Search Results (32,254)

This study explores advanced multivariate models that incorporate non-climatic exogenous variables for very short-term photovoltaic energy forecasting. By integrating historical energy data from multiple photovoltaic plants, the research aims to improve the prediction accuracy of a target plant while addressing critical challenges in electric power systems (EPS), such as frequency stability. Frequency stability becomes increasingly complex as renewable energy sources penetrate the grid because of their intermittent nature. To mitigate this challenge, precise forecasting of photovoltaic energy generation is essential for balancing supply and demand in real time. The performance of long short-term memory (LSTM) networks and bidirectional LSTM (BiLSTM) networks was compared over a 5 min horizon. Including energy generation data from neighboring plants significantly improved prediction accuracy compared to univariate models. Among the models, multivariate BiLSTM showed superior performance, achieving a lower root-mean-square error (RMSE) and higher correlation coefficients. Quantile regression applied to manage prediction uncertainty, providing robust confidence intervals. The results suggest that incorporating an exogenous power series effectively captures spatial correlations and enhances prediction accuracy. This approach offers practical benefits for optimizing grid management, reducing operational costs, improving the integration of renewable energy sources, and supporting frequency stability in power generation systems. Full article

Power demand forecasting is a critical and challenging task for modern power systems and integrated energy systems. Due to the absence of well-established theoretical frameworks and publicly available feature databases on power demand changes, the known interpretable features of power demand fluctuations are primarily derived from expert experience and remain significantly limited. This substantially hinders advancements in power demand forecasting accuracy. Emerging multimodal learning approaches have demonstrated great promise in machine learning and AI-generated content (AIGC). In this paper, we propose, for the first time, a textual-knowledge-guided numerical feature discovery (TKNFD) framework for short-term power demand forecasting by interacting text modal data—a potentially valuable yet long-overlooked resource in the field of power demand forecasting—with numerical modal data. TKNFD systematically and automatically aggregates qualitative textual knowledge, expands it into a candidate feature-type set, collects corresponding numerical data for these features, and ultimately constructs four-dimensional multivariate source-tracking databases (4DM-STDs). Subsequently, TKNFD introduces a two-stage quantitative feature identification strategy that operates independently of forecasting models. The essence of TKNFD lies in achieving reliable and comprehensive feature discovery by fully exploiting the dual relationships of synonymy and complementarity between text modal data and numerical modal data in terms of granularity, scope, and temporality. In this study, TKNFD identifies 38–50 features while further interpreting their contributions and dependency correlations. Benchmark experiments conducted in Maine, Texas, and New South Wales demonstrate that the forecasting accuracy using TKNFD-identified features consistently surpasses that of state-of-the-art feature schemes by up to 36.37% MAPE. Notably, driven by multimodal interaction, TKNFD can discover previously unknown interpretable features without relying on prior empirical knowledge. This study reveals 10–16 previously unknown interpretable features, particularly several dominant features in integrated energy and astronomical dimensions. These discoveries enhance our understanding of the origins of strong randomness and non-linearity in power demand fluctuations. Additionally, the 4DM-STDs developed for these three regions can serve as public baseline databases for future research. Full article

Muscular strength is an essential factor in sports performance and general health, especially for optimizing mechanical power, as well as for injury prevention. The present study biomechanically characterized the half squat (HS) using a systemic structural approach based on mechanical power, called Power-Based Training (PBT), through which four phases of the movement were determined (acceleration and deceleration of lowering and lifting). Five weightlifters from the Mexican national team (categories U17, U20, and U23) participated, who performed five repetitions per set of HS with progressive loads (20%, 35%, 50%, 65%, and 80% of the one repetition maximum). The behavior of the center of mass of the subject–bar system was recorded by photogrammetry, calculating position, velocity, acceleration, mechanical power, and mechanical work. The results showed a significant reduction in velocity, acceleration, and mechanical power as the load increases, as well as variations in the duration and range of displacement per phase. These findings highlight the importance of a detailed analysis to understand the neuromuscular demands of HS and to optimize its application. The PBT approach and global center of mass analysis provide a more accurate view of the mechanics of this exercise, facilitating its application in future research, as well as in performance planning and monitoring. Full article

