Search Results (34,850)

To address the challenges of high complexity in modeling the correlation of multi-dimensional stochastic variables and the difficulty of solving long-term scheduling models in continuous action spaces in multi-energy complementary systems, this paper proposes a long-term optimization scheduling method based on Deep Deterministic Policy Gradient (DDPG). First, an improved C-Vine Copula model is used to construct the multi-dimensional joint probability distribution of water, wind, and solar energy, and Latin Hypercube Sampling (LHS) is employed to generate a large number of water–wind–solar coupling scenarios, effectively reducing the model’s complexity. Then, a long-term optimization scheduling model is established with the goal of maximizing the absorption of clean energy, and it is converted into a Markov Decision Process (MDP). Next, the DDPG algorithm is employed with a noise dynamic adjustment mechanism to optimize the policy in continuous action spaces, yielding the optimal long-term scheduling strategy for the water–wind–solar multi-energy complementary system. Finally, using a water–wind–solar integrated energy base as a case study, comparative analysis demonstrates that the proposed method can improve the renewable energy absorption capacity and the system’s power generation efficiency by accurately quantifying the uncertainties of water, wind, and solar energy and precisely controlling the continuous action space during the scheduling process. Full article

Esterification is a key transformation in the production of lubricants, pharmaceuticals, and fine chemicals. Conventional processes employing homogeneous acid catalysts suffer from limitations such as corrosive byproducts, energy-intensive separation, and poor catalyst reusability. This review provides a comprehensive overview of heterogeneous catalytic systems, including ion exchange resins, zeolites, metal oxides, mesoporous materials, and others, for improved ester synthesis. Recent advances in membrane-integrated reactors, such as pervaporation and nanofiltration, which enable continuous water removal, shifting equilibrium and increasing conversion under milder conditions, are reviewed. Dual-functional membranes that combine catalytic activity with selective separation further enhance process efficiency and reduce energy consumption. Enzymatic systems using immobilized lipases present additional opportunities for mild and selective reactions. Future directions emphasize the integration of pervaporation membranes, hybrid catalyst systems combining biocatalysts and metals, and real-time optimization through artificial intelligence. Modular plug-and-play reactor designs are identified as a promising approach to flexible, scalable, and sustainable esterification. Overall, the interaction of catalyst development, membrane technology, and digital process control offers a transformative platform for next-generation ester synthesis aligned with green chemistry and industrial scalability. Full article

Polymer flooding is a widely used enhanced oil recovery (EOR) method for improving energy efficiency and reducing the carbon footprint of oil production. Optimizing polymer concentration is critical for maximizing recovery while minimizing economic and environmental costs. Here, we present a systematic experimental study which shows that even very low concentrations of polymers yield relatively high recovery rates at adverse mobility ratios (230 cP oil). A series of core flood experiments were conducted on Bentheimer sandstone rock, with polymer concentrations ranging from 40 ppm (1.35 cP) to 600 ppm (10.0 cP). Beyond a mobility ratio threshold, increasing polymer concentration did not significantly enhance recovery. This plateau in performance was attributed to the persistence of viscous fingering and oil crossflow into pre-established water channels. The study suggests that low concentrations of polymer may mobilize oil at high mobility ratios by making use of the pre-established water channels as transport paths for the oil and that the rheology of the polymer enhances this effect. These findings enable reductions in the polymer concentration in fields with adverse mobility ratios, leading to substantial reductions in chemical usage, energy consumption, and environmental impact of the extraction process. Full article

