Search Results (3,193)

This paper presents a conceptual and thermodynamic assessment of an innovative cogeneration system based on the aluminium–water reaction, designed to simultaneously produce hydrogen and electricity. The proposed layout integrates a liquid aluminium combustion chamber with a dual-stage heat recovery section and a steam turbine cycle, enabling the valorisation of industrial aluminium scraps within a circular-economy framework. A steady-state thermodynamic model was developed in Aspen Plus to evaluate system performance under different operating conditions, with a sensitivity analysis on key parameters such as the aluminium-to-water ratio (2.4–4), combustion efficiency, and steam generation cycle parameters. The system performance is investigated in terms of useful output (i.e., hydrogen and electricity production), including a simplified economic evaluation for the assessment of sustainability. Results indicate that, for equivalence ratios ensuring acceptable peak temperatures (≤1700 °C), the system can deliver 2–3 MW of electric power per kg/s of aluminium and achieve cogeneration efficiencies up to 83–87%, assuming a high conversion rate of water into hydrogen (roughly 0.106 kg of produced H₂ per kg of inlet Al, if 95% of mole conversion is considered). The minimum break-even levelized cost of hydrogen is estimated to be 15.7 EUR/kg under current economic conditions. Full article

The food industry generates large volumes of nutrient-rich by-products that remain underutilised despite their considerable biochemical potential. These materials originate predominantly from the fruit and vegetable, dairy, meat, and fish and seafood sectors and represent a substantial opportunity for sustainable valorisation. Fermentation has emerged as a powerful platform for converting such by-products into high-value ingredients, including bioactive compounds, functional metabolites, enzymes, antimicrobials, and nutritionally enriched fractions. This review synthesises recent advances in microbial fermentation strategies—spanning lactic acid bacteria, filamentous fungi, yeasts, and mixed microbial consortia—and highlights their capacity to enhance the bioavailability, stability, and functionality of recovered compounds across diverse substrate streams. Key technological enablers, including substrate pre-treatments, precision fermentation, omics-guided strain selection and improvement, and bioprocess optimisation, are examined within the broader framework of circular bioeconomy integration. Despite significant scientific progress, major challenges remain, particularly related to substrate heterogeneity, process scalability, regulatory alignment, safety assessment, and consumer acceptance. The review identifies critical research gaps and future directions, emphasising the need for standardised analytical frameworks, harmonised compositional databases, AI-driven fermentation control, integrated biorefinery concepts, and pilot-scale validation. Overall, the evidence indicates that integrated fermentation-based approaches—especially those combining complementary by-product streams, tailored microbial consortia, and system-level process integration—represent the most promising pathway toward the scalable, sustainable, and economically viable valorisation of food industry by-products. Full article

Achieving a green transition in the energy structure and reducing reliance on traditional fossil fuels has become a global imperative for addressing climate change and promoting sustainable development. The search for clean energy alternatives to traditional fossil fuels has emerged as a critical challenge in the energy and power sector. Ammonia (NH₃) shows great potential as a zero-carbon fuel in the energy sector, but issues such as its low flame propagation speed, high ignition energy requirements, and elevated NO_x emissions limit its widespread industrial application. To address these issues and enhance ammonia combustion, plasma-assisted combustion technology has gained widespread attention in recent years as an effective solution. The plasma-assisted technology enhances combustion stability and efficiency of ammonia, and effectively suppresses NO_x emissions. Additionally, the high-energy electrons and intense chemical reactions in plasma help to decompose and crack ammonia fuel, increase flame propagation speed, and thus improve ammonia combustion performance. This paper provides a comprehensive review of the latest research advancements in plasma-assisted technology in ammonia combustion. It covers the fundamental principles of plasma generation, the mechanisms of combustion enhancement, industrial application status, and development trends. The aim is to assess the potential of plasma-assisted combustion technology in achieving efficient, stable, and low-carbon ammonia combustion, and to explore its future prospects for industrial application. Full article

