Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainabili...Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty.展开更多
To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy...To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.展开更多
Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian O...Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian Optimization utilizes Gaussian Process (GP) as its core model and employs an adaptive search strategy to efficiently explore and identify optimal combinations of operational parameters within a limited parameter space, thereby enabling rapid optimization of the required parameters to achieve the target droplet size. Traditional methods typically rely on manually selecting a series of operational parameters and conducting multiple simulations to gradually approach the target droplet size. This process is time-consuming and prone to getting trapped in local optima. In contrast, Bayesian Optimization adaptively adjusts its search strategy, significantly reducing computational costs and effectively exploring global optima, thus greatly improving optimization efficiency. Additionally, the study investigates the impact of rectangular rib structures within the T-junction microchannel on droplet generation, revealing how the channel geometry influences droplet formation and size. After determining the target droplet size, we further applied Bayesian Optimization to refine the rib geometry. The integration of Bayesian Optimization with computational fluid dynamics (CFD) offers a promising tool and provides new insights into the optimal design of microfluidic devices.展开更多
In this research work,the localized generation from renewable resources and the distribution of energy to agricultural loads,which is a local microgrid concept,have been considered,and its feasibility has been assesse...In this research work,the localized generation from renewable resources and the distribution of energy to agricultural loads,which is a local microgrid concept,have been considered,and its feasibility has been assessed.Two dispatch algorithms,named Cycle Charging and Load Following,are implemented to find the optimal solution(i.e.,net cost,operation cost,carbon emission.energy cost,component sizing,etc.)of the hybrid system.The microgrid is also modeled in the DIgSILENT Power Factory platform,and the respective power system responses are then evaluated.The development of dispatch algorithms specifically tailored for agricultural applications has enabled to dynamically manage energy flows,responding to fluctuating demands and resource availability in real-time.Through careful consideration of factors such as seasonal variations and irrigation requirements,these algorithms have enhanced the resilience and adaptability of the microgrid to dynamic operational conditions.However,it is revealed that both approaches have produced the same techno-economic results showing no significant difference.This illustrates the fact that the considered microgrid can be implemented with either strategy without significant fluctuation in performance.The study has shown that the harmful gas emission has also been limited to only 17,928 kg/year of CO_(2),and 77.7 kg/year of Sulfur Dioxide.For the proposed microgrid and load profile of 165.29 kWh/day,the net present cost is USD 718,279,and the cost of energy is USD 0.0463 with a renewable fraction of 97.6%.The optimal sizes for PV,Bio,Grid,Electrolyzer,and Converter are 1494,500,999,999,500,and 495 kW,respectively.For a hydrogen tank(HTank),the optimal size is found to be 350 kg.This research work provides critical insights into the techno-economic feasibility and environmental impact of integrating biomass-PV-hydrogen storage-Grid hybrid renewable microgrids into agricultural settings.展开更多
Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help...Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.展开更多
Carbon dioxide-enhanced oil recovery(CO_(2)-EOR)and storage is recognized as an economically feasible technique if used in suitable reservoirs.The type or form and capacity of this CO_(2) sequestration technique is sy...Carbon dioxide-enhanced oil recovery(CO_(2)-EOR)and storage is recognized as an economically feasible technique if used in suitable reservoirs.The type or form and capacity of this CO_(2) sequestration technique is synergistically affected by heat,flow,stress,and chemical reactions.Aimed at addressing the technological issues in applying CO_(2)-EOR and storage in a high water-cut reservoir in Xinjiang,China,this paper proposes a thermo-hydro-mechanical-chemical coupling method during CO_(2) flooding.The potential of CO_(2) sequestration and EOR in the target reservoir is discussed in combination with the surrogate optimization method.This method works better as it considers the evolution of structural trapping,capillary trapping,solubility trapping,and mineral trapping during CO_(2) injection as well as the influence the physical field has on the sequestration capacity for different forms of CO_(2) sequestration.The main mechanisms of CO_(2) sequestration in the high water-cut reservoir is structural trapping,followed by capillary trapping.Solubility trapping and mineral trapping have less contribution to the total sequestration capacity of CO_(2).After optimization,the cumulative oil production was 2.36×10^(6)m^(3),an increase of 0.25×10^(6)m3or 11.9%compared to the pre-optimization value.The CO_(2) sequestration capacity after optimization was 1.39×10^(6)t,which is an increase of 0.23×10^(6)t compared to values obtained before optimization;this effectively increases the area affected by CO_(2) by 24.4%.Of the four trapping mechanisms,capillary trapping and structural trapping showed a high increase of 32.5%and17.28%,respectively,while solubility trapping and mineral trapping only led to an increase of 5.1%and0.43%,respectively.This research could provide theoretical support for fully utilizing the potential of CO_(2)-EOR and sequestration technology.展开更多
