Search Results (83)

To address the limitations of traditional hardened concrete road surfaces in coal mine tunnels, which are prone to damage and entail high maintenance costs, this study proposes using modular concrete blocks composed of fly ash and coal gangue as an alternative to conventional materials. These blocks offer advantages including ease of construction and rapid, straightforward maintenance, while also facilitating the reuse of substantial quantities of solid waste, thereby mitigating resource wastage and environmental pollution. Initially, the mineral composition of the raw materials was analyzed, confirming that although the physical and chemical properties of Liangshui Well coal gangue are slightly inferior to those of natural crushed stone, they still meet the criteria for use as concrete aggregate. For concrete blocks incorporating 20% fly ash, the steam curing process was optimized with a recommended static curing period of 16–24 h, a temperature ramp-up rate of 20 °C/h, and a constant temperature of 50 °C maintained for 24 h to ensure optimal performance. Orthogonal experimental analysis revealed that fly ash content exerted the greatest influence on the compressive strength of concrete, followed by the additional water content, whereas the aggregate particle size had a comparatively minor effect. The optimal mix proportion was identified as 20% fly ash content, a maximum aggregate size of 20 mm, and an additional water content of 70%. Performance testing indicated that the fabricated blocks exhibited a compressive strength of 32.1 MPa and a tensile strength of 2.93 MPa, with strong resistance to hydrolysis and sulfate attack, rendering them suitable for deployment in weakly alkaline underground environments. Considering the site-specific conditions of the Liangshuijing coal mine, ANSYS 2020 was employed to simulate and analyze the mechanical behavior of the blocks under varying loads, thicknesses, and dynamic conditions. The findings suggest that hexagonal coal gangue blocks with a side length of 20 cm and a thickness of 16 cm meet the structural requirements of most underground mine tunnels, offering a reference model for cost-effective paving and efficient roadway maintenance in coal mines. Full article

The global urgency to mitigate climate change by reducing transport-related emissions drives the accelerated electrification of road freight transport. This paper presents a comprehensive meta-study forecasting the development and corresponding power demand of electric trucks and tractor units in Hamburg up to 2050, emphasizing the shift from conventional to electric vehicles. Utilizing historical registration data and existing commercial and institutional reports from 2007 to 2024, the analysis estimates future distributions of electric heavy-duty vehicles across Hamburg’s 103 city quarters. Distinct approaches are evaluated to explore potential heavy-duty vehicle distribution in the city, employing Mixed-Integer Linear Programming to quantify and minimize distribution uncertainties. Power demand forecasts at this detailed geographical level enable effective infrastructure planning and strategy development. The findings serve as a foundation for Hamburg’s transition to electric heavy-duty vehicles, ensuring a sustainable, efficient, and reliable energy supply aligned with the city’s growing electrification requirements. Full article

Three-dimensional NAND Flash has adopted the cell-over-peripheral (COP) structure to increase storage density. Unlike the conventional structure, the COP structure cannot directly increase the channel potential via substrate bias during the erase operation. Therefore, the gate-induced drain leakage (GIDL) erase method, which utilizes band-to-band tunneling (BTBT) to raise the channel potential, is employed. However, compared to bulk erase, the BTBT-based erase method requires a longer time to generate holes in the channel, leading to erase speed degradation. To address this issue, we propose a structure which enhances the erase speed by surrounding the bitline (BL) PAD with SiGe. In the case of a SiGe thickness (t_SiGe) of 13 nm, the lower bandgap of SiGe increases the BTBT generation rate, boosting the channel potential rise at the end of the erase voltage ramp-up by 861% compared to the Si-only structure, while limiting the reduction in read on-current to within 4%. We modeled the voltage and electric field across the SiGe layer, as well as BTBT generation rate and GIDL current in the SiGe layer, by varying t_SiGe, Ge composition ratio (SiGe_X), and the voltage difference between V_BL and V_{GIDL_TR}. Full article

