Search Results (398)

The global rise in population and advancement in civilization have led to a substantial increase in energy demand, particularly in the industrial sector. This sector accounts for a considerable proportion of total energy consumption, with approximately three-quarters of its energy consumption being used for heat processes. To meet the Paris Agreement goals, countries are aligning policies with international agreements, and companies are setting net-zero targets. Upstream emissions of the Scope 3 category refer to activities in the company’s supply chain, being crucial for achieving its net-zero ambitions. This study analyzes heating solutions for the supply chain of certain globally operating companies, contributing to their 2030 carbon-neutral ambition. The objective is to identify current and emerging heating solutions from carbon dioxide equivalent (CO₂e) impact, economic, and technical perspectives, considering regional aspects. The methodology includes qualitative and quantitative surveys to identify heating solutions and gather regional CO₂e emission factors and energy prices. Calculations estimate the CO₂e emissions and energy costs for each technology or fuel, considering each solution’s efficiency. The study focuses on Europe, the United States, Brazil, China, and Saudi Arabia, regions or countries representative of companies’ global supply chain setups. Results indicate that heat pumps are the optimal solution for low temperatures, while biomass is the second most prevalent solution, except in Saudi Arabia where natural gas is more feasible. For medium and high temperatures, natural gas is viable in the short term for Saudi Arabia and China, while biomass and electrification are beneficial for other regions. The proportion of electricity in the energy mix is expected to increase, but achieving decarbonization targets requires cleaner energy mixes or competitive Power Purchase Agreement (PPA) projects. Brazil, with its high proportion of renewable energy sources, offers favorable conditions for using green electricity to reduce emissions. The utilization of biomethane is promising if costs and incentives align with those in the EU. Although not the objective of this study, a comprehensive analysis of CAPEX and lifecycle costs associated with equipment is necessary when migrating technologies. Policies and economic incentives can also make these solutions more or less favorable. Full article

This paper proposes an experimental design to publish online the measurements obtained from four sensors: one sensor inside a cistern measures the level of drinking water, another sensor in a water tank monitors its level, a third sensor measures water flow or pressure from the pipes, and a fourth sensor assesses water quality. Several tank filling and emptying tests were performed. Experimental results demonstrated that if the cistern perceived that there was no water in the tank, it turned on the water pump to fill the tank to 100% of its storage capacity; while this was happening, the water level in the cistern and tank, the flow of water from the piped water, and the quality of the water could be visualized on a dashboard. In short, this proposal monitors water levels and flows through the Internet of Things. Data collected by sensors are posted online and stored in a database. Full article

Validation of groundwater models is relatively challenging due to the need to reserve scarce water level data for calibration targets. In addition, traditional statistical validation metrics are unintuitive for non-technical audiences and do not directly identify model behaviors that require further refinement. We developed a novel model validation method that analyzes rate change events at pump-and-treat wells and statistically compares the water level responses at nearby monitoring wells between the data and model. The method takes advantage of events that occur alongside ambient pumping, unlike parameter estimation techniques that require independent drawdown or recovery events. The ability of the model to match well connections that are evident (or not evident) in the observations is characterized statistically, leading to four decision scenarios: model matches the observed connection (1) or lack thereof (2), model exhibits a connection that is not observed (3), or model over- or understates the observed connection (4). The method is applied to an FEHM-based groundwater flow and transport model that is shown to match 84.5% of the well connections analyzed. The method provides novel perspectives on the influence of calibration targets on the flow field and suggests that although the overall effect of drawdown targets was to improve the model, the choice of target well pairs creates flow pathways that may be inconsequential during normal operational conditions. The model adequately matches the flow over short spatial scales (<800 m) and over-represents the flow over greater distances (300–1200 m), suggesting the need for “null” drawdown targets in subsequent rounds of calibration. Full article

Rod pumping systems are widely used in oil wells. Accurate fault prediction could reduce equipment fault rate and has practical significance in improving oilfield production efficiency. This paper analyzed the production journal of rod pumping wells in block X of Xinjiang Oilfield. According to the production journal, oil well maintenance operations are primarily caused by five types of faults: scale, wax, corrosion, fatigue, and wear. These faults make up approximately 90% of all faults. 1354 oil wells in the block that experienced workover operations as a result of the aforementioned factors were chosen as the research objects for this paper. To lower the percentage of data noise, wavelet threshold denoising and variational mode decomposition were used. Based on the bidirectional long short-term memory network, an intelligent model for fault prediction was built. It was trained and verified with the help of the sparrow search algorithm. Its efficacy was demonstrated by testing various deep learning models in the same setting and with identical parameters. The results show that the prediction accuracy of the model is the highest compared with other 11 models, reaching 98.61%. It is suggested that the model using artificial intelligence can provide an accurate fault warning for the oilfield and offer guidance for the maintenance of the rod pumping system, which is meant to reduce the occurrence of production stagnation and resource waste. Full article

