Search Results (751)

Petroleum oily sludge (OLS), a hazardous by-product of the petroleum industry, and high-alkali lignite (HAL), an underutilized low-rank coal, pose significant challenges to sustainable waste management and resource efficiency. This study systematically investigated the combustion behavior, reaction pathways, and gaseous-pollutant-release mechanisms across varying blend ratios, utilizing integrated thermogravimetric-mass spectrometry analysis (TG-MS), interaction analysis, and kinetic modeling. The key findings reveal that co-combustion significantly enhances the combustion performance compared to individual fuels. This is evidenced by reduced ignition and burnout temperatures, as well as an improved comprehensive combustion index. Notably, an interaction analysis revealed coexisting synergistic and antagonistic effects, with the synergistic effect peaking at a blending ratio of 50% OLS due to the complementary properties of the fuels. The activation energy was found to be at its minimum value of 32.5 kJ/mol at this ratio, indicating lower reaction barriers. Regarding gas emissions, co-combustion at a 50% OLS blending ratio reduces incomplete combustion products while increasing CO₂, indicating a more complete reaction. Crucially, sulfur-containing pollutants (SO₂, H₂S) are suppressed, whereas nitrogen-containing emissions (NH₃, NO₂) increase but remain controllable. This study provides novel insights into the synergistic mechanisms between OLS and HAL during co-combustion, offering foundational insights for the optimization of OLS-HAL combustion systems toward efficient energy recovery and sustainable industrial waste management. Full article

This paper proposes a speed-adaptive autonomous driving path-tracking framework based on the soft actor–critic (SAC) and pure pursuit (PP) methods, named the SACPP controller. The framework first analyzes the obstacles around the vehicle and plans an obstacle-free reference path with the minimum curvature using the hybrid A* algorithm. Next, based on the generated reference path, the current state of the vehicle, and the vehicle motor energy efficiency diagram, the optimal speed is calculated in real time, and the vehicle dynamics preview point at the future moment—specifically, the look-ahead distance—is predicted. This process relies on the learning of the SAC network structure. Finally, PP is used to generate the front wheel angle control value by combining the current speed and the predicted preview point. In the second layer, we carefully designed the evaluation function in the tracking process based on the uncertainties and performance requirements that may occur during vehicle driving. This design ensures that the autonomous vehicle can not only quickly and accurately track the path, but also effectively avoid surrounding obstacles, while keeping the motor running in the high-efficiency range, thereby reducing energy loss. In addition, since the entire framework uses a lightweight network structure and a geometry-based method to generate the front wheel angle, the computational load is significantly reduced, and computing resources are saved. The actual running results on the i7 CPU show that the control cycle of the control framework exceeds 100 Hz. Full article

During a volcanic eruption, a large volume of pyroclastic material can be deposited on the roads and roofs of the urban areas near volcanoes. The use of volcanic ash as an additive for the manufacture of bricks provides a solution to the disposal of part of this natural residue and reduces the depletion of a non-renewable natural resource, clayey soil, which brings some environmental and economic advantages. The pore system, compactness, uniaxial compression strength, thermal conductivity, color and durability of bricks without and with the addition of volcanic ash were evaluated through hydric tests, mercury intrusion porosimetry, ultrasound, uniaxial compression tests, IR thermography, spectrophotometry and salt crystallization tests. The purpose of this research is to determine the feasibility of adding 10, 20 and 30% by weight of volcanic ash from La Palma (Canary Islands, Spain) in two grain sizes to produce bricks fired at 800, 950 and 1100 °C. The novelty of this study is to use two sizes of volcanic ash and fire the samples at 1100 °C, which is close to the liquidus temperature of basaltic magmas and allows a high degree of interaction between the volcanic ash and the brick matrix. The addition of fine volcanic ash was found to decrease the porosity of the bricks, although the use of high percentages of coarse volcanic ash resulted in bricks with almost the same porosity as the control samples. The volcanic ash acted as a filler, reducing the number of small pores in the bricks. The presence of vesicles in the volcanic ash reduced the compressive strength and the compactness of the bricks with additives. This reduction was more evident in bricks manufactured with 30% of coarse volcanic ash and fired at 800 and 950 °C, although they still reached the minimum resistance required for their use in construction. No significant differences in thermal conductivity were noticed between the bricks with and without volcanic ash additives, which is crucial in terms of energy savings and the construction of sustainable buildings. At 1100 °C the volcanic ash changed in color from black to red. As a result, the additive blended in better with the matrix of bricks fired at 1100 °C than in those fired at 800 and 950 °C. The bricks with and without volcanic ash and fired at 1100 °C remained intact after the salt crystallization tests. Less salt crystallized in the bricks with volcanic ash and fired at 800 and 950 °C than in the samples without additives, although their low compressive strength made them susceptible to decay. Full article