With the rising global demand for energy and growing awareness of environmental sustainability,hydrogen energy has emerged as a promising clean and efficient alternative.Supported by national policies,both basic and a...With the rising global demand for energy and growing awareness of environmental sustainability,hydrogen energy has emerged as a promising clean and efficient alternative.Supported by national policies,both basic and applied research in hydrogen and hydrogen energy have seen significant advancements in recent years.Reflecting these developments,the teaching of“hydrogen element and hydrogen energy”in college level inorganic chemistry has gradually expanded.In the context of the new era,there is an urgent need to reform and enrich this teaching content to cultivate students’comprehensive abilities and align with the country’s evolving demand for talent in the energy sector.This paper analyzes current challenges in the teaching of hydrogen energy within college chemistry curricula and proposes targeted strategies to optimize instructional content.The goal is to offer practical insights and references for educators seeking to improve the effectiveness and relevance of hydrogen energy education.展开更多
In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the op...In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.展开更多
The growing demand for carbon neutrality has heightened the focus on CO_(2)hydrogenation as a viable strategy for transforming carbon dioxide into valuable chemicals and fuels.Advanced machine learning(ML)approaches i...The growing demand for carbon neutrality has heightened the focus on CO_(2)hydrogenation as a viable strategy for transforming carbon dioxide into valuable chemicals and fuels.Advanced machine learning(ML)approaches integrate materials science with artificial intelligence,enabling scientists to identify hidden patterns in datasets,make informed decisions,and reduce the need for labor-intensive,repetitive experimentation.This review provides a comprehensive overview of ML applications in the thermocatalytic hydrogenation of CO_(2).Following an introduction to ML tools and workflows,various ML algorithms employed in CO_(2)hydrogenation are systematically categorized and reviewed.Next,the application of ML in catalyst discovery is discussed,highlighting its role in identifying optimal compositions and structures.Then,ML-driven strategies for process optimization,particularly in enhancing CO_(2)conversion and product selectivity,are examined.Studies modeling descriptors,spanning catalyst properties and reaction conditions,to predict catalytic performance are analyzed.Consequently,ML-based mechanistic studies are reviewed to elucidate reaction pathways,identify key intermediates,and optimize catalyst performance.Finally,key challenges and future perspectives in leveraging ML for advancing CO_(2)hydrogenation research are presented.展开更多
This paper explores a UAV-mounted active Reconfigurable Intelligent Surface(aRIS)network designed to enhance secure downlink communication for multiple users while mitigating the impact of multiple Eavesdroppers(EVs)....This paper explores a UAV-mounted active Reconfigurable Intelligent Surface(aRIS)network designed to enhance secure downlink communication for multiple users while mitigating the impact of multiple Eavesdroppers(EVs).The focus is on optimizing the UAV’s trajectory,the Base Station’s(BS)transmit beamforming,and the power-Amplified Programmable Reflecting Elements(APREs)of the aRIS to maximize the minimum secrecy rate in the presence of EVs.This is a complex non-convex problem due to multiple optimization variables,high-dimensional matrix operations,and log-determinant objective functions,which makes it challenging to solve.Hence,a Successive Convex Approximation(SCA)-based optimization strategy is developed to efficiently solve the subproblems related to the UAV’s trajectory,aRIS’s APREs,and BS’s beamforming.By leveraging slack variables and approximation techniques,we solve the nonconvex subproblems by a sequence of convex subproblems.Simulation results demonstrate that the proposed UAV-aRIS network significantly outperforms its passive RIS counterpart in improving communication security,highlighting the effectiveness of the optimization strategy.展开更多
The park-level integrated energy system(PIES)is essential for achieving carbon neutrality by managing multi-energy supply and demand while enhancing renewable energy integration.However,current carbon trading mechanis...The park-level integrated energy system(PIES)is essential for achieving carbon neutrality by managing multi-energy supply and demand while enhancing renewable energy integration.However,current carbon trading mechanisms lack sufficient incentives for emission reductions,and traditional optimization algorithms often face challenges with convergence and local optima in complex PIES scheduling.To address these issues,this paper introduces a low-carbon dispatch strategy that combines a reward-penalty tiered carbon trading model with P2G-CCS integration,hydrogen utilization,and the Secretary Bird Optimization Algorithm(SBOA).Key innovations include:(1)A dynamic reward-penalty carbon trading mechanism with coefficients(μ=0.2,λ=0.15),which reduces carbon trading costs by 47.2%(from$694.06 to$366.32)compared to traditional tiered models,incentivizing voluntary emission reductions.(2)The integration of P2G-CCS coupling,which lowers natural gas consumption by 41.9%(from$4117.20 to$2389.23)and enhances CO_(2) recycling efficiency,addressing the limitations of standalone P2G or CCS technologies.(3)TheSBOA algorithm,which outperforms traditionalmethods(e.g.,PSO,GWO)in convergence speed and global search capability,avoiding local optima and achieving 24.39%faster convergence on CEC2005 benchmark functions.(4)A four-energy PIES framework incorporating electricity,heat,gas,and hydrogen,where hydrogen fuel cells and CHP systems improve demand response flexibility,reducing gas-related emissions by 42.1%and generating$13.14 in demand response revenue.Case studies across five scenarios demonstrate the strategy’s effectiveness:total operational costs decrease by 14.7%(from$7354.64 to$6272.59),carbon emissions drop by 49.9%(from 5294.94 to 2653.39kg),andrenewable energyutilizationincreases by24.39%(from4.82%to8.17%).These results affirmthemodel’s ability to reconcile economic and environmental goals,providing a scalable approach for low-carbon transitions in industrial parks.展开更多