The growing miniaturization of electronic systems and the expansion of sustainable, autonomous IoT technologies emphasize the need for efficient, ultra-low-power energy harvesting devices. This study evaluates fifteen devices from five industry leaders for use in small-scale autonomous seed germination systems. Its novelty lies in applying a competitive profile matrix within a flexible multicriteria evaluation framework based on the simple additive weighting (SAW) method that uses a comprehensive set of competitive technology factors (CTFs). The results demonstrate that a transparent and structured methodology can generate prioritized lists of suitable energy harvesters while accounting for technical, economic, and environmental trade-offs. The study also shows that device rankings depend on the scope and objectives of the project. If these change, then the CTF selection, classification, and weighting adjust accordingly. Therefore, the relevance of this study lies in the adaptability, replicability, and audibility of the proposed framework, which supports the selection of informed technology for autonomous, IoT-based germination systems and other technological projects. Full article

In recent years, intelligent control of complex thermodynamic systems has gained increasing attention due to global demands for higher energy efficiency and reduced environmental impact in industrial settings. This study explores the integration of quantum control methodologies-grounded in established principles of quantum mechanics—into the automation of thermal processes in power plant operations. Specifically, it investigates a hybrid quantum-fuzzy control system for managing steam heating processes, a critical subsystem in thermal power generation. Unlike conventional control strategies that often struggle with nonlinearity, time delays, and parameter uncertainty, the proposed method incorporates quantum-inspired optimization algorithms to enhance adaptability and robustness. The quantum component, based on recognized models of coherent control and quantum interference, is utilized to refine the inference mechanisms within the fuzzy logic framework, allowing more precise handling of state transitions in multivariable environments. A simulation model was constructed using validated physical parameters of a pilot-scale steam heating unit, and the methodology was tested against baseline scenarios with conventional proportional-integral-derivative (PID) control. Experimental protocols and statistical analysis confirmed measurable improvements: up to 25% reduction in fuel usage under specific operational conditions, with an average of 1 to 2% improvement in energy efficiency. The results suggest that quantum-enhanced intelligent control offers a feasible pathway for bridging the gap between quantum theoretical models and macroscopic thermal systems, contributing to the development of more energy-resilient industrial automation solutions. Full article

The expansion of renewables in modern power systems and the coordinated development of upstream and downstream industrial chains are promoting a shift on the utility side from traditional settlement by energy toward operation driven by data and models. Industrial electricity consumption data exhibit pronounced multi-scale temporal structures and sectoral heterogeneity, which makes unified long-term load and generation forecasting while maintaining accuracy, interpretability, and scalability a challenge. From a modern system identification perspective, this paper proposes a System Identification in Adaptive Bayesian Framework (SIABF) for medium- and long-term industrial load forecasting based on daily freeze electricity time series. By combining daily aggregation of high-frequency data, frequency domain analysis, sparse identification, and long-term extrapolation, we first construct daily freeze series from 15 min measurements, and then we apply discrete Fourier transforms and a spectral complexity index to extract dominant periodic components and build an interpretable sinusoidal basis library. A sparse regression formulation with ${\ell }_1$ regularization is employed to select a compact set of key basis functions, yielding concise representations of sector and enterprise load profiles and naturally supporting multivariate and joint multi-sector modeling. Building on this structure, we implement a state-space-implicit physics-informed Bayesian forecasting model and evaluate it on real data from three representative sectors, namely, steel, photovoltaics, and chemical, using one year of 15 min measurements. Under a one-month-ahead evaluation using one year of 15 min measurements, the proposed framework attains a Mean Absolute Percentage Error (MAPE) of 4.5% for a representative PV-related customer case and achieves low single-digit MAPE for high-inertia sectors, often outperforming classical statistical models, sparse learning baselines, and deep learning architectures. These results should be interpreted as indicative given the limited time span and sample size, and broader multi-year, population-level validation is warranted. Full article