The development of ion exchange chromatography to polish biopharmaceuticals requires extensive experimental benchmarking. As part of the Design of Experiments (DoE), statistical models increased efficiency somewhat and are still state of the art; however, the capability to predict process conditions is limited due to their nature as interpolating models. Applying the DoE still requires numerous experiments and is constrained to the design space, posing a risk of missing the potential optimum. To make a leap in model-based process development, applying extrapolating models can tremendously extend the design space and also allow for process understanding and knowledge transfer. While existing chromatography modeling software explains experimental data, it often lacks predictive power for new conditions. In academic–industrial cooperation, we demonstrate a new high-fidelity model based on biophysics for developing ion-exchange chromatography in biomanufacturing, making it a general tool in rationalizing process development for the present demand of recombinant proteins and monoclonal antibodies and the emerging demand of new modalities. Using the new computational tool, we achieved predictability and attained high accuracy; with minimal experimental effort to calibrate the system, the mathematical model predicted sensitive process conditions, and even described product-related impurities, antibody charge variants. Thus, the computational tool can be deployed for process-by-design and material-by-design approaches. Full article

The classical exponential model, despite its flexibility, fails to describe data with non-constant failure or between-event dependency. To overcome this limitation, two new bivariate lifetime distributions are introduced in this paper. The Farlie–Gumbel–Morgenstern (FGM)-based and Ali–Mikhail–Haq (AMH)-based modified Fréchet–exponential (MFE) models, by embedding the flexible MEF margin in the FGM and AMH copulas. The resulting distributions accommodate a wide range of positive or negative dependence while retaining analytical traceability. Closed-form expressions for the joint and marginal density, survival, hazard, and reliability functions are derived, together with product moments and moment-generating functions. Unknown parameters are estimated through the maximum likelihood estimation (MLE) and inference functions for margins (IFM) methods, with asymptotic confidence intervals provided for these parameters. An extensive Monte Carlo simulation quantifies the bias, mean squared error, and interval coverage, indicating that IFM retains efficiency while reducing computational complexity for moderate sample sizes. The models are validated using two real datasets, from the medical sector regarding the infection recurrence times of 30 kidney patients undergoing peritoneal dialysis, and from the economic sector regarding the growth of the gross domestic product (GDP). Overall, the proposed copula-linked MFE distributions provide a powerful and economical framework for survival analysis, reliability, and economic studies. Full article

The accelerated urbanisation that is occurring in many regions of the world is resulting in a corresponding increase in the volume of sewage sludge. This sludge is then stored in specialised landfills, the area of which is increasing annually. One of the methods of utilising this sludge is through its combustion in power plants, where it serves to generate heat. However, due to the low calorific value of sewage sludge, it is recommended to combust it in conjunction with high-calorific fuel. To improve energy efficiency of sewage residue biomass utilisation by co-combustion with coal, it is necessary to determine the main combustion parameters and mass fraction in the mixture. The objective of this study is to estimate the primary parameters of combustion of sewage sludge and coal by employing the synchronous thermal analysis method, in addition to determining the concentrations of gaseous substances formed during the combustion process. A comprehensive technical and elemental analysis of the fuels was conducted, and their thermal properties were thoroughly determined. The inorganic residue from sewage sludge combustion was analysed by scanning electron microscopy for the content of trace elements and basic oxides. Thermogravimetric analysis (TGA) of fuels was conducted in an oxidising medium, utilising a 6 mg suspension with a heating rate of 20 °C/min. The profiles of TG, DTG, and DSC curves were then utilised to determine the ignition and burnout temperatures, maximum mass loss rate, combustion index, and synergistic effects. The mixture of coal with 25% sewage sludge was found to have the most energy-efficient performance compared to other mixtures, with a 3% reduction in ignition temperature compared to coal. Concentrations of carbon dioxide, carbon monoxide, nitrogen oxides, and sulphur oxides were also determined. Full article