This work investigates the enhancement of optical energy in the synchronized dynamics of three erbium-doped fiber lasers (EDFLs) that are diffusively coupled in a unidirectional ring configuration without the need for external pump modulation. Before the system shows stable high-energy pulses, different dynamic behaviors can be observed in the dynamics of the coupled lasers. The evolution of the studied system was analyzed using different techniques for different values of coupling strength. The system shows the well-known dynamic behavior towards chaos at weak coupling, starting with a fixed point at low coupling and passing through Hopf and torus bifurcations as the coupling strength increases. An interesting finding emerged at high coupling strengths, where phase locking occurs between the frequencies of the three lasers of the system. This phase-locking leads to a significant increase in the peak energy of the EDFL pulses, effectively converting the emission into short, high amplitude pulses. With this method, it is possible to significantly increase the peak energy of the laser compared to a continuous EDFL single pulse. Full article

Under “dual-carbon” goals and rapid renewable energy growth, increasing start-stop frequency poses new challenges to safe operations of pumped-storage power plant equipment. Ensuring equipment safety and predictive maintenance under complex conditions urgently requires vibration warnings and trend forecasting for pumped-storage units. In this study, the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. We monitored the vibration-signal data of hydroelectric units over a long period of time, and the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are accurately obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. In this paper, a BP neural network prediction model, a support vector machine (SVM) prediction model, a convolutional neural network (CNN) prediction model, and a long short-term memory network (LSTM) prediction model are used to predict the trend of vibration signals of the pumped-storage unit under different operating conditions. The model prediction effect is analyzed by using the different error evaluation functions, and the prediction results are compared with the predicted results of the four different methods. By comparing the prediction effects of the four different methods, it is concluded that LSTM has higher prediction accuracy and can predict the vibration trends of hydropower units more accurately. Full article

Photodetectors play a critical role in space lidars designed for scientific investigations from orbit around planetary bodies. The detectors must be highly sensitive due to the long range of measurements and tight constraints on the size, weight, and power of the instrument. The detectors must also be space radiation tolerant over multi-year mission lifetimes with no significant performance degradation. Early space lidars used diode-pumped Nd:YAG lasers with a single beam for range and atmospheric backscattering measurements at 1064 nm or its frequency harmonics. The photodetectors used were single-element photomultiplier tubes and infrared performance-enhanced silicon avalanche photodiodes. Space lidars have advanced to multiple beams for surface topographic mapping and active infrared spectroscopic measurements of atmospheric species and surface composition, which demand increased performance and new capabilities for lidar detectors. Higher sensitivity detectors are required so that multi-beam and multi-wavelength measurements can be performed without increasing the laser and instrument power. Pixelated photodetectors are needed so that a single detector assembly can be used for simultaneous multi-channel measurements. Photon-counting photodetectors are needed for active spectroscopy measurements from short-wave infrared to mid-wave infrared. HgCdTe avalanche photodiode arrays have emerged recently as a promising technology to fill these needs. This paper gives a review of the photodetectors used in past and present lidars and the development and outlook of HgCdTe APD arrays for future space lidars. Full article

Ongoing investments in irrigation technologies highlight the need to accurately estimate the longevity and magnitude of water savings at the watershed level to avoid the paradox of irrigation efficiency. This paradox arises when irrigation pumping exceeds crop water demand, leading to excess water that is not recovered by the watershed. Comprehensive water accounting from farm to watershed scales is challenging due to spatial variability and inadequate socio-hydrological data. We hypothesize that water savings are short term, as prior studies show rapid recharge responses to surface changes. Precise estimation of these time scales and water savings can aid water managers making decisions. In this study, we examined water savings at three 65-hectare sites in Nebraska with diverse soil textures, management practices, and groundwater depths. Surface geophysics effectively identified in-field variability in soil water content and water flux. A one-dimensional model showed an average 80% agreement with chloride mass balance estimates of deep drainage. Our findings indicate that groundwater response times are short and water savings are modest (1–3 years; 50–900 mm over 10 years) following a 120 mm/year reduction in pumping. However, sandy soils with shallow groundwater show minimal potential for water savings, suggesting limited effectiveness of irrigation efficiency programs in such regions. Full article