The fastest growth in electricity demand in the industrialized world will likely come from the broad adoption of artificial intelligence (AI)—accelerated by the rise of generative AI models such as OpenAI’s ChatGPT. The global “data center arms race” is driving up power demand and grid stress, which creates local and regional challenges because people in the area understand that the additional data center-related electricity demand is coming from faraway places, and they will have to support the additional infrastructure while not directly benefiting from it. So, there is an incentive for the data center operators to manage the fast and unpredictable power surges internally so that their loads appear like a constant baseload to the electricity grid. Such high-intensity and short-duration loads can be served by hybrid energy storage systems (HESSs) that combine multiple storage technologies operating across different timescales. This review presents an overview of energy storage technologies, their classifications, and recent performance data, with a focus on their applicability to AI-driven computing. Technical requirements of storage systems, such as fast response, long cycle life, low degradation under frequent micro-cycling, and high ramping capability—which are critical for sustainable and reliable data center operations—are discussed. Based on these requirements, this review identifies lithium titanate oxide (LTO) and lithium iron phosphate (LFP) batteries paired with supercapacitors, flywheels, or superconducting magnetic energy storage (SMES) as the most suitable HESS configurations for AI data centers. This review also proposes AI-specific evaluation criteria, defines key performance metrics, and provides semi-quantitative guidance on power–energy partitioning for HESSs in AI data centers. This review concludes by identifying key challenges, AI-specific research gaps, and future directions for integrating HESSs with on-site generation to optimally manage the high variability in the data center load and build sustainable, low-carbon, and intelligent AI data centers. Full article

Mixing processes in stirred tanks are widely applied across various industries, but still offer significant potential for optimization. A promising strategy is the use of double-stage impeller setups instead of conventional single impellers. While multi-impeller configurations are common in tall vessels, their benefits for standard tanks with a height-to-diameter ratio of 1 are largely unexplored. This study systematically investigates the flow fields of single, identical, and mixed double-stage configurations of a Rushton turbine, a pitched-blade turbine, and a retreat curve impeller. To ensure balanced power input in mixed configurations, a refined method for harmonizing specific power via impeller diameter adjustment is proposed. Stereo particle image velocimetry is applied to visualize flow fields, supported by refractive-index matching to enable measurements in a dished-bottom tank. The results reveal substantial flow deficiencies in single-impeller setups. In contrast, double-impeller setups generate novel and significantly improved velocity fields that offer clear advantages and demonstrate strong potential to enhance process efficiency across various mixing applications. These findings provide new experimental insights into the characteristics of dual impellers and form a valuable basis for the design and scale-up of stirred tanks, contributing to more efficient, reliable, and sustainable mixing processes. Full article

Power supply for traction substations (TSs) of AC railways has traditionally been provided by 110–220 kV overhead transmission lines (OHL). These OHLs can be damaged during strong winds and ice formation. Furthermore, these lines generate significant electromagnetic fields (EMFs), which adversely affect maintenance personnel, the public, and the environment. Mitigating the resulting damages requires the establishment of protection zones, necessitating significant land allocation. Enhancing the reliability of power supply to traction substations and reducing EMF levels can be achieved through the use of gas-insulated lines (GIL), whose application in the power industry of many countries is continuously increasing. The aim of the research presented in this article was to develop computer models for determining the EMF of a GIL supplying a group of traction substations, taking into account actual traction loads characterized by non-sinusoidal waveforms and asymmetry. To solve this problem, an approach implemented in the Fazonord AC-DC software package, based on the use of phase coordinates, was applied. This allowed for the correct accounting of the skin effect and proximity effect in the massive current-carrying parts of the GIL, as well as the influence of asymmetry and harmonic distortions. The simulation results showed that the use of GIL brings the voltage unbalance factors at the 110 kV busbars of the traction substations within the permissible range, with the maximum values of these coefficients not exceeding 2%. The results of the harmonic distortion assessment demonstrated a significant reduction in harmonic distortion factors in the 110 kV network for the GIL compared to the OHL. The performed electromagnetic field calculations confirmed that the GIL generates magnetic field strengths one order of magnitude lower than those of the OHL. The obtained results lead to the conclusion that the use of gas-insulated lines for powering traction substations is highly effective, ensuring increased reliability, improved power quality, and a reduced negative impact of EMF on personnel, the public, the environment, and electronic equipment. Full article