The integration of large-scale distributed photovoltaics (DGPVs) and the generation of distributed photovoltaics (PVs) and loads with distinct characteristics in different transformer areas causes voltage problems in distribution networks, significantly compromising operational reliability and economy. To address this challenge, this study proposes the installation of a relocatable static var compensator (RSVC) to enhance the voltage regulation capability in addition to conventional voltage regulation methods. An RSVC can be deployed at critical nodes of distribution lines to provide continuous adjustable reactive power. RSVCs’ relocation capability in response to seasonal shifts in reactive power demand makes them an effective solution for spatiotemporal load disparities across transformer areas. A bi-level planning framework is established by first generating multiple typical scenarios based on load categories and their seasonal characteristics. The lower level achieves optimal operation in multiple scenarios through the coordination of active–reactive power regulation devices. The upper level employs a particle swarm optimization algorithm to determine the optimal siting and sizing of energy storage and the RSVC, iteratively invoking the lower-level model to minimize the total investment and operational costs. Validation was conducted on a modified IEEE 33-node test system. The results demonstrate that the proposed method effectively mitigates voltage violations caused by DGPVs and spatiotemporal load disparities while significantly enhancing the economic efficiency of distribution networks. Full article

This study investigates the nanomechanical and tribological behavior of multilayered nitride/carbonitride nanostructured superlattice type coatings (NTCs) composed of alternating TiAlSiNb-N and TiCr-CN sublayers, deposited via high-power ion-plasma magnetron sputtering (HiPIPMS) technique. Reinforced with refractory elements Cr and Nb, the NTC samples exhibit high nanohardness (39–59 GPa), low friction, and excellent wear resistance. A novel analytical approach was introduced to extract stress–strain field (SSF) gradients and divergences from nanoindentation data, revealing alternating strain-hardening and strain-softening cycles beneath the incrementally loaded indenter. The discovered oscillatory behavior, consistent across all samples under the investigation, suggests a general deformation mechanism in thin films under incremental loading. Fourier analysis of the SSF gradient oscillatory pattern revealed a variety of characteristic dominant wavelengths within the length-scale interval (0.84–8.10) nm, indicating multi-scale nanomechanical responses. Additionally, the NTC samples display an anisotropic coating morphology exhibited as unidirectional undulating surface roughness waves, potentially attributed to atomic shadowing, strain-induced instabilities, and limited adatom diffusion. These findings deepen our understanding of nanoscale deformation in advanced PVD coatings and underscore the utility of SSF analysis for probing thin-film mechanics. Full article

Multi-energy microgrids (MEMs) can achieve efficient and low-carbon energy utilization by relying on the coordination, complementarity, and coupling conversion of different energy sources, which is of great significance for new energy consumption and energy cascade utilization. In this paper, a low-carbon economic dispatch model of a multi-energy microgrid that uses a joint carbon capture–CHP-P2G operation is proposed. Firstly, the basic structure of the power–electrolysis–methanol energy (PEME) is established. Secondly, a flexible mechanism for the joint operation of CCPPs and CHP is analyzed, and a flexible joint operation model for carbon capture–CHP-P2G is proposed. Finally, considering the system’s low-carbon operation and economy, a low-carbon economic dispatch model for a multi-energy microgrid in a park is established, with the goal of minimizing the total operating cost of PEME in the park. The results illustrate that the introduction of a liquid storage tank reduces the total cost and carbon emissions of the MEM by 4.04% and 8.49%, respectively. The application of an electric boiler and ORC effectively alleviates the problem of peak–valley differences in the electric heating load. Our joint operation model realizes the dual optimization of the MEM’s flexibility and low-carbon requirement through the collaboration of multiple pieces of technology. Full article