Combining high precision numerical analysis methods with optimization algorithms to make a systematic exploration of a design space has become an important topic in the modern design methods. During the design process...Combining high precision numerical analysis methods with optimization algorithms to make a systematic exploration of a design space has become an important topic in the modern design methods. During the design process of an underwater glider＇s flying-wing structure, a surrogate model is introduced to decrease the computation time for a high precision analysis. By these means, the contradiction between precision and efficiency is solved effectively. Based on the parametric geometry modeling, mesh generation and computational fluid dynamics analysis, a surrogate model is constructed by adopting the design of experiment （DOE） theory to solve the multi-objects design optimization problem of the underwater glider. The procedure of a surrogate model construction is presented, and the Gaussian kernel function is specifically discussed. The Particle Swarm Optimization （PSO） algorithm is applied to hydrodynamic design optimization. The hydrodynamic performance of the optimized flying-wing structure underwater glider increases by 9.1%.展开更多
Effects of hydrocarbon compositions on raw exhaust emissions and combustion processes were studied on an engine test bench. The optimization of gasoline hydrocarbon composition was discussed. As olefins content increa...Effects of hydrocarbon compositions on raw exhaust emissions and combustion processes were studied on an engine test bench. The optimization of gasoline hydrocarbon composition was discussed. As olefins content increased from 10.0% to 25.0% in volume, the combustion duration was shortened by about 2 degree crank angle （°CA）, and the engine-out THC emission was reduced by about 15%. On the other hand, as aromatics content changed from 35.0% to 45.0%, the engine-out NOx emissions increased by 4%. An increment in olefins content resulted in a slight increase in engine-out CO emission, while the aromatics content had little effect on engine-out total hydrocarbon （THC） and CO emissions. Over the new European driving cycle （NEDC）, the THC, NOx and CO emissions of fuel with 25.0% olefins and 35.0% aromatics were about 45%, 21% and 19% lower than those of fuel with 10.0% olefins and 40.0% aromatics, respectively. The optimized gasoline compositions for new engines and new vehicles have low aromatics and high olefins contents.展开更多
Drop-on-demand (DOD) bioprinting has been widely used in tissue engineering due to its highthroughput efficiency and cost effectiveness. However, this type of bioprinting involves challenges such as satellite generati...Drop-on-demand (DOD) bioprinting has been widely used in tissue engineering due to its highthroughput efficiency and cost effectiveness. However, this type of bioprinting involves challenges such as satellite generation, too-large droplet generation, and too-low droplet speed. These challenges reduce the stability and precision of DOD printing, disorder cell arrays, and hence generate further structural errors. In this paper, a multi-objective optimization (MOO) design method for DOD printing parameters through fully connected neural networks (FCNNs) is proposed in order to solve these challenges. The MOO problem comprises two objective functions: to develop the satellite formation model with FCNNs;and to decrease droplet diameter and increase droplet speed. A hybrid multi-subgradient descent bundle method with an adaptive learning rate algorithm (HMSGDBA), which combines the multisubgradient descent bundle (MSGDB) method with Adam algorithm, is introduced in order to search for the Pareto-optimal set for the MOO problem. The superiority of HMSGDBA is demonstrated through comparative studies with the MSGDB method. The experimental results show that a single droplet can be printed stably and the droplet speed can be increased from 0.88 to 2.08 m·s^-1 after optimization with the proposed method. The proposed method can improve both printing precision and stability, and is useful in realizing precise cell arrays and complex biological functions. Furthermore, it can be used to obtain guidelines for the setup of cell-printing experimental platforms.展开更多
According to site-specific environments such as high water pressures, high in-situ stresses and strong rockbursts, the design scheme of the long and deep diversion tunnels at Jinping II hydropower station was optimize...According to site-specific environments such as high water pressures, high in-situ stresses and strong rockbursts, the design scheme of the long and deep diversion tunnels at Jinping II hydropower station was optimized to ensure construction safety. New drainage tunnels were considered. Furthermore, lining structures and grouting pressures were modified during the excavation of tunnels. The construction scheme was updated dynamically based on the complex geological conditions. For instances, the diversion tunnels were first excavated by drilling and blasting method at the first stage of construction, and then by the combination method of tunnel boring machine （TBM） and drilling and blasting, and finally by drilling and blasting method. Through optimized scheme and updated construction scheme, the excavation of diversion tunnel #1 was successfully completed in June, 2011. This paper summarizes the key issues in rock mechanics associated with the construction of the long and deep diversion tunnels at Jinping II hydropower station. The experiences of design and construction obtained from this project could provide reference to similar projects.展开更多