Trajectory planning is crucial for solar aircraft endurance. The multi-wing morphing solar aircraft can enhance solar energy acquisition through wing deflection, which simultaneously incurs aerodynamic losses, complicating energy coupling and challenging existing planning methods in efficiency and long-term optimization. This study presents an energy-optimal trajectory planning method based on Hierarchical Reinforcement Learning for morphing solar-powered Unmanned Aerial Vehicles (UAVs), exemplified by a Λ-shaped aircraft. This method aims to train a hierarchical policy to autonomously track energy peaks. It features a top-level decision policy selecting appropriate bottom-level policies based on energy factors, which generate control commands such as thrust, attitude angles, and wing deflection angles. Shaped properly by reward functions and training conditions, the hierarchical policy can enable the UAV to adapt to changing flight conditions and achieve autonomous flight with energy maximization. Evaluated through 24 h simulation flights on the summer solstice, the results demonstrate that the hierarchical policy can appropriately switch its bottom-level policies during daytime and generate real-time control commands that satisfy optimal energy power requirements. Compared with the minimum energy consumption benchmark case, the proposed hierarchical policy achieved 0.98 h more of full-charge high-altitude cruise duration and 1.92% more remaining battery energy after 24 h, demonstrating superior energy optimization capabilities. In addition, the strong adaptability of the hierarchical policy to different quarterly dates was demonstrated through generalization ability testing. Full article

Cryosurgery employs extremely low temperatures to destroy abnormal or cancerous tissue. Conventional systems use cryogenic fluids like liquid nitrogen or argon, which pose challenges in handling, cost, and precise temperature control. This study explores thermoelectric (TE) cooling using the Peltier effect as an efficient alternative. A numerical optimization of multi-stage TE coolers using bismuth telluride (Bi₂Te₃) is performed through finite element analysis in COMSOL Multiphysics. Results show that the optimized multi-stage TE system achieves a minimum temperature of −70 °C, a 90 K temperature difference, and 4.0 W cooling power—outperforming single-stage (SS) systems with a maximum ΔT of 73.27 K. The study also investigates the effects of material properties, current density, and geometry on performance. An optimized multi-stage (MS) configuration improves cooling efficiency by 22.8%, demonstrating the potential of TE devices as compact, energy-efficient, and precise solutions for cryosurgical applications. Future work will explore advanced nanomaterials and hybrid systems to further improve performance in biomedical cooling. Full article

Uncoordinated electric vehicle (EV) charging can significantly complicate power system operations. In this paper, we develop a hybrid EV charging method that seamlessly integrates centralized EV charging and distributed control schemes to address EV energy demand challenges. The proposed method includes (1) a centralized day-ahead optimal scheduling mechanism and EV shifting process based on mixed-integer linear programming (MILP) and (2) a distributed control strategy based on a genetic algorithm (GA) that dynamically adjusts the charging rate in real-time grid scenarios. The MILP minimizes energy imbalance at overloaded slots by reallocating EVs based on supply–demand mismatch. By combining full and minimum charging strategies with MILP-based shifting, the method significantly reduces network stress due to EV charging. The centralized model schedules time slots using valley-filling and EV-specific constraints, and the local GA-based distributed control adjusts charging currents based on minimum energy, system availability, waiting time, and a priority index (PI). This PI enables user prioritization in both the EV shifting process and power allocation decisions. The method is validated using demand data on a radial feeder with residential and commercial load profiles. Simulation results demonstrate that the proposed hybrid EV charging framework significantly improves grid-level efficiency and user satisfaction. Compared to the baseline without EV integration, the average-to-peak demand ratio is improved from 61% to 74% at Station-A, from 64% to 80% at Station-B, and from 51% to 63% at Station-C, highlighting enhanced load balancing. The framework also ensures that all EVs receive energy above their minimum needs, achieving user satisfaction scores of 88.0% at Stations A and B and 81.6% at Station C. This study underscores the potential of hybrid charging schemes in optimizing energy utilization while maintaining system reliability and user convenience. Full article