In recent years, increasing attention has been given to reducing energy consumption in hydraulic excavators, resulting in extensive research in this field. One promising solution has been the integration of hydrostatic transmission (HST) and hydraulic pump/motor (HPM) configurations in parallel systems. However, these systems face challenges such as noise, throttling losses, and leakage, which can negatively impact both tracking accuracy and energy efficiency. To address these issues, this paper introduces an intelligent real-time prediction framework for system positioning, incorporating particle swarm optimization (PSO), long short-term memory (LSTM), a gated recurrent unit (GRU), and proportional–integral–derivative (PID) control. The process begins by analyzing real-time system data using Pearson correlation to identify hyperparameters with medium to strong correlations to the positioning parameters. These selected hyperparameters are then used as inputs for forecasting models. Independent LSTM and GRU models are subsequently developed to predict the system’s position, with PSO optimizing four key hyperparameters of these models. In the final stage, the PSO-optimized LSTM-GRU models are employed to perform real-time intelligent predictions of motion trajectories within the system. Simulation and experimental results show that the model achieves a prediction deviation of less than 3 mm, ensuring precise real-time predictions and providing reliable data for system operators. Compared to traditional PID and LSTM-GRU-PID controllers, the proposed controller demonstrated superior tracking accuracy while also reducing energy consumption, achieving energy savings of up to 10.89% and 2.82% in experimental tests, respectively. Full article

Sustainable development of islands cannot be achieved without the use of renewable energy to address energy and freshwater supply issues. Utilizing the widely distributed tidal current energy in island regions can enhance local energy and water supply security. To achieve economic and operational efficiency, it is crucial to fully account for the unique periodicity and intermittency of tidal current energy. In this study, a tidal current-powered freshwater and energy supply system is proposed. The marine current turbine adopts a direct-drive configuration and will be able to directly transfer the power of the turbine rotation to the seawater pump to improve the energy efficiency. Additionally, the system incorporates batteries for short-term energy storage, aimed at increasing the capacity factor of the electrolyzer. A simulation is conducted using measured inflow velocity data from a full 12 h tidal cycle. The results show that the turbine’s average power coefficient reaches 0.434, the electrolyzer’s average energy efficiency is 60.9%, the capacity factor is 70.1%, and the desalination system’s average specific energy consumption is 6.175 kWh/m³. The feasibility of the system design has been validated. Full article

Deep and offshore drilling operations face complex geological formations, uncertain formation pressures, and narrow safety density windows, making them susceptible to lost circulation risks. To address these challenges, this paper introduces an innovative, intelligent lost circulation monitoring model that incorporates geological lithology information. This model not only utilizes real-time drilling parameters, but also encodes geological information such as rock type as inputs to the model. By combining these key lithological features, the model can comprehensively assess wellbore stability and reduce the lost circulation risks. In this paper, the Conditional Tabular Generative adversarial network (CTGAN) model is used to enhance the data of small-sample risk data, which can effectively expand the data distribution space and improve the performance of the model. This paper conducts a comparative analysis of intelligent monitoring results using artificial neural networks (ANNs), long short-term memory (LSTM), and temporal convolutional networks (TCNs). The results show that the TCN achieves an identification accuracy of 93.7%. Furthermore, the analysis reveals that the inclusion of lithology information significantly enhances the model’s performance, resulting in a 7.1% increase in accuracy. The false alarm rate of the model can be reduced by 10.2%, considering the fluctuation of the logging curve caused by the on/off condition of the pump. This indicates that the introduction of lithology information and the condition of the pump on−off provide advantages in monitoring and identifying lost circulation risks, enabling a more precise assessment of wellbore stability and a reduction in lost circulation incidents. The method of lost circulation monitoring proposed in this paper provides an important safety guarantee for the oil drilling industry. Full article

Background: This study examines the results of autopsy examinations specifically aimed at documenting complications arising from the implantation phase and treatment with veno–arterial extracorporeal membrane oxygenation (VA-ECMO) in patients with refractory cardiac arrest. ECMO and VA-ECMO in particular are life-saving interventions that, in the case of cardiac arrest, can temporarily replace cardiac pump function. VA-ECMO is, however, a very invasive procedure and is associated with early mechanical, haemorrhagic, and thrombotic events, infections, and late multi-organ dysfunction. Aim: This research aims to evaluate autoptic and histologic findings in patients on VA-ECMO support, providing clinical and forensic evaluation elements with respect to the procedure and clinical settings. Materials and Methods: The study analysed 10 cases, considering variables such as the duration of cardiac arrest, understood as the time between the cardiac arrest event and reperfusion with VA-ECMO, the duration of VA-ECMO support, and any complications detected by clinicians during treatment. Results: The results highlighted the presence of numerous ischemic and haemorrhagic events affecting various organs. Among them, the intestines were particularly vulnerable, even after a short ECMO duration. Conclusions: ECMO was found to accelerate post-mortem decomposition, affecting post-mortem interval estimations, and cardiac damage from reperfusion, underlining the need to meticulously select indications for treatment with VA-ECMO and perform constant clinical evaluations during the treatment itself. Full article