Microbial fuel cells (MFCs) have attracted increasing attention due to their potential applications in renewable energy generation, waste utilization, and biomass upgrading, offering a promising alternative to traditional fossil fuels. By directly converting carbon-rich wastes into electricity, MFCs provide a unique approach to simultaneously address energy demand and waste management challenges. This review systematically examines the effects of various carbon-rich substrates on MFC performance, including lignocellulosic biomasses, molasses, lipid waste, crude glycerol, and C1 compounds. These substrates, characterized by wide availability, low cost, and high carbon content, have demonstrated considerable potential for efficient bioelectricity generation and resource recovery. Particular emphasis is placed on the roles of microbial community regulation and genetic engineering strategies in enhancing substrate utilization efficiency and power output. Additionally, the application of carbon-rich wastes in electrode fabrication is discussed, highlighting their contributions to improved electrical conductivity, sustainability, and overall system performance. The integration of carbon-rich substrates into MFCs offers promising prospects for alleviating energy shortages, improving wastewater treatment efficiency, and reducing environmental pollution, thereby supporting the development of a circular bioeconomy. Despite existing challenges related to scalability, operational stability, and system cost, MFCs exhibit strong potential for large-scale implementation across diverse industrial sectors. Full article

Modern power transmission systems are required to meet increasingly stringent demands, including a wide range of transmission ratios, compact dimensions, high precision, energy efficiency, reliability, and thermal stability under dynamic operating conditions. Among the solutions that satisfy these requirements, cycloidal reducers are particularly prominent, with their application continuously expanding in industrial robotics, computer numerical control (CNC) machines, and military and transportation systems, as well as in the satellite industry. However, as with all mechanical power transmissions, friction in the contact zones of load-carrying elements in cycloidal reducers leads to power losses and an increase in operating temperature, which in turn results in a range of adverse effects. These undesirable phenomena strongly depend on lubrication conditions, namely on the type and properties of the applied lubricant. Although manufacturers’ catalogs provide general recommendations for lubricant selection, they do not address the fundamental tribological mechanisms in the most heavily loaded contact pairs. At the same time, the available scientific literature reveals a significant lack of systematic and experimentally validated studies examining the influence of lubricant type on the energetic and thermal performance of cycloidal reducers. To address this identified research gap, this study presents an analytical and experimental investigation of the effects of different lubricant types—primarily greases and mineral oils—on the thermal stability and efficiency of cycloidal reducers. The results demonstrate that grease lubrication provides lower total power losses and a more stable thermal operating regime compared to oil lubrication, while oil film thickness analyses indicate that the most unfavorable lubrication conditions occur in the contact between the eccentric bearing rollers and the outer raceway. These findings provide valuable guidelines for engineers involved in cycloidal reducer design and lubricant selection under specific operating conditions, as well as deeper insight into the lubricant behavior mechanisms within critical contact zones. Full article

PM_2.5 pollution has become one of the major environmental issues in Shandong Province in recent years. High concentrations of PM_2.5 not only reduce atmospheric visibility but also induce respiratory and cardiovascular diseases, and significantly increase health risks. Source apportionment of PM_2.5 is important for policy makers to determine control strategies. This study analyzed regional and sectoral PM_2.5 sources across 17 Shandong cities during the 2017–2018 winter heating season, which is selected because it is representative of severe air pollution with an average PM_2.5 of 65.75 μg/m³ and hourly peak exceeding 250 μg/m³. This air pollution episode aligned with key control policies, where seven major cities implemented steel capacity reduction and coal-to-gas/electric heating, as a baseline for evaluating emission reduction effectiveness. The particulate matter source apportionment technology in the Comprehensive Air Quality Model with extensions (CAMx) was applied to simulate the source contributions to PM_2.5 in 17 cities from different regions and sectors including industry, residence, transportation, and coal-burning power plants. The meteorological fields required for the CAMx model were generated using the Weather Research and Forecasting (WRF) model. The results showed that all cities besides Dezhou city in Shandong Province contributed PM_2.5 locally, varying from 39% to 53%. The emissions from Hebei province have a large impact on the PM_2.5 concentrations in Shandong Province. The non-local industrial and residential sources in Shandong Province accounted for the prominent proportion of local PM_2.5 in all cities. The contribution of non-local industrial sources to PM_2.5 in Heze City was up to 56.99%. As for Zibo City, the largest contribution of PM_2.5 was from non-local residential sources, around 56%. Additionally, the local industrial and residential sources in Jinan and Rizhao cities had relatively more contributions to the local PM_2.5 concentrations compared to the other cities in Shandong Province. Finally, the emission reduction effects were evaluated by applying different reduction ratios of local industrial and transportation sources, with decreases in PM_2.5 concentrations ranging from 0.2 to 26 µg/m³ in each city. Full article