Due to climate change, the electric vehicle (EV) industry is rapidly growing and drawing researchers interest. Driving conditions like mountainous roads, slick surfaces, and rough terrains illuminate the vehicles inherent nonlinearities. Under such scenarios, the behavior of power sources (fuel cell, battery, and super-capacitor), power processing units (converters), and power consuming units (traction motors) deviates from nominal operation. The increasing demand for FCHEVs necessitates control systems capable of handling nonlinear dynamics, while ensuring robust, precise energy distribution among fuel cells, batteries, and super-capacitors. This paper presents a DSMC strategy enhanced with Robust Uniform Exact Differentiators for FCHEV energy management. To optimally tune DSMC parameters, reduce chattering, and address the limitations of conventional methods, a hybrid metaheuristic framework is proposed. This framework integrates moth flame optimization (MFO) with the gravitational search algorithm (GSA) and Fractal Heritage Evolution, implemented through three spiral-based variants: MFOGSAPSO-A (Archimedean), MFOGSAPSO-H (Hyperbolic), and MFOGSAPSO-L (Logarithmic). Control laws are optimized using the Integral of Time-weighted Absolute Error (ITAE) criterion. Among the variants, MFOGSAPSO-L shows the best overall performance with the lowest ITAE for the fuel cell (56.38), battery (57.48), super-capacitor (62.83), and DC bus voltage (4741.60). MFOGSAPSO-A offers the most accurate transient response with minimum RMSE and MAE FC (0.005712, 0.000602), battery (0.004879, 0.000488), SC (0.002145, 0.000623), DC voltage (0.232815, 0.058991), and speed (0.030990, 0.010998)—outperforming MFOGSAPSO, GSA, and PSO. MFOGSAPSO-L further reduces the ITAE for fuel cell tracking by up to 29% over GSA and improves control smoothness. PSO performs moderately but lags under transient conditions. Simulation results conducted under EUDC validate the effectiveness of the MFOGSAPSO-based DSMC framework, confirming its superior tracking, faster convergence, and stable voltage control under transients making it a robust and high-performance solution for FCHEV. Full article

Fed-batch cultivation is a widely used strategy for microbial lipid production, offering flexibility in nutrient control and the potential for high lipid productivity. However, optimizing feeding strategies remains a complex challenge, as it depends on multiple factors, including strain-specific metabolism and process limitations. In this study, we developed a computational framework based on dynamic flux balance analysis and small-scale metabolic models to evaluate and optimize lipid production in Rhodosporidium toruloides strains. We proposed equations to estimate both the carbon and energy source mass feed rate ($F_{in}·s_r$) and its concentration in the feed ($s_r$) based on lipid accumulation targets, and defined minimum feeding flow rate ($F_{in}$) according to process duration. We then assessed the impact of these parameters on commonly used bioprocess metrics—lipid yield, titer, productivity, and intracellular accumulation—across wild-type and engineered strains. Our results showed that the selection of $F_{in}·s_r$ was strongly strain-dependent and significantly influenced strain performance. Moreover, for a given $F_{in}·s_r$, the specific values of $s_r$, and the resulting $F_{in}$, had distinct and non-equivalent effects on performance metrics. This methodology enables the rational pre-selection of feeding strategies and strains, improving resource efficiency and reducing the probability of failed experiments. Full article

In recent years, to achieve “dual carbon” goals, increasing the penetration of renewable energy has become a critical approach in China’s power sector. Power electronic converters play a key role in integrating renewable energy into the power system. Among them, the Dual Active Bridge (DAB) DC-DC converter has gained widespread attention due to its merits, such as galvanic isolation, bidirectional power transfer, and soft switching. It has been extensively applied in microgrids, distributed generation, and electric vehicles. However, with the large-scale integration of stochastic renewable sources and uncertain loads into the grid, DAB converters are required to operate over a wider voltage regulation range and under more complex operating conditions. Conventional control strategies often fail to meet these demands due to their limited soft-switching range, restricted optimization capability, and slow dynamic response. To address these issues, this paper proposes a multi-mode switching optimized control strategy for the three-port DAB (3p-DAB) converter. The proposed method aims to broaden the soft-switching range and optimize the operation space, enabling high-power transfer capability while reducing switching and conduction losses. First, to address the issue of the narrow soft-switching range at medium and low power levels, a single-cycle interleaved phase-shift control mode is proposed. Under this control, the three-phase Dual Active Bridge can achieve zero-voltage switching and optimize the minimum current stress, thereby improving the operating efficiency of the converter. Then, in the face of the actual demand for wide voltage regulation of the converter, a standardized global unified minimum current stress optimization scheme based on the virtual phase-shift ratio is proposed. This scheme establishes a unified control structure and a standardized control table, reducing the complexity of the control structure design and the gain expression. Finally, both simulation and experimental results validate the effectiveness and superiority of the proposed multi-mode optimized control strategy. Full article