In order to reduce the cost of ABs-type hydrogen storage alloys, effects of substitution of Ce for La （A side） and Fe, Mn, Al for Ni （B side） on structural and electrochemical properties of （LaCe）;（NiFeMnAl）s ...In order to reduce the cost of ABs-type hydrogen storage alloys, effects of substitution of Ce for La （A side） and Fe, Mn, Al for Ni （B side） on structural and electrochemical properties of （LaCe）;（NiFeMnAl）s alloys were studied systematically. To make component uniform and operation easy, uniform design （UD） method was introduced into the study of composition optimization of Co-free Fe-containing ABs-type alloys for the first time. X-ray diffraction （XRD） results showed that the designed alloys were of single CaCus-type structure phase. The replacement of Fe had a severe effect on electrochemical capacity, and the substitution of Fe and A1 had a synergetic action among the unit cell volume, cycling stability and high rate discharge property. Interestingly, it was found that the hydrogen storage alloys with excessively high plateau pressure showed a tilted line in Nyquist plot instead of the semicircle, and the current decayed rapidly to near zero at the beginning of the step in constant potential step （CPS）, indicating that electrochemical impedance spectra （EIS） and CPS cannot accurately measure the electrochemical kinetics process of the hydrogen storage alloys with excessively high plateau pressure.展开更多
The objective of this research was to investigate CO2adsorption capacity of tetraethylenepentamine-functionalized basic-modified calcined hydrotalcite（TEPA/b-c HT）sorbents at atmospheric pressure formed under varyin...The objective of this research was to investigate CO2adsorption capacity of tetraethylenepentamine-functionalized basic-modified calcined hydrotalcite（TEPA/b-c HT）sorbents at atmospheric pressure formed under varying TEPA loading levels,temperatures,sorbent weight to total gaseous flow rate（W/F）ratios and CO2concentrations in the influent gas.The TEPA/b-c HT sorbents were characterized by means of X-ray diffraction（XRD）,Fourier transform infrared spectrometry（FT–IR）,thermal gravimetric analysis（TGA）,Brunauer–Emmet–Teller（BET）analysis of nitrogen（N2）adsorption/desorption and carbon–hydrogen–nitrogen（CHN）elemental analysis.Moreover,a full 2~4factorial design with three central points at a 95%confidence interval was used to screen important factor（s）on the CO2adsorption capacity.It revealed that85.0%variation in the capacity came from the influence of four main factors and the15.0%one was from their interactions.A face-centered central composite design response surface method（FCCCD–RSM）was then employed to optimize the condition,the maximal capacity of 5.5–6.1 mmol/g was achieved when operating with a TEPA loading level of 39%–49%（W/W）,temperature of 76–90℃,W/F ratio of 1.7–2.60（g·sec）/cm^3and CO2concentration of 27%–41%（V/V）.The model fitted sufficiently the experimental data with an error range of±1.5%.From cyclical adsorption/desorption and selectivity at the optimal condition,the 40%TEPA/b-c HT still expressed its effective performance after eight cycles.展开更多
In drilling operation, a large saving in time and money would be achieved by reducing the drilling time, since some of the costs are time-dependent. Drilling time could be minimized by raising the penetration rate. In...In drilling operation, a large saving in time and money would be achieved by reducing the drilling time, since some of the costs are time-dependent. Drilling time could be minimized by raising the penetration rate. In the comparative optimization method, by using the records of the first drilled wells and comparing the criteria like penetration rate, cost per foot and specific energy, the drilling parameters of the next wells being drilled can be optimized in each depth interval. In the mathematical optimization technique, some numerical equations to model the penetration rate, bit wear rate and hydraulics would be used to minimize the drilling cost and time as much as possible and improve the results of the primary comparative optimization. In this research, as a case study the Iranian Khangiran gas field has been evaluated to optimize the drilling costs. A combination of the mentioned optimization techniques resulted in an optimal well which reduced the drilling time and cost considerably in comparison with the wells already drilled.展开更多
The Adaptive Dropped Hinge Flap(ADHF) is a novel trailing edge high-lift device characterized by the integration of downward deflection spoiler and simple hinge flap, with excellent aerodynamic and mechanism performan...The Adaptive Dropped Hinge Flap(ADHF) is a novel trailing edge high-lift device characterized by the integration of downward deflection spoiler and simple hinge flap, with excellent aerodynamic and mechanism performance. In this paper, aerodynamic optimization design of an ADHF high-lift system is conducted considering the mechanism performance. Shape and settings of both takeoff and landing configurations are optimized and analyzed, with considering the kinematic constraints of ADHF mechanism, and the desired optimization results were obtained after optimization. Sensitivity analysis proves the robustness of the optimal design. Comparison shows that the ADHF design has better comprehensive performance of both mechanism and aerodynamics than the conventional Fowler flap and simple hinge flap design.展开更多
基金supported by National Key Research and Development Program(2024YFE0115600).
