China, People’s Republic

This work provides a detailed evaluation of a novel biomass-fueled multigeneration system conceived to contribute to the growing emphasis on sustainable energy solutions. The architecture comprises a biomass gasifier an innovative cascaded organic Rankine cycle (CORC) incorporating a high-temperature mixture in the top cycle a proton exchange membrane electrolyzer (PEME) a Brayton cycle and waste heat utilization units all operating together to deliver electricity hydrogen (H2) and thermal output. A comprehensive thermodynamic modeling framework is established to evaluate the system’s performance across various operational scenarios. The framework emphasizes critical metrics including exergy efficiency levelized total emissions (LTE) and payback period (PP). These indicators ensure a holistic assessment of energy exergy economic and environmental considerations. Parametric studies demonstrate that enhancements in biomass mass flow rate and combustion chamber temperature significantly increase power output and H2 production while reducing the payback period underscoring the system’s flexibility and economic feasibility. Furthermore the study employs sophisticated machine learning optimization methods combining artificial neural networks (ANNs) with genetic algorithms (GA) to determine optimal operating conditions with minimal computational effort and maximum efficiency. When evaluated at nominal parameters the system records an exergy efficiency of 23.72 % achieves a PP of 5.61 years and yields an LTE value of 0.34 ton/GJ. However under optimized conditions these values improve to 35.01 % 3.78 years and 0.241 ton/GJ respectively.

The hybrid energy storage system (HESS) that combines battery with hydrogen storage exploits complementary power/energy characteristics but most studies optimize capacity and operation separately leading to suboptimal overall performance. To address this issue this paper proposes a bi-level co-optimization framework that integrates deep reinforcement learning (DRL) and mixed integer programming (MIP). The outer layer employs the TD3 algorithm for capacity configuration while the inner layer uses the Gurobi solver for optimal operation under constraints. On a standalone PV–wind–load-HESS system the method attains near-optimal quality at dramatically lower runtime. Relative to GA + Gurobi and PSO + Gurobi the cost is lower by 4.67% and 1.31% while requiring only 0.52% and 0.58% of their runtime; compared with a direct Gurobi solve the cost remains comparable while runtime decreases to 0.07%. Sensitivity analysis further validates the model’s robustness under various cost parameters and renewable energy penetration levels. These results indicate that the proposed DRL–MIP cooperation achieves near-optimal solutions with orders of magnitude speedups. This study provides a new DRL–MIP paradigm for efficiently solving strongly coupled bi-level optimization problems in energy systems.

This study investigates the performance degradation of X70 steel weld material in highpressure natural gas pipelines in the Sichuan-Chongqing region and its impact on pipeline safety by investigating their behavior under multiphysics environments including varying gas media (nitrogen methane hydrogen-blended) pressure conditions (0.1–10 MPa) and material regions (base metal vs. weld). A key novelty of this work is the introduction of a “degradation rate” metric to quantitatively assess the deterioration of weld mechanical properties. A key novelty of this work is the explicit introduction of a “degradation rate” metric to quantitatively assess the deterioration of weld mechanical properties. Slow strain rate tensile tests combined with fracture morphology and microstructure analysis reveal that welds exhibit inferior mechanical properties due to microstructural inhomogeneity and residual stresses including a yield stress reduction of 15.2–18.7%. The risk of brittle fracture was highest in the hydrogen-blended environment while nitrogen exhibited the most benign effect. Material region changes were identified as the most significant factor affecting degradation. This research provides crucial data and theoretical support for pipeline safety design and material performance optimization.

Hydrogen is widely regarded as a promising carbon-free alternative fuel. However the development of low-emission marine gas turbine combustion systems has been hindered by the associated risks of combustion instability also termed as thermoacoustic oscillations. Although there is sufficient literature on hydrogen fuel and combustion instability systematic reviews addressing the manifestations and mechanisms of these instabilities remain limited. The present study aims to provide a comprehensive review of combustion instabilities in hydrogen-enriched marine gas turbines with a particular focus on elucidating the characteristics and underlying mechanisms. The review begins with a concise overview of recent progress in understanding the fundamental combustion properties of hydrogen and then details various instability phenomena in hydrogen-enriched methane flames. The mechanisms by which hydrogen enrichment affects combustion instabilities are extensively discussed particularly in relation to the feedback loop in thermoacoustic combustion systems. The paper concludes with a summary of the key combustion instability challenges associated with hydrogen addition to methane flames and offers prospects for future research. In summary the review highlights the interaction between hydrogenenriched methane flames and thermoacoustic phenomena providing a foundation for the development of stable low-emission combustion systems in industrial marine applications incorporating hydrogen enrichment.