Under the background of “dual-carbon”, expanding renewable energy grid integration exacerbates grid net load volatility, and system climbing requirements escalate. In this paper, the problem of uncertain ramping demand prediction caused by net load prediction error in new power systems is investigated. First, the total system ramping demand calculation model is constructed, and the effects of deterministic and uncertain ramping demand on the total system ramping demand are analyzed. Secondly, a prediction model based on a CNN-LSTM hybrid neural network is proposed for the uncertain ramp-up demand, which extracts the spatial correlation features of the multi-source influencing factors through the convolutional layer, captures the dynamic evolution law in the time series by using the LSTM layer, and realizes the high-precision point prediction and reliable interval prediction by combining the quantile regression method. Finally, the actual operation data and forecast data of a provincial power grid are used for example verification, and the results show that the proposed model outperformed traditional models (SVM, RF, BPNN) and single deep learning models (CNN, LSTM) in point prediction performance, achieving higher prediction accuracy and validating the effectiveness of the spatio-temporal feature extraction module. In terms of interval prediction quality, compared with the histogram and QRF benchmark models, the proposed model achieves a significant reduction in the average width of the prediction interval, average upward ramp-up demand, and average downward ramp-down demand while maintaining 100% interval coverage. This demand realizes a better balance between prediction economic efficiency and safety, providing more reliable technical support for the precise assessment of uncertain ramp-up demand in new power systems. Full article

In integrated permanent magnet synchronous motors (IPMSMs) coupled with mechanical devices such as ball screws and reducers, complex nonlinear friction characteristics often arise, leading to asymmetrical distortions such as position “flat-top” and speed “ramp-up”. These phenomena significantly degrade the system’s positioning accuracy. To address this issue, this paper introduces a symmetry-inspired nonlinear predictive speed control approach based on the Stribeck piecewise linearized friction compensation and a generalized proportional integral (GPI) observer. The proposed method leverages the inherent symmetry in the Stribeck friction model to describe the nonlinear behavior, employing online piecewise linearization via the least squares method. A GPI observer was designed to estimate the lumped disturbance, including time-varying components in the speed dynamics, friction model deviations, and external loads. By incorporating these estimates, a nonlinear predictive controller was developed, employing a quadratic cost function to derive the optimal control law. The experimental results demonstrate that, compared to traditional integral NPC and PI controllers, the proposed method effectively restores system symmetry by eliminating the “flat-top” and “ramp-up” distortions while maintaining computational efficiency. Full article

Traditional coal-fired power units are required to improve their operational flexibility to accommodate increasing renewable energy. In this paper, an optimized operation approach of the cold-end system is proposed to improve the flexibility of coal-fired power units. The dynamic models of the cold-end system of a 330 MW coal-fired power unit are developed. The model validation results show that the error between the simulated results and measured values is <3% at the common load range and <5% at the low load range. The applications of cold-end optimization in the load-variation processes with ±3% Pe/min ramps and actual automatic generation control (AGC) response are then studied. The results show that when the back pressure of the unit is relatively low, the cold-end optimization is more effective in improving the ramp-down rate. On the contrary, when the unit operates with relatively high back pressure, this approach is more suitable for improving the ramp-up rate. Moreover, the AGC response quality is noticeably enhanced, which improves the phenomenon of overshooting and reverse regulation. The comprehensive performance indicator K_P increased from 2.27 to 4.63 in the summer scenario, while it increased from 2.08 to 4.34 in the winter scenario. Moreover, the profits under the two scenarios are raised by 39.2% and 42.5%, respectively. The findings of this study are also applicable to supercritical units or other power units with the cold end adopting similar water cooling systems. Future work will incorporate advanced control theories to enhance control robustness, which is critical for the practical implementation of the proposed cold-end optimization approach. Full article