With the expansion of the scale of high-proportion wind and solar power grid connections, the problems of abandoned wind and solar power and insufficient peak shaving have become increasingly prominent. The electric–hydrogen coupling system has greater potential in flexible regulation, providing a new technological approach for the consumption of new energy. This paper proposes a two-layer optimization model for an electricity–hydrogen coupled distributed power generation system. The model is based on the collaborative regulation of flexible loads by electrolytic cells and fuel cells. Through the collaborative optimization of capacity configuration and operation scheduling, it breaks through the strong dependence of traditional systems on the distribution network and enhances the autonomous consumption capacity of new energy. The upper-level optimization model aims to minimize the total life-cycle cost of the system, and the lower-level optimization model aims to minimize the system’s operating cost. The capacity configuration of each module before and after the integration of flexible loads is compared. The simulation results show that the integration of flexible loads can not only effectively reduce the level of wind and solar power consumption in distributed power generation systems, but also play a role in load peak shaving and valley filling. At the same time, it can effectively reduce the system’s peak electricity purchase and sale cost and reduce the system’s dependence on the distribution network. Based on this, with the premise of meeting the load demand, the capacity configuration results of each module were compared when connecting electrolytic cells of different capacities. The results show that the simulated area has the best economic benefits when connected to a 4 MW electrolytic cell. This optimization model can increase the high wind and solar power consumption rate by 23%, reduce the peak purchase and sale cost of electricity by 40%, and achieve an economic benefit coefficient of up to 0.097. Full article

With the increasing penetration of renewable energy, doubly-fed variable speed pumped storage units (DFVSPSUs) are playing an increasingly critical role in grid frequency regulation. However, traditional PI control struggles to address the control challenges posed by the strong nonlinearity of the units and abrupt operational condition changes. This paper proposes an improved β-incremental generalized predictive controller (β-GPC), which achieves precise rotor-side current control through instantaneous linearization combined with parameter identification featuring a forgetting factor. Simulation results demonstrate that under different power command step changes, the traditional PI controller requires up to approximately 0.48 s to reach a steady state while exhibiting a certain degree of oscillations. In contrast, the enhanced β-GPC controller can stabilize the unit in just 0.2 s without any overshoot or subsequent oscillations. It is evident that the proposed controller delivers a superior regulation performance, characterized by a shorter settling time, reduced overshoot, and minimized oscillations. Full article

The propagation of high-frequency ultrasound waves will generate both physical and chemical effects as they propagate through a liquid medium, such as acoustic streaming, an acoustic fountain, and atomization. These phenomena are believed to be the main factors that contribute to the enhancement of mass transfer in the gas–liquid carbon dioxide (CO₂) absorption system. Computational Fluid Dynamic (CFD) simulation is one of the powerful tools that can be used to model the complex hydrodynamic behavior induced by the propagation of ultrasound waves in the liquid medium. In this study, the ultrasonic irradiation forces were simulated via the momentum source term method using commercial CFD software (ANSYS Fluent V19.1). In addition, a parametric study was conducted to investigate the influences of absorber height and ultrasonic power on the hydrodynamic mixing performance. The simulation results indicated that enhanced mixing and a higher intensification factor were achieved with increased fountain flow velocity, particularly at the lowest absorber height and highest ultrasonic power. Conversely, the energy efficiency was improved with the increase of absorber height and decrease of ultrasonic power. To determine the optimal combination of absorber height and ultrasonic power, this trade-off between the energy efficiency and intensification in the ultrasonic-assisted absorption system (UAAS) is a crucial consideration during process scale-up. Full article