The development of durable and efficient catalysts capable of driving both hydrogen and oxygen evolution reactions is essential for advancing sustainable hydrogen production through overall water electrolysis. In this study we developed a corrosion-mediated approach where Ni ions originate from the self-corrosion of the nickel foam (NF) substrate to construct Pt-modified NiFe layered double hydroxide (Pt-NiFeOxHy@NiFe-LDH) under ambient conditions. The obtained catalyst exhibits a hierarchical architecture with abundant defect sites which favor the uniform distribution of Pt clusters and optimized electronic configuration. The Pt-NiFeOxHy@NiFe-LDH catalyst constructed through the interaction between Pt sites and defective NiFe layered double hydroxide (NiFe-LDH) demonstrates remarkable hydrogen evolution reaction (HER) activity delivering an overpotential as low as 29 mV at a current density of 10 mA·cm−2 and exhibiting a small tafel slope of 34.23 mV·dec−1 in 1 M KOH together with excellent oxygen evolution reaction (OER) performance requiring only 252 mV to reach 100 mA·cm−2 . Moreover the catalyst demonstrates outstanding activity and durability in alkaline seawater maintaining stable operation over long-term tests. The Pt-NiFeOxHy@NiFe-LDH electrode when integrated into a two-electrode system demonstrates operating voltages as low as 1.42 and 1.51 V for current densities of 10 and 100 mA·cm−2 respectively and retains outstanding stability under concentrated alkaline conditions (6 M KOH 70 ◦C). Overall this work establishes a scalable and economically viable pathway toward high-efficiency bifunctional electrocatalysts and deepens the understanding of Pt-LDH interfacial synergy in promoting water-splitting catalysis.

During the critical period of energy transition the collaborative optimization of the electricity-hydrogentransportation coupling system is of vital importance for achieving efficient energy utilization and sustainable development.This paper proposes a collaborative operation mechanism of Distributed Robust Optimization (DRO) considering carbon emissions. Firstly a Stackelberg game dynamic pricing strategy is constructed for the integrated energy station (IES) and the electricity-hydrogen hybrid charging station (HRS) where the upper-level IES optimizes the electricity price setting strategy and the lower-level HRS dynamically adjusts the electricity purchase-hydrogen production plan. Secondly the Wasserstein distance is used to describe the uncertainties of hydrogen vehicle loads and wind-solar power generation and a bisection algorithm-column constraint generation (BA-C&CG) hybrid algorithm is designed to solve the model. Finally the numerical example verification shows that the daily operation cost of HRS under the proposed mechanism is as low as 1108.53 EUR which is 10.58 % and 7.38 % lower than that of the commonly used stochastic optimization (SO) and robust optimization (RO) respectively. The variance analysis (F = 536.05P < 0.001) confirms that the cost advantage is statistically significant. In terms of carbon emission reduction effect the DRO-Stackelberg game model has the lowest daily carbon cost (6.98EUR). This mechanism effectively balances the economic and robustness of the system and the single dispatch calculation time is only 112.09 s meeting the real-time operation requirements of engineering. It provides technical support for the low-carbon collaborative operation of the electricity-hydrogen-transportation coupling system.