文摘Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty.
基金Supported by State Grid Zhejiang Electric Power Co.,Ltd.Science and Technology Project Funding(No.B311DS230005).
文摘To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.
基金support from National Key Research and Development Program of China(2023YFC3905400)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA0490102)National Natural Science Foundation of China(22178354,2242100322408374).
文摘Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian Optimization utilizes Gaussian Process (GP) as its core model and employs an adaptive search strategy to efficiently explore and identify optimal combinations of operational parameters within a limited parameter space, thereby enabling rapid optimization of the required parameters to achieve the target droplet size. Traditional methods typically rely on manually selecting a series of operational parameters and conducting multiple simulations to gradually approach the target droplet size. This process is time-consuming and prone to getting trapped in local optima. In contrast, Bayesian Optimization adaptively adjusts its search strategy, significantly reducing computational costs and effectively exploring global optima, thus greatly improving optimization efficiency. Additionally, the study investigates the impact of rectangular rib structures within the T-junction microchannel on droplet generation, revealing how the channel geometry influences droplet formation and size. After determining the target droplet size, we further applied Bayesian Optimization to refine the rib geometry. The integration of Bayesian Optimization with computational fluid dynamics (CFD) offers a promising tool and provides new insights into the optimal design of microfluidic devices.
基金financed by the Ministry of Science and Technology(MOST)Bangladesh under Special Research grant for the FY 2023-24(SRG 232410)Further,the authors extend their appreciation to the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabi for funding this research work through the project number“NBU-FFR-2025-3623-05”。
文摘In this research work,the localized generation from renewable resources and the distribution of energy to agricultural loads,which is a local microgrid concept,have been considered,and its feasibility has been assessed.Two dispatch algorithms,named Cycle Charging and Load Following,are implemented to find the optimal solution(i.e.,net cost,operation cost,carbon emission.energy cost,component sizing,etc.)of the hybrid system.The microgrid is also modeled in the DIgSILENT Power Factory platform,and the respective power system responses are then evaluated.The development of dispatch algorithms specifically tailored for agricultural applications has enabled to dynamically manage energy flows,responding to fluctuating demands and resource availability in real-time.Through careful consideration of factors such as seasonal variations and irrigation requirements,these algorithms have enhanced the resilience and adaptability of the microgrid to dynamic operational conditions.However,it is revealed that both approaches have produced the same techno-economic results showing no significant difference.This illustrates the fact that the considered microgrid can be implemented with either strategy without significant fluctuation in performance.The study has shown that the harmful gas emission has also been limited to only 17,928 kg/year of CO_(2),and 77.7 kg/year of Sulfur Dioxide.For the proposed microgrid and load profile of 165.29 kWh/day,the net present cost is USD 718,279,and the cost of energy is USD 0.0463 with a renewable fraction of 97.6%.The optimal sizes for PV,Bio,Grid,Electrolyzer,and Converter are 1494,500,999,999,500,and 495 kW,respectively.For a hydrogen tank(HTank),the optimal size is found to be 350 kg.This research work provides critical insights into the techno-economic feasibility and environmental impact of integrating biomass-PV-hydrogen storage-Grid hybrid renewable microgrids into agricultural settings.
基金supported by National Key Research and Development Program of China (2023YFB3307800)National Natural Science Foundation of China (Key Program: 62136003, 62373155)+1 种基金Major Science and Technology Project of Xinjiang (No. 2022A01006-4)the Fundamental Research Funds for the Central Universities。
文摘Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.
文摘Carbon dioxide-enhanced oil recovery(CO_(2)-EOR)and storage is recognized as an economically feasible technique if used in suitable reservoirs.The type or form and capacity of this CO_(2) sequestration technique is synergistically affected by heat,flow,stress,and chemical reactions.Aimed at addressing the technological issues in applying CO_(2)-EOR and storage in a high water-cut reservoir in Xinjiang,China,this paper proposes a thermo-hydro-mechanical-chemical coupling method during CO_(2) flooding.The potential of CO_(2) sequestration and EOR in the target reservoir is discussed in combination with the surrogate optimization method.This method works better as it considers the evolution of structural trapping,capillary trapping,solubility trapping,and mineral trapping during CO_(2) injection as well as the influence the physical field has on the sequestration capacity for different forms of CO_(2) sequestration.The main mechanisms of CO_(2) sequestration in the high water-cut reservoir is structural trapping,followed by capillary trapping.Solubility trapping and mineral trapping have less contribution to the total sequestration capacity of CO_(2).After optimization,the cumulative oil production was 2.36×10^(6)m^(3),an increase of 0.25×10^(6)m3or 11.9%compared to the pre-optimization value.The CO_(2) sequestration capacity after optimization was 1.39×10^(6)t,which is an increase of 0.23×10^(6)t compared to values obtained before optimization;this effectively increases the area affected by CO_(2) by 24.4%.Of the four trapping mechanisms,capillary trapping and structural trapping showed a high increase of 32.5%and17.28%,respectively,while solubility trapping and mineral trapping only led to an increase of 5.1%and0.43%,respectively.This research could provide theoretical support for fully utilizing the potential of CO_(2)-EOR and sequestration technology.