This study aimed to investigate the influence of different walking speeds on shoulder and elbow joint kinematics, specifically focusing on range of motion, angular velocity, and angular acceleration during arm swing. The natural rhythm of human gait was studied to develop an effective mechanical interface, particularly with respect to joint impedance and force controllability. The independent variable in this study was walking speed, operationalized at four levels—3.6 km/h (slow), 4.2 km/h (preferred walking speed, PWS), 5.4 km/h (normal), and 7.2 km/h (fast)—and defined as a within-subject factor. The dependent variables consisted of quantitative kinematic parameters, including joint range of motion (ROM, in degrees), peak and minimum joint angular velocity (deg/s), and peak and minimum joint angular acceleration (deg/s²). For each subject, data from twenty gait cycles were extracted for analysis. The kinematic variables of the shoulder and elbow were analyzed, showing increasing trends as the walking speed increased. As walking speed increases, adequate arm swing contributes to gait stability and energy efficiency. Notably, the ROM of shoulder was slightly reduced at the PWS compared to the slowest speed (3.6 km/h), which may reflect more natural and coordinated limb movements at the PWS. Dynamic covariation of torque patterns in the shoulder and elbow joints was observed, reflecting a synergistic coordination between these joints in response to human body movement. Full article

COVID-19 restricted the number of employees. Operational data showed that traditional methods of modeling heat consumption are not correct anymore. The aim is to model the energy demand of an office building during COVID-19 limitations and showcase improvements after a new controller or suggested alternatives are applied. After an actual heat consumption profile was simulated, energy conservation scenarios were considered: the usage of thermostatic radiator valves (TRVs); accounting impacts of solar radiation and wind; changing mass flow rates based on the indoor temperature; adopting an additional control, changing the temperature setpoint; introducing night and day setbacks. After implementing new design and operational methods, the overheating of indoor spaces was alleviated, and the average indoor temperature was reduced from 23.5 °C to 20.4 °C. The annual specific heat consumption decreased to 174 kWh/m² (20.2% lower). The methodology ensured thermal comfort and high energy-saving potential. If operating parameters were adjusted, the total saving effect in energy demand was 119.8 MWh, with an energy-saving rate of 19.8%. Employing TRV-related savings and considering thermal inertia provided more stable indoor temperatures and higher energy performance. The minimum saving effect corresponded to the optimal operation and ensuring the indoor environment by considering wind and the maximum one-to-night setbacks. The fluctuations in indoor temperature became smoother. Full article

The aim of this study was to evaluate the effect of suture material made of polyester (PET), polypropylene (PP), and polytetrafluoroethylene (PTFE) on the calcification of a bovine pericardium (BP) consisting of collagen biopolymer preserved with an epoxy compound. Non-porous film made of the synthetic reinforced polymer REPEREN^® was chosen as a control material. Samples of the material (sutured or non-sutured with each of the three types of surgical sutures) were implanted subcutaneously in 45 young rats for 30, 60, and 90 days. The calcium content of the explants was quantified using atomic absorption spectrometry, a histological examination was performed using hematoxylin and eosin and von Kossa staining, and the structure of the calcium phosphate deposits was studied using scanning electron microscopy (SEM) and energy dispersive spectrometry (EDS) with color field mapping. The results demonstrated the absence of calcification in the non-sutured BP and in all the REPEREN^® groups. In the sutured BP samples, a dynamic increase in the Ca content and the Ca/P ratio to 1.67–1.7 (crystalline hydroxyapatite) was observed by the 90th day. The minimum Ca content among the sutured BP groups was detected in samples where the PET thread was used. The cellular reaction to BP was significantly more pronounced than the reaction to REPEREN^® throughout the entire observation period; collagen homogenization was noted near the sutures. It can be concluded that all the studied suture materials provoke BP calcification. PET has the minimal negative effect. Full article