This paper proposes a PI-Dual-STGCN fault diagnosis model based on a SHAP-LLM joint explanation framework to address issues such as the lack of transparency in the diagnostic process of deep learning models and the weak interpretability of diagnostic results. PI-Dual-STGCN enhances the interpretability of graph data by introducing physical constraints and constructs a dual-graph architecture based on physical topology graphs and signal similarity graphs. The experimental results show that the dual-graph complementary architecture enhances diagnostic accuracy to 99.22%. Second, a general-purpose SHAP-LLM explanation framework is designed: Explainable AI (XAI) technology is used to analyze the decision logic of the diagnostic model and generate visual explanations, establishing a hierarchical knowledge base that includes performance metrics, explanation reliability, and fault experience. Retrieval-Augmented Generation (RAG) technology is innovatively combined to integrate model performance and Shapley Additive Explanations (SHAP) reliability assessment through the main report prompt, while the sub-report prompt enables detailed fault analysis and repair decision generation. Finally, experiments demonstrate that this approach avoids the uncertainty of directly using large models for fault diagnosis: we delegate all fault diagnosis tasks and core explainability tasks to more mature deep learning algorithms and XAI technology and only leverage the powerful textual reasoning capabilities of large models to process pre-quantified, fact-based textual information (e.g., model performance metrics, SHAP explanation results). This method enhances diagnostic transparency through XAI-generated visual and quantitative explanations of model decision logic while reducing the risk of large model hallucinations by restricting large models to reasoning over grounded, fact-based textual content rather than direct fault diagnosis, providing verifiable intelligent decision support for industrial fault diagnosis. Full article

This study proposes a neural-augmented Libor Market Model (LMM) for swaption surface calibration that enhances expressive power while maintaining the interpretability, arbitrage-free structure, and numerical stability of the classical framework. Classical LMM parametrizations, based on exponential decay volatility functions and static correlation kernels, are known to perform poorly in sparsely quoted and long-tenor regions of swaption volatility cubes. Machine learning–based diffusion models offer flexibility but often lack transparency, stability, and measure-consistent dynamics. To reconcile these requirements, the present approach embeds a compact neural network within the volatility and correlation layers of the LMM, constrained by structural diagnostics, low-rank correlation construction, and HJM-consistent drift. Empirical tests across major currencies (EUR, GBP, USD) and multiple quarterly datasets from 2024 to 2025 show that the neural-augmented LMM consistently outperforms the classical model. Improvements of approximately 7–10% in implied volatility RMSE and 10–15% in PV RMSE are observed across all datasets, with no deterioration in any region of the surface. These results reflect the model’s ability to represent cross-tenor dependencies and surface curvature beyond the reach of classical parametrizations, while remaining economically interpretable and numerically tractable. The findings support hybrid model designs in quantitative finance, where small neural components complement robust analytical structures. The approach aligns with ongoing industry efforts to integrate machine learning into regulatory-compliant pricing models and provides a pathway for future generative LMM variants that retain an arbitrage-free diffusion structure while learning data-driven volatility geometry. Full article

Massive MIMO systems can substantially enhance spectral efficiency, but such gains rely on the availability of accurate channel state information (CSI). However, the increase in the number of antennas leads to a significant growth in feedback overhead, while conventional deep-learning-based CSI feedback methods also impose a substantial computational burden on the user equipment (UE). To address these challenges, this paper proposes LLM4FB, a one-sided CSI feedback framework that leverages a pre-trained large language model (LLM). In this framework, the UE performs only low-complexity linear projections to compress CSI. In contrast, the BS leverages a pre-trained LLM to accurately reconstruct and predict CSI. By utilizing the powerful modeling capabilities of the pre-trained LLM, only a small portion of the parameters needs to be fine-tuned to improve CSI recovery accuracy with low training cost. Furthermore, a multiobjective loss function is designed to simultaneously optimize normalized mean square error (NMSE) and spectral efficiency (SE). Simulation results show that LLM4FB outperforms existing methods across various compression ratios and mobility levels, achieving high-precision CSI feedback with minimal computational capability from terminal devices. Therefore, LLM4FB presents a highly promising solution for next-generation wireless sensor networks and industrial IoT applications, where terminal devices are often strictly constrained by energy and hardware resources. Full article