文摘With the rising global demand for energy and growing awareness of environmental sustainability,hydrogen energy has emerged as a promising clean and efficient alternative.Supported by national policies,both basic and applied research in hydrogen and hydrogen energy have seen significant advancements in recent years.Reflecting these developments,the teaching of“hydrogen element and hydrogen energy”in college level inorganic chemistry has gradually expanded.In the context of the new era,there is an urgent need to reform and enrich this teaching content to cultivate students’comprehensive abilities and align with the country’s evolving demand for talent in the energy sector.This paper analyzes current challenges in the teaching of hydrogen energy within college chemistry curricula and proposes targeted strategies to optimize instructional content.The goal is to offer practical insights and references for educators seeking to improve the effectiveness and relevance of hydrogen energy education.
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.
文摘The growing demand for carbon neutrality has heightened the focus on CO_(2)hydrogenation as a viable strategy for transforming carbon dioxide into valuable chemicals and fuels.Advanced machine learning(ML)approaches integrate materials science with artificial intelligence,enabling scientists to identify hidden patterns in datasets,make informed decisions,and reduce the need for labor-intensive,repetitive experimentation.This review provides a comprehensive overview of ML applications in the thermocatalytic hydrogenation of CO_(2).Following an introduction to ML tools and workflows,various ML algorithms employed in CO_(2)hydrogenation are systematically categorized and reviewed.Next,the application of ML in catalyst discovery is discussed,highlighting its role in identifying optimal compositions and structures.Then,ML-driven strategies for process optimization,particularly in enhancing CO_(2)conversion and product selectivity,are examined.Studies modeling descriptors,spanning catalyst properties and reaction conditions,to predict catalytic performance are analyzed.Consequently,ML-based mechanistic studies are reviewed to elucidate reaction pathways,identify key intermediates,and optimize catalyst performance.Finally,key challenges and future perspectives in leveraging ML for advancing CO_(2)hydrogenation research are presented.
基金co-supported by Technology Key Project of Guangdong Province,China(No.HZJBGS-2021001)the Shanghai Sailing Scholar,China(No.23YF1412700)+1 种基金the National Natural Science Foundation of China(No.61901254)the Shanghai Technical Service Computing Center of Science and Engineering,Shanghai University,China.
文摘This paper explores a UAV-mounted active Reconfigurable Intelligent Surface(aRIS)network designed to enhance secure downlink communication for multiple users while mitigating the impact of multiple Eavesdroppers(EVs).The focus is on optimizing the UAV’s trajectory,the Base Station’s(BS)transmit beamforming,and the power-Amplified Programmable Reflecting Elements(APREs)of the aRIS to maximize the minimum secrecy rate in the presence of EVs.This is a complex non-convex problem due to multiple optimization variables,high-dimensional matrix operations,and log-determinant objective functions,which makes it challenging to solve.Hence,a Successive Convex Approximation(SCA)-based optimization strategy is developed to efficiently solve the subproblems related to the UAV’s trajectory,aRIS’s APREs,and BS’s beamforming.By leveraging slack variables and approximation techniques,we solve the nonconvex subproblems by a sequence of convex subproblems.Simulation results demonstrate that the proposed UAV-aRIS network significantly outperforms its passive RIS counterpart in improving communication security,highlighting the effectiveness of the optimization strategy.
基金funded by State Grid Beijing Electric Power Company Technology Project,grant number 520210230004.