Achieving optimal agricultural yields and promoting sustainable farming relies on accurate crop recommendations. However, the applicability of many current systems is limited by their considerable computational requirements and dependence on comprehensive datasets, especially in resource-limited contexts. This paper presents HOLISTIQ RS, a novel crop recommendation system explicitly designed for operation on low-specification hardware and in data-scarce regions. HOLISTIQ RS combines collaborative filtering with a Markov model to predict appropriate crop choices, drawing upon user profiles, regional agricultural data, and past crop performance. Results indicate that HOLISTIQ RS provides a significant increase in recommendation accuracy, achieving a MAP@5 of 0.31 and nDCG@5 of 0.41, outperforming standard collaborative filtering methods (the KNN achieved MAP@5 of 0.28 and nDCG@5 of 0.38, and the ANN achieved MAP@5 of 0.25 and nDCG@5 of 0.35). Significantly, the system also demonstrates enhanced recommendation diversity, achieving an Item Variety (IV@5) of 23%, which is absent in deterministic baselines. Significantly, the system is engineered for reduced energy consumption and can be deployed on low-cost hardware. This provides a feasible and adaptable method for encouraging informed decision-making and promoting sustainable agricultural practices in areas where resources are constrained, with an emphasis on lower energy usage. Full article

Wireless sensor networks provide a solution for structural health monitoring of aviation pipelines. In the installation environment of aviation pipelines, widespread vibrations can be utilized to extract energy through vibration energy harvesting technology to achieve self-powering of sensors. This study analyzed the vibration characteristics of aviation pipeline structures. The vibration characteristics and influencing factors of typical aviation pipeline structures were obtained through simulations and experiments. An N-shaped symmetric vibration energy harvester was designed considering the limited space in aviation pipeline structures. To improve the efficiency of electrical energy extraction from the vibration energy harvester, expand its operating frequency band, and achieve efficient vibration energy harvesting, this study first analyzed its natural frequency characteristics through theoretical analysis. Finite element simulation software was then used to analyze the effects of the external excitation acceleration direction, mass and combination of counterweights, piezoelectric sheet length, and piezoelectric material placement on the output power of the energy harvester. The structural parameters of the vibration energy harvester were optimized, and the optimal working conditions were determined. The experimental results indicate that the N-shaped symmetric vibration energy harvester designed and optimized in this study improves the efficiency of vibration energy harvesting and can be arranged in the limited space of aviation pipeline structures. It achieves efficient energy harvesting under multi-modal conditions, different excitation directions, and a wide operating frequency band, thus meeting the practical application requirement and engineering feasibility of aircraft design. Full article

Eccentric muscle actions are integral to human movement, rehabilitation, and performance training due to their characteristic high force output (overload) and low energy cost and perceived exertion. Despite the growing use of eccentric devices, a gap in the research exists exploring multi-muscle activation profiles during multi-joint eccentric-only, isokinetic exercise. This study aimed to quantify and compare surface electromyographic (EMG) activity of four leg muscles—vastus lateralis (VL), tibialis anterior (TA), biceps femoris (BF), and medial gastrocnemius (GM)—during a standardized (isokinetic) submaximal eccentric multi-joint exercise using the Eccentron dynamometer. Eighteen healthy adults performed eccentric exercise at 40% of their maximal eccentric strength. Surface EMG data were analyzed using root mean square (RMS) and integrated EMG (iEMG) variables. Repeated-measures ANOVAs and effect sizes (ES) were used to evaluate within-subject differences across muscles. Results showed significantly greater activation in the VL compared to all other muscles (p < 0.05; and ES of 1.28–3.17 versus all other muscles), with the TA also demonstrating higher activation than the BF (p < 0.05). The BF exhibited the lowest activation, suggesting limited hamstring engagement. These findings highlight the effectiveness of the multi-joint isokinetic eccentric leg press movement (via an Eccentron machine) in targeting the quadriceps and dorsiflexors, while indicating the possible need for supplementary hamstring and plantar flexor exercises when aiming for a comprehensive lower body training routine. This study provides important insights for optimizing eccentric training protocols and rehabilitation strategies. Full article