The current research presents an extension of the Pit-Stop Manufacturing framework. It addresses the challenges of managing complexity and uncertainty in the production ramp-up phase of manufacturing systems, bridging the gap in existing approaches that lack comprehensive, quantitative, and system-level solutions. This research integrates state-of-the-art methodologies, utilising such metrics as Overall Equipment Effectiveness and Effective Throughput Loss to enhance ramp-up management. The developed framework is represented by a conceptual model, which is translated into a digital product combining multiple artefacts for comprehensive ramp-up research, namely a digital twin of the production system, a Custom Experiment Manager for multiple simulation runs, and a Graph Solver that uses the stochastic dynamic programming approach to address the decision-making issues during the production system ramp-up evolution. This work provides a robust decision-support tool to optimise production transitions under dynamic conditions by combining stochastic dynamic programming and discrete event simulation. The framework enables manufacturers to model, simulate, and optimise system evolution, reducing throughput losses, improving equipment efficiency, and enhancing decision-making precision. This paper demonstrates the framework’s potential to streamline ramp-up processes and boost competitiveness in volatile manufacturing environments. Full article

Split air conditioning units are crucial for ensuring the thermal comfort of buildings. Conventional scheduling or pre-timed system activities result in high consumption and wasted energy. This study proposes an intelligent control system for automatic setpoint adjustment in an educational building based on real-time indoor and outdoor environmental and room occupancy data. Principal component analysis was used to identify energy consumption components in ramp-up and steady-state power usage scenarios. K-means clustering was then used to categorize environmental scenarios and occupancy patterns to identify operational states, predict power consumption and environmental variables, and generate fuzzy inference system rules. The application of rough set theory eliminated rule redundancy by at least 99.27% and enhanced computational speed by 96.40%. After testing using real historical data from an uncontrolled environment and occupant thermal comfort satisfaction surveys reflecting a range of ACU setpoints, the enhanced inference system achieved daily average power savings of 25.56% and a steady-state power period at 63.24% of the ACU operating time, as compared to conventional and variable setpoint operations. The proposed technique provides a basis for dynamic and data-driven decision-making, enabling sustainable energy management in smart building applications. Full article

This study presents an experimental analysis of a turbocharger with semi-floating ring bearings, focusing on hysteresis in subsynchronous vibrations. Four automotive oils (SAE 0W-20, SAE 0W-30, SAE 5W-30, SAE 5W-40) were tested across six oil inlet temperatures from 20 °C to 120 °C during ramp-up and ramp-down cycles to examine the effects of lubricant viscosity and temperature on rotor dynamics. Hysteresis and bifurcation points were observed at distinct rotational speeds in both directions, with subsynchronous components providing insights into rotor–lubrication interactions. This study applies the concept of hysteresis loop width for turbocharger rotors, highlighting its nonlinear dependence on oil temperature, an unexpected and unexplained phenomenon. Additionally, the results suggest that vibration sensors could provide real-time feedback on oil supply conditions, offering potential enhancements for turbochargers and other rotating machinery. Full article

A hemicellulose-first approach can offer advantages for biorefineries utilizing wheat straw as it combines lignocellulose fractionation and potentially higher added value from pentose-based hemicellulose. Therefore, a tailored hydrothermal concept for the production of xylooligosaccharides and xylan was investigated. The focus was on assessing the energy requirements and potential improvements based on experimental results. The wheat straw pretreatment and the downstream processing of hemicellulose hydrolysate were modeled at a scale of 30,000 tons of wheat straw dry mass per year. The results confirmed that the hydrothermal concept can be implemented in an energy-efficient manner without the need for additional auxiliaries, due to targeted process design, heat integration and a high solids loading during hydrolysis. The resulting specific energy requirements for pretreatment and hydrolysate processing are 0.28 kWh/kg and 0.13 kWh/kg of wheat straw dry mass, respectively. Compared to thermal hydrolysate processing alone, the combination of a multi-effect evaporator and pressure-driven ultrafiltration can reduce the heating and cooling energy by 29% and 44%, respectively. However, the ultrafiltration requirements (e.g., electrical energy, membrane area and costs) depend heavily on the properties of the hydrolysate and its interactions with the membrane. This work can contribute to the commercially viable ramp-up of wheat straw multi-product biorefineries. Full article