文摘The park-level integrated energy system(PIES)is essential for achieving carbon neutrality by managing multi-energy supply and demand while enhancing renewable energy integration.However,current carbon trading mechanisms lack sufficient incentives for emission reductions,and traditional optimization algorithms often face challenges with convergence and local optima in complex PIES scheduling.To address these issues,this paper introduces a low-carbon dispatch strategy that combines a reward-penalty tiered carbon trading model with P2G-CCS integration,hydrogen utilization,and the Secretary Bird Optimization Algorithm(SBOA).Key innovations include:(1)A dynamic reward-penalty carbon trading mechanism with coefficients(μ=0.2,λ=0.15),which reduces carbon trading costs by 47.2%(from$694.06 to$366.32)compared to traditional tiered models,incentivizing voluntary emission reductions.(2)The integration of P2G-CCS coupling,which lowers natural gas consumption by 41.9%(from$4117.20 to$2389.23)and enhances CO_(2) recycling efficiency,addressing the limitations of standalone P2G or CCS technologies.(3)TheSBOA algorithm,which outperforms traditionalmethods(e.g.,PSO,GWO)in convergence speed and global search capability,avoiding local optima and achieving 24.39%faster convergence on CEC2005 benchmark functions.(4)A four-energy PIES framework incorporating electricity,heat,gas,and hydrogen,where hydrogen fuel cells and CHP systems improve demand response flexibility,reducing gas-related emissions by 42.1%and generating$13.14 in demand response revenue.Case studies across five scenarios demonstrate the strategy’s effectiveness:total operational costs decrease by 14.7%(from$7354.64 to$6272.59),carbon emissions drop by 49.9%(from 5294.94 to 2653.39kg),andrenewable energyutilizationincreases by24.39%(from4.82%to8.17%).These results affirmthemodel’s ability to reconcile economic and environmental goals,providing a scalable approach for low-carbon transitions in industrial parks.
基金financially supported by the National Natural Science Foundation of China(Grant No.61233013)
文摘Combining high precision numerical analysis methods with optimization algorithms to make a systematic exploration of a design space has become an important topic in the modern design methods. During the design process of an underwater glider＇s flying-wing structure, a surrogate model is introduced to decrease the computation time for a high precision analysis. By these means, the contradiction between precision and efficiency is solved effectively. Based on the parametric geometry modeling, mesh generation and computational fluid dynamics analysis, a surrogate model is constructed by adopting the design of experiment （DOE） theory to solve the multi-objects design optimization problem of the underwater glider. The procedure of a surrogate model construction is presented, and the Gaussian kernel function is specifically discussed. The Particle Swarm Optimization （PSO） algorithm is applied to hydrodynamic design optimization. The hydrodynamic performance of the optimized flying-wing structure underwater glider increases by 9.1%.
基金supported by the Beijing Municipal Science & Technology Commission(No. D0405002040221)
文摘Effects of hydrocarbon compositions on raw exhaust emissions and combustion processes were studied on an engine test bench. The optimization of gasoline hydrocarbon composition was discussed. As olefins content increased from 10.0% to 25.0% in volume, the combustion duration was shortened by about 2 degree crank angle （°CA）, and the engine-out THC emission was reduced by about 15%. On the other hand, as aromatics content changed from 35.0% to 45.0%, the engine-out NOx emissions increased by 4%. An increment in olefins content resulted in a slight increase in engine-out CO emission, while the aromatics content had little effect on engine-out total hydrocarbon （THC） and CO emissions. Over the new European driving cycle （NEDC）, the THC, NOx and CO emissions of fuel with 25.0% olefins and 35.0% aromatics were about 45%, 21% and 19% lower than those of fuel with 10.0% olefins and 40.0% aromatics, respectively. The optimized gasoline compositions for new engines and new vehicles have low aromatics and high olefins contents.
文摘Drop-on-demand (DOD) bioprinting has been widely used in tissue engineering due to its highthroughput efficiency and cost effectiveness. However, this type of bioprinting involves challenges such as satellite generation, too-large droplet generation, and too-low droplet speed. These challenges reduce the stability and precision of DOD printing, disorder cell arrays, and hence generate further structural errors. In this paper, a multi-objective optimization (MOO) design method for DOD printing parameters through fully connected neural networks (FCNNs) is proposed in order to solve these challenges. The MOO problem comprises two objective functions: to develop the satellite formation model with FCNNs;and to decrease droplet diameter and increase droplet speed. A hybrid multi-subgradient descent bundle method with an adaptive learning rate algorithm (HMSGDBA), which combines the multisubgradient descent bundle (MSGDB) method with Adam algorithm, is introduced in order to search for the Pareto-optimal set for the MOO problem. The superiority of HMSGDBA is demonstrated through comparative studies with the MSGDB method. The experimental results show that a single droplet can be printed stably and the droplet speed can be increased from 0.88 to 2.08 m·s^-1 after optimization with the proposed method. The proposed method can improve both printing precision and stability, and is useful in realizing precise cell arrays and complex biological functions. Furthermore, it can be used to obtain guidelines for the setup of cell-printing experimental platforms.
文摘According to site-specific environments such as high water pressures, high in-situ stresses and strong rockbursts, the design scheme of the long and deep diversion tunnels at Jinping II hydropower station was optimized to ensure construction safety. New drainage tunnels were considered. Furthermore, lining structures and grouting pressures were modified during the excavation of tunnels. The construction scheme was updated dynamically based on the complex geological conditions. For instances, the diversion tunnels were first excavated by drilling and blasting method at the first stage of construction, and then by the combination method of tunnel boring machine （TBM） and drilling and blasting, and finally by drilling and blasting method. Through optimized scheme and updated construction scheme, the excavation of diversion tunnel #1 was successfully completed in June, 2011. This paper summarizes the key issues in rock mechanics associated with the construction of the long and deep diversion tunnels at Jinping II hydropower station. The experiences of design and construction obtained from this project could provide reference to similar projects.