For transmission systems with regular order excitation, the order vibration will be conducted to each component of the system and affect the stability and service life of the system. A method with a regular non-circular active pulley is proposed in this paper, which is used to counteract the regular order excitation and the regular load excitation. A toothed belt drive system with second-order excitation is taken as an example. According to the existing analytical model of the tooth belt drive system, the modeling process and analytical solution algorithm of the system are derived. Based on the coordinate transformation, the algorithms for any position of an elliptical pulley and the common tangent of the circular pulley are given. And the algorithm for the arc length of the elliptical pulley at any arc degree is proposed. The influence of the phase and eccentricity in the elliptical pulley on the dynamic performance of the system is analyzed. Then the experimental verification is carried out. This shows that this system can generate excitation opposite to the main order rotational vibration of the driving pulley and opposite to the load of the driven pulley. Under the combined effect of other load pulleys in the system, there will be an amplification phenomenon in its vibration response. Considering the decrease in the belt span tension and the decline in the performance of energy-absorbing components after long operation, the presented method can better maintain the stability of system performance. This method can provide new ideas for the vibration reduction optimization process of systems with first-order wave excitation. Full article

In the aluminum electrolysis production process, the traditional cell control method based on cell voltage and series current can no longer meet the goals of energy conservation, consumption reduction, and digital-intelligent transformation. Therefore, a new digital cell control technology that is centrally dependent on various process parameters has become an urgent demand in the aluminum electrolysis industry. Among them, the real-time online measurement of alumina concentration is one of the key data points for implementing such technology. However, due to the harsh production environment and limitations of current sensor technologies, hardware-based detection of alumina concentration is difficult to achieve. To address this issue, this study proposes a soft-sensing model for alumina concentration based on a long short-term memory (LSTM) neural network optimized by a weighted average algorithm (WAA). The proposed method outperforms BiLSTM, CNN-LSTM, CNN-BiLSTM, CNN-LSTM-Attention, and CNN-BiLSTM-Attention models in terms of predictive accuracy. In comparison to LSTM models optimized using the Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), Optuna, Tornado Optimization Algorithm (TOC), and Whale Migration Algorithm (WMA), the WAA-enhanced LSTM model consistently achieves significantly better performance. This superiority is evidenced by lower MAE and RMSE values, along with higher R² and accuracy scores. The WAA-LSTM model remains stable throughout the training process and achieves the lowest final loss, further confirming the accuracy and superiority of the proposed approach. Full article

Electric buses are key in the strategy towards a greenhouse-gas-neutral fleet. However, their restrictions in terms of range and refueling as well as their increased price point present new challenges for public transport companies. This study aims to address, based on real-world operational data, how energy consumption and charging behavior affect battery aging and how operational strategies can be optimized to extend battery life under realistic conditions. This article presents an energy consumption analysis with respect to ambient temperatures and average vehicle speed based exclusively on real-world data of an urban bus fleet, providing a data foundation for range forecasting and infrastructure planning optimized for public transport needs. Additionally, the State of Charge (SOC) window during operation and vehicle idle time as well as the charging power were analyzed in this case study to formulate recommendations towards a more battery-friendly treatment. The central research question is whether battery-friendly operational strategies—such as reduced charging power and lower SOC windows—can realistically be implemented in daily public transport operations. The impact of the recommendations on battery lifetime is estimated using a battery aging model on drive cycles. Finally, the reduction in CO₂ emissions compared to diesel buses is estimated. Full article

Biomass torrefaction must be self-sustaining and continuous to be commercially viable, eliminating dependence on additional fuels while achieving industrial-scale production. This study presents a predictive model of a full-scale continuous biomass torrefaction process that explicitly incorporates the radiation absorption properties of torrefaction gas, with a focus on water vapor. Previous research, primarily based on lab-scale batch processes, has not adequately addressed scale-up challenges or the dynamic evolution of torrefaction gas. Industrial insights from Perpetual Next confirm that water vapor significantly impacts reactor performance by absorbing heat and reducing radiative flux to the biomass. Simulations show that neglecting water vapor absorption in reactor design can lead to throughput deviations of 10–20%, affecting process stability and efficiency. Industrial-scale validation demonstrates that the model accurately predicts this effect, ensuring realistic energy demand and throughput expectations. By explicitly incorporating water vapor absorption into the radiation balance, the model provides a validated framework for optimizing reactor design and process scale-up. It demonstrates that failing to consider this effect can lead to operational instability and deviations from the intended torrefaction severity, ultimately affecting industrial-scale performance and self-sustaining operation. Full article