Nickel (Ni) single-layer coatings were electrodeposited under varying pulse periods (T), duty cycles (θ), and average current densities (i_av) using four distinct pulse current waveforms: rectangular (Rec), triangular (Tri), ramp-up triangular (Rup), and ramp-down triangular (Rdn). This study demonstrated, through dynamic polarization curves and surface morphology analysis, that single-layer coatings showed relatively good corrosion resistance when deposited at shorter pulse periods, larger duty cycles, and higher average current densities. Moreover, compared with other pulse current waveforms, single-layer coatings electrodeposited at T = 10 ms, θ = 0.5, and i_av = 10 mA/cm², 20 mA/cm², and 40 mA/cm² with Rdn had similar dynamic polarization curves and relatively good corrosion resistance. Consequently, two pulse current combinations, the descending gradient and convex gradient, were introduced for electrodepositing Ni multilayer coatings. Analysis revealed that the corrosion resistance of coatings deposited with the convex gradient current was further enhanced. Full article

Background: Even though venetoclax in combination with azacitidine (VenAza) is considered a low-intensity regimen, its patients present a high incidence of cytopenia and infections during the first courses, making the initial management a challenging phase. Methods: This difficulty in our center led to the establishment of an At-Home (AH) program for ramp-up and follow-up patients during the VenAza combination induction phase focused on therapy administration, patient and caregiver education, and management of adverse events (AEs). A total of 70 patients with newly diagnosed acute myeloid leukemia (ND-AML) or relapsed/refractory AML (R/R AML) were treated with VenAza from March 2019 to May 2022. We compared outcomes between patients managed with a hospital-based (inpatient) approach and those managed through the AH program. Results: Despite most patients experiencing grade 3–4 cytopenias (96.9%), the incidence of serious infections and other AEs was comparable between both groups, with no significant difference in febrile neutropenia (42.3% vs. 27.8%, p = 0.38). Overall, the AH cohort demonstrated a significantly lower hospital readmission rate after ramp-up (29.5% vs. 84.6%, p = 0.001). Moreover, the inpatient cohort’s admission days were longer than in the AH cohort (13 vs. 8, p = 0.28). Conclusions: AH management was feasible and safe, leading to better resource use, enhanced patient comfort, and improved treatment compliance. The potential of AH programs for managing low-intensity chemotherapy regimens can reduce hospital admissions and subsequently improve patient and caregiver well-being. Full article

In response to the urgent threat of climate change and the drivers of high greenhouse gas emissions, countries worldwide are adopting policies to reduce their carbon emissions, with net-zero emissions targets. These targets vary by region, with Canada aiming to achieve net-zero emissions by 2050. In response to the Independent Electricity System Operator’s (IESO’s) “Pathways to Decarbonization” report, which evaluates a proposed moratorium on new natural gas generating stations, this study presents a methodology to support energy transitions in Ontario by using a modified Dynamic Integrated Climate-Economy (DICE) model, which focuses on replacing fossil fuel power plants (FFPPs) with clean energy sources, including nuclear, solar, wind, and hydro. This research expands on our prior work that used the DICE model to evaluate the potential for replacing FFPPs with Small Modular Reactors (SMRs) on a global scale. This study includes solar, wind, hydro, and SMRs to provide a diversified clean energy portfolio and integrates fuzzy logic to optimize construction rates and address uncertainties. The study uses Ontario as a case study, aligning with IESO’s objectives for Ontario’s energy transition. The IESO’s projections for net zero by 2050 are applied. The study is extended to 2100 to assess the longer-term implications of sustained energy transition efforts beyond the immediate goals set by the IESO. This approach is scalable to other regions and countries with similar energy transition challenges. The study results indicate that to meet Ontario’s 2050 net-zero target, approximately 183 SMR units, 1527 solar units, 289 wind units, and 449 hydro units need to be constructed. For the 2100 target, the required number of units is slightly higher due to the longer time frame, reflecting a gradual ramp-up in construction. The optimization of construction rates using fuzzy logic shows that the pace of deployment is influenced by critical factors such as resource availability, policy support, and public acceptance. This underscores the need for accelerated clean energy deployment to meet long-term emissions reduction goals. The findings highlight the complexities of transitioning to a low-carbon energy system and the importance of addressing uncertainties in planning. Policymakers are urged to integrate these insights into strategic energy planning to ensure the successful deployment of clean energy technologies. This study provides valuable recommendations for optimizing energy transitions through a robust, flexible framework that accounts for both technological and socio-economic challenges. Full article

