, ES-SAGD integrates thermal and compositional mechanisms to reduce viscosity, accelerate chamber development, and reduce steam–oil ratios. This review synthesizes the current state of knowledge on ES-SAGD, encompassing fundamental transport mechanisms, solvent selection and phase behavior, mass transfer dynamics, laboratory and physical modeling studies, numerical simulation approaches, and field-scale operational experiences. Experimental evidence consistently demonstrates substantial mobility enhancement through solvent-induced dilution, while compositional thermal simulations highlight an improved sweep efficiency and reduced energy intensity relative to steam-only processes. Field pilots further validate accelerated early-time production and significant steam savings, though challenges related to solvent retention, asphaltene stability, and reservoir heterogeneity persist. Key research gaps are identified in solvent transport prediction, formation damage risk, long-term solvent recovery, and integrated economic–environmental optimization. Overall, ES-SAGD offers a viable pathway toward lower-emission, higher-efficiency bitumen production, provided that solvent chemistry, reservoir complexity, and operational controls are carefully managed through continued research and targeted field deployment. Full article

This study develops a unified multiscale–machine learning framework to interpret and predict thermo-mechanical wear regime transitions in MWCNT- and nanoclay-reinforced bio-based epoxy composites. A physics-informed master wear formulation integrating real contact mechanics, geometry-dependent shear transfer, interfacial adhesion energetics, and fracture-controlled matrix detachment was combined with interpretable machine learning analytics on a unified tribological dataset. In the CNT system, increasing loading from 0.1 to 0.4 wt.% enhanced interfacial adhesion energy density from 0.00813 to 0.01906 J/m², resulting in a monotonic reduction in the wear rate from 0.00918 to 0.00613 mm³/N·m (~33% reduction). In contrast, nanoclay exhibited an optimum behavior, with a minimum wear at 0.25 wt.% (0.000093 mm³/N·m; 7.9% reduction vs. neat clay baseline), followed by deterioration at a higher loading due to dispersion loss. The unified probabilistic regime classification of low-wear conditions (k < 0.007 mm³/N·m) achieved an ROC − AUC = 0.9256 and balanced accuracy = 94.3%, with thermo-mechanical severity identified as the dominant regime-switching driver. Reinforcement identity significantly modulated regime stability, confirming distinct shear transfer (Carbon Nano Tubes(CNT)) and confinement/tribofilm (clay) mechanisms within a common mathematical framework. By enabling the durability-oriented design of bio-based tribological systems and extending component service life through predictive stability mapping, this work contributes to resource-efficient materials engineering and reduced lifecycle waste, supporting Sustainable Development Goals SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). Full article

Recent space missions demand higher pointing accuracy, smoother attitude transitions and lower energy consumption than those typically achievable with conventional control approaches. This motivates the exploration of intelligent and nonlinear control methods. The FuzzyBuzz experiment investigates the application of fuzzy logic for spacecraft attitude control using NASA’s Astrobee robotic system aboard the International Space Station. Unlike traditional control methods, fuzzy logic introduces a rule-based approach capable of handling uncertainties and nonlinearities inherent in space environments, making it particularly suited for autonomous operations in microgravity. The objective of FuzzyBuzz is to evaluate the effectiveness of fuzzy controllers compared to traditional linear ones, such as Proportional–Integral–Derivative (PID) and $H_{\infty }$ controllers. In addition, a comparison with a nonlinear controller based on a Model Predictive Control (MPC) strategy is considered. The controllers will be tested through predefined attitude maneuvers, evaluating precision, energy efficiency, and real-time adaptability. This work presents the design of the FuzzyBuzz experiment, including the software architecture, simulation environment, experiment protocol, and the development of a fuzzy logic-based attitude control system for Astrobee robots. The proposed fuzzy controller and a PID controller are optimized using a Multi-Objective Particle Swarm Optimization (MOPSO) method, providing a range of operational points with different trade-offs between two metrics, related to convergence time and energy consumption. Results show that the PID controller is better suited for scenarios demanding low convergence times, whereas the fuzzy controller provides smoother responses, reduced steady-state error, and maintains convergence under significant parametric uncertainties. Results from $H_{\infty }$ and MPC controllers will be reported once the in-orbit experiment is performed. Full article