This paper shows that using renewable energy sources in the power supply of transportation infrastructure is gradually becoming a new trend. Renewable energy systems are already valuable for railway and automotive infrastructure in various countries; however, this use is limited. This paper examines the improvement of control in a grid-connected, hybrid renewable energy system to meet the needs of a railway transportation infrastructure object by utilizing an additional diesel generator in autonomous mode. The aim is to reduce the depth of battery discharge and limit energy consumption from the grid during peak demand hours, considering the wide fluctuations in power consumption of the object and deviations in renewable energy generation relative to the forecast. Additionally, the task of ensuring long-term autonomous operation of the system is addressed. A control system is proposed based on the deviation of the battery’s state of charge relative to a set schedule, which is determined according to the forecast using an additional variable that sets the power consumption limit. This ensures the minimum possible depth of discharge and peak consumption, taking into account the generation of renewable energy sources, with a power-increase factor ranging from 1 to 1.5 relative to the calculated value. In autonomous mode, the task of minimizing energy consumption by the diesel generator is addressed. Solutions have been developed to implement control in grid and autonomous modes with the corresponding calculation algorithm. The system is not sensitive to the load schedule, and the battery’s depth of discharge limitations are maintained even when renewable energy generation is below the forecast by up to 20%. When generating renewable energy sources below the average monthly value in summer, it is possible to maintain a DoD of no less than 60%. Full article

Under the dual imperatives of air pollution control and energy conservation, this study proposes an enhanced optimization framework for combined heat and power (CHP) district heating systems based on bypass thermal storage (BTS). In contrast to conventional centralized tank-based approaches, this method leverages the dynamic hydraulic characteristics of secondary network bypass pipelines to achieve direct sensible heat storage in circulating water, significantly improving system flexibility and energy efficiency. The core innovation lies in addressing the critical yet under-explored issue of control valve dynamic response, which profoundly impacts system operational stability and economic performance. A quality regulation strategy is systematically implemented to stabilize circulation flow rates through temperature modulation by establishing a supply–demand equilibrium model under bypass conditions. To overcome the limitations of traditional feedback control in handling hydraulic transients and heat transfer dynamics in the plate heat exchanger, a Model Predictive Control (MPC) framework is developed, integrating a data-driven valve impedance-opening degree correlation model. This model is rigorously validated against four flow characteristics (linear, equal percentage, quick-opening, and parabolic) and critical impedance parameters (maximum/minimum controllable impedance). This study provides theoretical foundations and technical guidance for optimizing secondary network heating systems, enhancing overall system performance and stability, and promoting energy-efficient development in the heating sector. Full article

Adaptive façades, also known as climate-adaptive building shells (CABSs), could make a significant contribution towards reducing the energy consumption of buildings and their environmental impacts. There is extensive research on glazed adaptive façades, mainly due to the available technology for glass materials. The technological development of opaque adaptive façades has focused on variable-thermal-resistance envelopes, and the simulation of this type of façade is a challenging task that has not been thoroughly studied. The aim of this study was to configure and validate a simplified office model that could be used for simulating an adaptive façade with variable thermal resistance via adaptive insulation thickness in its opaque part. Software-to-software model comparison based on the results of an EnergyPlus Building Energy Simulation Test 900 (BesTest 900)-validated model was used. Cooling and heating annual energy demand (kWh), peak cooling and heating (kW), and maximum, minimum, and average annual hourly zone temperature variables were compared for both the Adaptive and non-adaptive validated model. An Adaptive EnergyPlus model based on the BesTest 900 model, which uses the EnergyPlus SurfaceControl:MovableInsulation class list, was successfully validated and could be used for studying office buildings with a variable-thermal-resistance adaptive façade wall configuration, equivalent to a heavyweight mass wall construction with an External Insulation Finishing System (EIFS). An example of the Adaptive model in the Denver location is included in this paper. Annual savings of up to 26% in total energy demand (heating + cooling) was achieved and could reach up to 54% when electro-chromic (EC) glass commanded by a rule-based algorithm was added to the glazed part of the variable-thermal-resistance adaptive façade. Full article