Stator faults are one of the common issues in pumped storage generators, significantly impacting their performance and safety. To ensure the safe and stable operation of pumped storage generators, a stator fault diagnosis method based on an improved short-time Fourier transform (STFT)-support vector data description (SVDD) hybrid algorithm is proposed. This method establishes a fault model for inter-turn short circuits in the stator windings of pumped storage generators and analyzes the electrical and magnetic states associated with such faults. Based on the three-phase current signals observed during an inter-turn short circuit fault in the stator windings, the three-phase currents are first converted into two-phase currents using the principle of equal magnetic potential. Then, the STFT is applied to transform the time-domain signals of the stator’s two-phase currents into frequency-domain signals, and the resulting fault current spectrum is input into the improved SVDD network for processing. This ultimately outputs the diagnosis result for inter-turn short circuit faults in the stator windings of the pumped storage generator. Experimental results demonstrate that this method can effectively distinguish between normal and faulty states in pumped storage generators, enabling the diagnosis of inter-turn short circuit faults in stator windings with low cross-entropy loss. Through analysis, under small data sample conditions, the accuracy of the proposed method in this paper can be improved by up to 7.2%. In the presence of strong noise interference, the fault diagnosis accuracy of the proposed method remains above 90%, and compared to conventional methods, the fault diagnosis accuracy can be improved by up to 6.9%. This demonstrates that the proposed method possesses excellent noise robustness and small sample learning ability, making it effective in complex, dynamic, and noisy environments. Full article

This study presents an advanced data augmentation framework to enhance fault diagnostics in industrial centrifugal pumps using vibration data. The proposed framework addresses the challenge of insufficient defect data in industrial settings by integrating traditional augmentation techniques, such as Gaussian noise (GN) and signal stretching (SS), with advanced models, including Long Short-Term Memory (LSTM) networks, Autoencoders (AE), and Generative Adversarial Networks (GANs). Our approach significantly improves the robustness and accuracy of machine learning (ML) models for fault detection and classification. Key findings demonstrate a marked reduction in false positives and a substantial increase in fault detection rates, particularly in complex operational scenarios where traditional statistical methods may fall short. The experimental results underscore the effectiveness of combining these augmentation techniques, achieving up to a 30% improvement in fault detection accuracy and a 25% reduction in false positives compared to baseline models. These improvements highlight the practical value of the proposed framework in ensuring reliable operation and the predictive maintenance of centrifugal pumps in diverse industrial environments. Full article

A pump-turbine may generate high-amplitude hydraulic excitations during operation, wherein the flow-induced response of the spiral case and concrete is a key factor affecting the stable and safe operation of the unit. The preloading spiral case can enhance the combined bearing capacity of the entire structure, yet there is still limited research on the impact of the preloading pressure on the hydrodynamic response. In this study, the pressure fluctuation characteristics and dynamic behaviors of preloading a steel spiral case and concrete under different preloading pressures at rated operating conditions are analyzed based on fluid–structure interaction theory and contact model. The results show that the dominant frequency of pressure fluctuations in the spiral case is 15 $f_n$, which is influenced by the rotor–stator interaction with a runner rotation of short and long blades. Under preloading pressures of 0.5, 0.7, and 1 times the maximum static head, higher preloading pressures reduce the contact regions, leading to uneven deformation and stress distributions with a near-positive linear correlation. The maximum deformation of the PSSC can reach 2.6 mm, and the stress is within the allowable range. The preloading pressure has little effect on the dominant frequency of the dynamic behaviors in the spiral case (15 $f_n$), but both the maximum and amplitudes of deformation and stress increase with higher preloading pressure. The high-amplitude regions of deformation and stress along the axial direction are located near the nose vane, with maximum values of 0.003 mm and 0.082 MPa, respectively. The contact of concrete is at risk of stress concentrations and cracking under high preloading pressure. The results can provide references for optimizing the structural design and the selection of preloading pressure, which improves operation reliability. Full article