With the IMO’s 2050 decarbonization target hydrogen is a key zero-carbon fuel for shipping but the lack of systematic risk assessment methods for hydrogen-powered marine propulsion systems (under harsh marine conditions) hinders its large-scale application. To address this gap this study proposes an integrated risk evaluation framework by fusing Failure Mode Effects and Criticality Analysis (FMECA) with the Fuzzy Analytic Hierarchy Process (FAHP)—resolving the limitation of traditional single evaluation tools and adapting to the dynamic complexity of marine environments. Scientific findings from this framework confirm that hydrogen leakage high-pressure storage tank valve leakage and inverter overload are the three most critical failure modes with hydrogen leakage being the primary risk source due to its high severity and detection difficulty. Further hazard matrix analysis reveals two key risk mechanisms: one type of failure (e.g. insufficient hydrogen concentration) features “high severity but low detectability” requiring real-time monitoring; the other (e.g. distribution board tripping) shows “high frequency but controllable impact” calling for optimized operations. This classification provides a theoretical basis for precise risk prevention. Targeted improvement measures (e.g. dual-sealed valves redundant cooling circuits AI-based regulation) are proposed and quantitatively validated reducing the system’s overall risk value from 4.8 (moderate risk) to 1.8 (low risk). This study’s core contribution lies in developing a universally applicable scientific framework for marine hydrogen propulsion system risk assessment. It not only fills the methodological gap in traditional evaluations but also provides a theoretical basis for the safe promotion of hydrogen shipping supporting the scientific realization of the IMO’s decarbonization goal.

Mobile emergency power supply vehicles (MEPSVs) powered by diesel engines or lithiumion batteries (LIBs) have become a viable tool for emergency power supply. However diesel-powered MEPSVs generate noise and environmental pollution while LIB-powered vehicles suffer from limited power supply duration. To overcome these limitations a hydrogen-powered MEPSV incorporating a soft open point (SOP) was developed in this study. We analyzed widely used operating scenarios for the SOP-equipped MEPSV and determined important parameters including vehicle body structure load capacity driving speed and power generation capability for the driving motor hydrogen fuel cell (FC) module auxiliary LIB module and SOP equipment. Subsequently we constructed an energy management strategy for the model for MEPSV which uses multiple energy sources of hydrogen fuel cells and lithium-ion batteries. Through simulations an optimal hydrogen consumption rate in various control strategies was validated using a predefined load curve to optimize the energy consumption minimization strategy and achieve the highest efficiency.

To address the economic and reliability challenges of high-penetration renewable energy integration in electricity-heat-hydrogen integrated energy systems and support the dualcarbon strategy this paper proposes an optimal dispatch method integrating Information Gap Decision Theory (IGDT) and Fuzzy Chance-Constrained Programming (FCCP). An IES model coupling multiple energy components was constructed to exploit multi-energy complementarity. A stepped carbon trading mechanism was introduced to quantify emission costs. For interval uncertainties in renewable generation IGDT-based robust and opportunistic dispatch models were established; for fuzzy load uncertainties FCCP transformed them into deterministic equivalents forming a dual-layer “IGDT-FCCP” uncertainty handling framework. Simulation using CPLEX demonstrated that the proposed model dynamically adjusts uncertainty tolerance and confidence levels effectively balancing economy robustness and low-carbon performance under complex uncertainties: reducing total costs by 12.7% cutting carbon emissions by 28.1% and lowering renewable curtailment to 1.8%. This study provides an advanced decision-making paradigm for low-carbon resilient IES.

Reliable operation of proton exchange membrane fuel cells (PEMFCs) is crucial for their widespread commercialization and accurate fault diagnosis is the key to ensuring their long-term stable operation. However traditional fault diagnosis methods not only lack sufficient interpretability making it difficult for users to trust their diagnostic decisions but also one-dimensional (1D) feature extraction methods highly rely on manual experience to design and extract features which are easily affected by noise. This paper proposes a new interpretable fault diagnosis algorithm that integrates Gramian angular field (GAF) transform convolutional neural network (CNN) and gradient-weighted class activation mapping (Grad-CAM) for enhanced fault diagnosis and analysis of proton exchange membrane fuel cells. The algorithm is systematically validated using experimental data to classify three critical health states: normal operation membrane drying and hydrogen leakage. The method first converts the 1D sensor signal into a two-dimensional GAF image to capture the temporal dependency and converts the diagnostic problem into an image recognition task. Then the customized CNN architecture extracts hierarchical spatiotemporal features for fault classification while Grad-CAM provides visual explanations by highlighting the most influential regions in the input signal. The results show that the diagnostic accuracy of the proposed model reaches 99.8% which is 4.18% 9.43% and 2.46% higher than other baseline models (SVM LSTM and CNN) respectively. Furthermore the explainability analysis using Grad-CAM effectively mitigates the “black box” problem by generating visual heatmaps that pinpoint the key feature regions the model relies on to distinguish different health states. This validates the model’s decision-making rationality and significantly enhances the transparency and trustworthiness of the diagnostic process.