Industrial energy systems are increasingly required to reduce operating costs and CO₂ emissions while integrating variable renewable energy sources. Managing these objectives under uncertainty requires advanced optimization strategies capable of delivering reliable and real-time decisions. To address these challenges, this study focuses on the short-term operational planning of an industrial energy supply system using the rolling horizon approach (RHA). The RHA offers an effective framework to handle uncertainties by repeatedly updating forecasts and re-optimizing over a moving time window, thereby enabling adaptive and responsive energy management. To solve the resulting nonlinear and constrained optimization problem at each RHA iteration, we propose a novel hybrid algorithm that combines Bayesian optimization (BO) with the Interior Point OPTimizer (IPOPT). While global deterministic and stochastic optimization methods are frequently used in practice, they often suffer from high computational costs and slow convergence, particularly when applied to large-scale, nonlinear problems with complex constraints. To overcome these limitations, we employ the BO–IPOPT, integrating the global search capabilities of BO with the efficient local convergence and constraint fulfillment of the IPOPT. Applied to a large-scale real-world case study of a food and cosmetic industry in Germany, the proposed BO–IPOPT method outperformed state-of-the-art solvers in both solution quality and robustness, achieving up to 97.25%-better objective function values at the same CPU time. Additionally, the influence of key parameters, such as forecast uncertainty, optimization horizon length, and computational effort per RHA iteration, was analyzed to assess their impact on system performance and decision quality. Full article

This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying inclusion criteria, 143 peer-reviewed studies published between January 2019 and April 2025 were analyzed. This review shows that AI-driven controllers—especially deep-reinforcement-learning agents—deliver median energy savings of 18–35% for HVAC and other major loads, consistently outperforming rule-based and model-predictive baselines. The evidence further reveals a rapid diversification of methods: graph-neural-network models now capture spatial interdependencies in dense sensor grids, federated-learning pilots address data-privacy constraints, and early integrations of large language models hint at natural-language analytics and control interfaces for heterogeneous IoT devices. Yet large-scale deployment remains hindered by fragmented and proprietary datasets, unresolved privacy and cybersecurity risks associated with continuous IoT telemetry, the growing carbon and compute footprints of ever-larger models, and poor interoperability among legacy equipment and modern edge nodes. The authors of researches therefore converges on several priorities: open, high-fidelity benchmarks that marry multivariate IoT sensor data with standardized metadata and occupant feedback; energy-aware, edge-optimized architectures that lower latency and power draw; privacy-centric learning frameworks that satisfy tightening regulations; hybrid physics-informed and explainable models that shorten commissioning time; and digital-twin platforms enriched by language-model reasoning to translate raw telemetry into actionable insights for facility managers and end users. Addressing these gaps will be pivotal to transforming isolated pilots into ubiquitous, trustworthy, and human-centered IoT ecosystems capable of delivering measurable gains in efficiency, resilience, and occupant wellbeing at scale. Full article