The fundamental principles of “sustainable development” and “green” promoted by ESG align with the concept of “green and sustainable” development. Enhancing enterprise ESG is a methodical endeavor that necessitates enterprises to possess ESG investment capabilities, coordinate many stakeholders, and leverage the influence of prominent market players. State-owned enterprises (SOEs) possess a specific level of support within a nation’s economy. SOEs serve as a fundamental pillar of China’s socialist economic system with distinctive characteristics, significantly influencing business conduct and reinforcing corporate value orientation. Consequently, the capacity of SOEs to assume a strategic leadership role in enhancing supply chain ESG performance is of paramount importance for the general elevation of ESG standards among Chinese enterprises. Limited research has investigated the transmission effect of the ESG performance among chain enterprises from a supply chain viewpoint, particularly regarding the pivotal role of SOEs in enhancing the ESG performance of these entities. This article examines the influence of SOEs’ ESG performance on the ESG performance of supply chain enterprises, focusing on the spillover effects of SOEs’ ESG performance within the supply chain context. It investigates how SOEs lead upstream and downstream enterprises in enhancing their ESG performance, aiming to address the existing cognitive gap in this area and provide substantial evidence for pertinent theories and practices. This article, employing an empirical research methodology, discovers that the ESG performance of state-owned supply chain core enterprises significantly enhances the ESG performance of enterprises in a supply chain, while non-state-owned supply chain core enterprises do not exhibit this effect. Furthermore, research indicates that this effect is asymmetric: when the supply chain core enterprise is a SOE and the enterprises in the supply chain are non-state-owned, the leading effect is more pronounced, and this effect is more powerful for upstream enterprises. The heterogeneity test reveals that the impact of the ESG performance is more pronounced in larger state-owned supply chain core enterprises that have been publicly listed for an extended duration and operate in highly competitive markets. The conclusions of this essay address the deficiencies of current research and provide significant practical implications for the development of green supply chains in the contemporary era. Full article

TRISO fuels are proposed for portable and modular power reactor technologies. Expansion is dependent upon improvements to safety through a thorough understanding of fission product behavior as related to functional containment design philosophies. Significant knowledge gaps exist in the thermodynamic behavior of neodymium and cerium fission products solubility in graphite, which play a role in the qualification of TRISO fuels. DSC measurements were conducted on Nd-C and Ce-C carbide fission products to expand upon calculated phase equilibria. Various crucible tests, calibrant experimentation, sample generation and sample preparation techniques, and new thermodynamic measurements have been performed. New phase equilibria were produced to improve Nd-C and Ce-C phase diagrams and further inform models of fission product behavior within TRISO fuels. The following phase transition temperatures are reported for Nd-C, with an error of ±32.7 °C: 10 mol%C—$844.6$ °C, $891.2$ °C, and $911.2$ °C; 16 mol%C—$724.1$ °C and $771.2$ °C; 20 mol%C—$731.8$ °C; 25 mol%C—$726.1$ °C and $762.2$ °C; 30 mol%C—$718.5$ °C and $976.4$ °C; 35 mol%C—$825.1$ °C and $995.3$ °C; 40 mol%C—$1274.6$ °C. The following phase transition temperatures are reported for Ce-C with an error of ±32.7 °C: 20 mol%C—$878.6$ °C; 32 mol%C—$714.7$ °C and 857 °C; 67 mol%C—$1138.7$ °C. Full article

The article describes a mathematical model of methanol steam reforming taking place at the anode of a molten carbonate fuel cell (MCFC). An artificial neural network with an appropriate structure was subjected to a learning process on the data obtained during an experiment on the laboratory stand for testing high-temperature fuel cells located at the Institute of Heat Engineering of the Warsaw University of Technology. The backpropagation of error method was used to train the neural network. The training data included the results of methanol steam reforming in the fuel cell for steam-to-carbon ratios of 2:1, 3:1, and 4:1. The artificial neural network was then asked to generate results for other steam-to-carbon ratios. As a result, the artificial neural network predicted that the highest power density for a molten carbonate fuel cell working on methanol would be obtained with a steam-to-carbon ratio of 2.8:1. The article’s key achievement is the application of artificial intelligence to calculate an unusual steam-to-carbon ratio for the methanol steam reforming process occurring directly at the anode of an MCFC fuel cell. The solution proposed in the article contributed to reducing the number of experimental studies. Full article