The general-purpose Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC) at CERN is used to study the production of new particles in proton–proton collisions at an LHC center of mass energy of 13.6 TeV. The detector includes a magnet based on a 6 m diameter superconducting solenoid coil operating at a current of 18.164 kA. This current creates a central magnetic flux density of 3.8 T that allows for the high-precision measurement of the momenta of the produced charged particles using tracking and muon subdetectors. The CMS magnet contains a 10,000 ton flux-return yoke of dodecagonal shape made from the assembly of construction steel blocks distributed in several layers. These steel blocks are magnetized with the solenoid returned magnetic flux and wrap the muons escaping the hadronic calorimeters of total absorption. To reconstruct the muon trajectories, and thus to measure the muon momenta, the drift tube and cathode strip chambers are located between the layers of the steel blocks. To describe the distribution of the magnetic flux in the magnet yoke layers, a three-dimensional computer model of the CMS magnet is used. To validate the calculations, special measurements are performed, with the flux loops wound in 22 cross-sections of the flux-return yoke blocks. The measured voltages induced in the flux loops during the CMS magnet ramp-ups and -downs, as well as during the superconducting coil fast discharges, are integrated over time to obtain the initial magnetic flux densities in the flux loop cross-sections. The measurements obtained during the seven standard ramp-downs of the magnet were analyzed in 2018. From that time, three fast discharges occurred during the standard ramp-downs of the magnet. This allows us to single out the contributions of the eddy currents, induced in steel, to the flux loop voltages registered during the fast discharges of the coil. Accounting for these contributions to the flux loop measurements during intentionally triggered fast discharges in 2006 allows us to perform the validation of the CMS magnet computer model with better precision. The technique for the flux loop measurements and the obtained results are presented and discussed. The method for measuring magnetic flux density in steel blocks described in this study is innovative. The experience of 3D modeling and measuring the magnetic field in steel blocks of the magnet yoke, as part of a muon detector system, has good prospects for use in the construction and operation of particle detectors for the Future Circular Electron–Positron Collider and the Circular Electron–Positron Collider. Full article

Manufacturing companies are increasingly challenged to deliver customizable products with shorter time to market and higher quality while adhering to sustainability requirements. To meet these challenges, the frequency and importance of production ramp-ups will increase in the future. However, most ramp-ups still fail to meet targets due to unpredictable equipment failures, operator errors, and system complexity. We propose a system dynamics model that captures the unique dynamics of ramp-up phases by integrating stability and disturbance factors that influence the key performance indicators overall equipment effectiveness, process capability, and production output. A systematic literature review informed the identification of stability factors, which were validated through expert interviews in the automotive industry. Our system dynamic simulation results indicate that control factors realistically influence production system behaviour during different ramp-up phases. Despite some limitations regarding the effects of maintenance personnel and engineering changes on key performance indicators, our model effectively simulates realistic ramp-up behaviour. The findings highlight the need for tailored models that consider specific ramp-up contexts and emphasize the importance of data acquisition for enhanced performance prognosis in future research. Full article