The present study was motivated by the need to enhance indoor air quality and reduce airborne disease transmission in dense urban environments where high-rise residential buildings face challenges in achieving effective natural ventilation. The problem lies in the lack of scalable and convenient tools to optimize natural ventilation rate, particularly in urban settings with varying building heights. To address this, the scientific technique developed with an innovative metric, the urbanized natural ventilation effectiveness factor (uNVeF), integrates regression analysis of wind direction, velocity, air change rate per hour (ACH), window configurations, and building height to quantify ventilation efficiency. By employing a field measurement methodology, the measurements were conducted across 25 window-opening scenarios in a 13.9 m² residential unit on the 35/F of a Hong Kong public housing building, supplemented by the Hellman Exponential Law with a site-specific friction coefficient (0.2907, R² = 0.9232) to estimate the lower floor natural ventilation rate. The results confirm compliance with Hong Kong’s statutory 1.5 ACH requirement (Practice Note for Authorized Persons, Registered Structural Engineers, and Registered Geotechnical Engineers) and achieving a peak ACH at a uNVeF of 0.953 with 75% window opening. The results also revealed that lower floors can maintain 1.5 ACH with adjusted window configurations. Using the Wells–Riley model, the estimation results indicated significant airborne disease infection risk reductions of 96.1% at 35/F and 93.4% at 1/F compared to the 1.5 ACH baseline which demonstrates a strong correlation between ACH, uNVeF and infection risks. The uNVeF framework offers a practical approach to optimize natural ventilation and provides actionable guidelines, together with future research on the scope of validity to refine this technique for residents and developers. The implications in the building industry include setting up sustainable design standards, enhancing public health resilience, supporting policy frameworks for energy-efficient urban planning, and potentially driving innovation in high-rise residential construction and retrofitting globally. Full article

Lithium slag (LS), a by-product of lithium extraction processes, poses a significant disposal challenge during the rapid development of new energy technologies. In this study, LS was used to replace partially washed sand in the process of mortar production to compensate for the content of stone powder in sand. Five mortar mixes containing varying proportions of LS were prepared, and the macroscopic performance was evaluated. A comprehensive microscopic analysis, including microstructure observations, hydration product identification, and pore structure analysis, was conducted. The impact of LS on the chloride ion permeability of mortar was also investigated in this study. The results indicate that an increase in LS content gradually reduces the workability of the mortar, with a 39.29% decrease in fluidity when 40% of the sand is replaced with LS. Moreover, the compressive and flexural strengths of the mortar initially increase and then decrease with higher LS content. Microscopic tests reveal that 20% LS substitution significantly optimizes the pore structure of the mortar, resulting in a lower chloride ion permeability coefficient. Consequently, 20% LS substitution is recommended as the optimal dosage for use as fine aggregate in mortar. Full article

Among the various available synthesis approaches, hydrolytic precipitation offers a simple, cost-effective, and scalable route for producing phase-pure NiO with a controlled morphology and crystallite size. However, the influence of calcination temperature on its crystalline phase, particle size, and antimicrobial activity remains an active field of research. This study aims to investigate the structural, morphological, and antibacterial properties of NiO nanoparticles synthesized via hydrolytic methods and thermally treated at different temperatures. XRD data indicate the presence of the hexagonal crystallographic phase of NiO (space group 166: R-3m), a structural variant less commonly reported in the literature, stabilized under mild hydrolytic synthesis conditions. The average crystallite size increases significantly from 4.97 nm at 300 °C to values of ~17.8 nm at 500–700 °C, confirming the development of the crystal lattice. The ATR-FTIR analysis confirms the presence of the characteristic Ni–O band for all samples, positioned between 367 and 383 cm⁻¹, with a reference value of 355 cm⁻¹ for commercial NiO. The displacements and variations in intensity reflect a thermal evolution of the crystalline structure, but also an important influence of the size of the crystallites and the agglomeration state. The results reveal a systematic evolution in particle morphology from porous, flake-like nanostructures at 300 °C to dense, well-faceted polyhedral crystals at 900 °C. With an increasing temperature, particle size increases. EDS spectra confirm the high purity of the NiO phase across all samples. Additionally, the NiO nanoparticles exhibit calcination-temperature-dependent antibacterial activity, with the complete inhibition of Escherichia coli and Enterococcus faecalis observed after 24 h for the sample calcined at 300 °C and over 90% CFU reduction within 4 h. A significant reduction in E. faecalis viability across all samples indicates time- and strain-specific bactericidal effects. Due to its remarkable multifunctionality, NiO has emerged as a strategic nanomaterial in fields ranging from energy storage and catalysis to antimicrobial technologies, where precise control over its structural phase and particle size is essential for optimizing performance. Full article