基金Project supported by the Guangdong-Ministry of Education (GD-MOE) Coordination Project of Industry Academic and Research (2008B090500274)Chengdu Key Technologies R&D Program (10GGYB897GX-023)
文摘In order to reduce the cost of ABs-type hydrogen storage alloys, effects of substitution of Ce for La （A side） and Fe, Mn, Al for Ni （B side） on structural and electrochemical properties of （LaCe）;（NiFeMnAl）s alloys were studied systematically. To make component uniform and operation easy, uniform design （UD） method was introduced into the study of composition optimization of Co-free Fe-containing ABs-type alloys for the first time. X-ray diffraction （XRD） results showed that the designed alloys were of single CaCus-type structure phase. The replacement of Fe had a severe effect on electrochemical capacity, and the substitution of Fe and A1 had a synergetic action among the unit cell volume, cycling stability and high rate discharge property. Interestingly, it was found that the hydrogen storage alloys with excessively high plateau pressure showed a tilted line in Nyquist plot instead of the semicircle, and the current decayed rapidly to near zero at the beginning of the step in constant potential step （CPS）, indicating that electrochemical impedance spectra （EIS） and CPS cannot accurately measure the electrochemical kinetics process of the hydrogen storage alloys with excessively high plateau pressure.
基金supported by the Rachadapisek Sompote Fund for Postdoctoral Fellowshipthe Thailand Research Fund (No. IRG5780001)+1 种基金Chulalongkorn University and Faculty of Science of Chulalongkorn Universitythe Department of Chemical Technology, Faculty of Science, Chulalongkorn University for the instrument support in this work
文摘The objective of this research was to investigate CO2adsorption capacity of tetraethylenepentamine-functionalized basic-modified calcined hydrotalcite（TEPA/b-c HT）sorbents at atmospheric pressure formed under varying TEPA loading levels,temperatures,sorbent weight to total gaseous flow rate（W/F）ratios and CO2concentrations in the influent gas.The TEPA/b-c HT sorbents were characterized by means of X-ray diffraction（XRD）,Fourier transform infrared spectrometry（FT–IR）,thermal gravimetric analysis（TGA）,Brunauer–Emmet–Teller（BET）analysis of nitrogen（N2）adsorption/desorption and carbon–hydrogen–nitrogen（CHN）elemental analysis.Moreover,a full 2~4factorial design with three central points at a 95%confidence interval was used to screen important factor（s）on the CO2adsorption capacity.It revealed that85.0%variation in the capacity came from the influence of four main factors and the15.0%one was from their interactions.A face-centered central composite design response surface method（FCCCD–RSM）was then employed to optimize the condition,the maximal capacity of 5.5–6.1 mmol/g was achieved when operating with a TEPA loading level of 39%–49%（W/W）,temperature of 76–90℃,W/F ratio of 1.7–2.60（g·sec）/cm^3and CO2concentration of 27%–41%（V/V）.The model fitted sufficiently the experimental data with an error range of±1.5%.From cyclical adsorption/desorption and selectivity at the optimal condition,the 40%TEPA/b-c HT still expressed its effective performance after eight cycles.
文摘In drilling operation, a large saving in time and money would be achieved by reducing the drilling time, since some of the costs are time-dependent. Drilling time could be minimized by raising the penetration rate. In the comparative optimization method, by using the records of the first drilled wells and comparing the criteria like penetration rate, cost per foot and specific energy, the drilling parameters of the next wells being drilled can be optimized in each depth interval. In the mathematical optimization technique, some numerical equations to model the penetration rate, bit wear rate and hydraulics would be used to minimize the drilling cost and time as much as possible and improve the results of the primary comparative optimization. In this research, as a case study the Iranian Khangiran gas field has been evaluated to optimize the drilling costs. A combination of the mentioned optimization techniques resulted in an optimal well which reduced the drilling time and cost considerably in comparison with the wells already drilled.
基金supported by the National Natural Science Foundation of China(Nos.11872230,91852108,91952302,92052203)the Aeronautical Science Foundation of China(No.2020Z006058002)。
文摘The Adaptive Dropped Hinge Flap(ADHF) is a novel trailing edge high-lift device characterized by the integration of downward deflection spoiler and simple hinge flap, with excellent aerodynamic and mechanism performance. In this paper, aerodynamic optimization design of an ADHF high-lift system is conducted considering the mechanism performance. Shape and settings of both takeoff and landing configurations are optimized and analyzed, with considering the kinematic constraints of ADHF mechanism, and the desired optimization results were obtained after optimization. Sensitivity analysis proves the robustness of the optimal design. Comparison shows that the ADHF design has better comprehensive performance of both mechanism and aerodynamics than the conventional Fowler flap and simple hinge flap design.
