Na_(3)V_(2)(PO_(4))_(3)(NVP)has garnered great attentions as a prospective cathode material for sodium-ion batteries(SIBs)by virtue of its decent theoretical capacity,superior ion conductivity and high structural stab...Na_(3)V_(2)(PO_(4))_(3)(NVP)has garnered great attentions as a prospective cathode material for sodium-ion batteries(SIBs)by virtue of its decent theoretical capacity,superior ion conductivity and high structural stability.However,the inherently poor electronic conductivity and sluggish sodium-ion diffusion kinetics of NVP material give rise to inferior rate performance and unsatisfactory energy density,which strictly confine its further application in SIBs.Thus,it is of significance to boost the sodium storage performance of NVP cathode material.Up to now,many methods have been developed to optimize the electrochemical performance of NVP cathode material.In this review,the latest advances in optimization strategies for improving the electrochemical performance of NVP cathode material are well summarized and discussed,including carbon coating or modification,foreign-ion doping or substitution and nanostructure and morphology design.The foreign-ion doping or substitution is highlighted,involving Na,V,and PO_(4)^(3−)sites,which include single-site doping,multiple-site doping,single-ion doping,multiple-ion doping and so on.Furthermore,the challenges and prospects of high-performance NVP cathode material are also put forward.It is believed that this review can provide a useful reference for designing and developing high-performance NVP cathode material toward the large-scale application in SIBs.展开更多
This study addresses the energy-intensive challenge of small-scale biogas upgrading by optimizing a chemical absorption process employing methyl diethanolamine(MDEA).Focusing on a typical distributed application of 30...This study addresses the energy-intensive challenge of small-scale biogas upgrading by optimizing a chemical absorption process employing methyl diethanolamine(MDEA).Focusing on a typical distributed application of 300 Nm^(3)/d,we developed an integrated simulation-optimization framework using Aspen HYSYS 14.0 to systematically evaluate the effects of critical operating parameters—absorption pressure,MDEA concentration,flow rate,temperature,number of trays,and reboiler duty—on methane purity and energy consumption.The key finding is the identification of an optimal parameter set:absorption pressure of 1200 kPa,MDEA concentration of 20mol%,lean flow rate of 2.5 kmol/h,temperature of 298.15 K,20 absorber trays,10 regenerator trays,and a reboiler duty of 4 kW,which enabled the product gas to achieve a high CH4 concentration of 97mol%,compliant with pipeline standards.A detailed energy consumption analysis revealed that the reboiler is the most energy-intensive unit,accounting for 75.40%of the total 5.29 kW energy consumption,followed by the gas compressor(23.38%).The specific energy consumption for CH4 recovery and the Energy Consumption Index(ECI)were quantified at 0.8852 kWh/kg CH_(4)and 6.82,respectively.This work provides a validated optimization strategy and critical energy breakdown,offering practical guidance for enhancing the technical and economic viability of small-scale,centralized biogas purification systems.展开更多
Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address t...Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.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
CO_(2) Water-Alternating-Gas(CO_(2)-WAG)injection is not only a method to enhance oil recovery but also a feasible way to achieve CO_(2) sequestration.However,inappropriate injection strategies would prevent the attai...CO_(2) Water-Alternating-Gas(CO_(2)-WAG)injection is not only a method to enhance oil recovery but also a feasible way to achieve CO_(2) sequestration.However,inappropriate injection strategies would prevent the attainment of maximum oil recovery and cumulative CO_(2) storage.Furthermore,the optimization of CO_(2)-WAG is computationally expensive as it needs to frequently call the compositional simulation model that involves various CO_(2) storage mechanisms.Therefore,the surrogate-assisted evolutionary optimization is necessary,which replaces the compositional simulator with surrogate models.In this paper,a surrogate-based multi-objective optimization algorithm assisted by the single-objective pre-search method is proposed.The results of single-objective optimization will be used to initialize the solutions of multi-objective optimization,which accelerates the exploration of the entire Pareto front.In addition,a convergence criterion is also proposed for the single-objective optimization during pre-search,and the gradient of surrogate models is adopted as the convergence criterion.Finally,the method proposed in this work is applied to two benchmark reservoir models to prove its efficiency and correctness.The results show that the proposed algorithm achieves a better performance than the conventional ones for the multi-objective optimization of CO_(2)-WAG.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
Carbon dioxide Enhanced Oil Recovery(CO_(2)-EOR)technology guarantees substantial underground CO_(2) sequestration while simultaneously boosting the production capacity of subsurface hydrocarbons(oil and gas).However,...Carbon dioxide Enhanced Oil Recovery(CO_(2)-EOR)technology guarantees substantial underground CO_(2) sequestration while simultaneously boosting the production capacity of subsurface hydrocarbons(oil and gas).However,unreasonable CO_(2)-EOR strategies,encompassing well placement and well control parameters,will lead to premature gas channeling in production wells,resulting in large amounts of CO_(2) escape without any beneficial effect.Due to the lack of prediction and optimization tools that integrate complex geological and engineering information for the widely used CO_(2)-EOR technology in promising industries,it is imperative to conduct thorough process simulations and optimization evaluations of CO_(2)-EOR technology.In this paper,a novel optimization workflow that couples the AST-GraphTrans-based proxy model(Attention-based Spatio-temporal Graph Transformer)and multi-objective optimization algorithm MOPSO(Multi-objective Particle Swarm Optimization)is established to optimize CO_(2)-EOR strategies.The workflow consists of two outstanding components.The AST-GraphTrans-based proxy model is utilized to forecast the dynamics of CO_(2) flooding and sequestration,which includes cumulative oil production,CO_(2) sequestration volume,and CO_(2) plume front.And the MOPSO algorithm is employed for achieving maximum oil production and maximum sequestration volume by coordinating well placement and well control parameters with the containment of gas channeling.By the collaborative coordination of the two aforementioned components,the AST-GraphTrans proxy-assisted optimization workflow overcomes the limitations of rapid optimization in CO_(2)-EOR technology,which cannot consider high-dimensional spatio-temporal information.The effectiveness of the proposed workflow is validated on a 2D synthetic model and a 3D field-scale reservoir model.The proposed workflow yields optimizations that lead to a significant increase in cumulative oil production by 87%and 49%,and CO_(2) sequestration volume enhancement by 78%and 50%across various reservoirs.These findings underscore the superior stability and generalization capabilities of the AST-GraphTrans proxy-assisted framework.The contribution of this study is to provide a more efficient prediction and optimization tool that maximizes CO_(2) sequestration and oil recovery while mitigating CO_(2) gas channeling,thereby ensuring cleaner oil production.展开更多
To solve the problems of deformation,micro-cracks,and residual tensile stress in laser cladding coatings,the technique of laser cladding with Fe-based memory alloy can be considered.However,the process of in-situ synt...To solve the problems of deformation,micro-cracks,and residual tensile stress in laser cladding coatings,the technique of laser cladding with Fe-based memory alloy can be considered.However,the process of in-situ synthesis of Fe-based memory alloy coatings is extremely complex.At present,there is no clear guidance scheme for its preparation process,which limits its promotion and application to some extent.Therefore,in this study,response surface methodology(RSM)was used to model the response surface between the target values and the cladding process parameters.The NSGA-2 algorithm was employed to optimize the process parameters.The results indicate that the composite optimization method consisting of RSM and the NSGA-2 algorithm can establish a more accurate model,with an error of less than 4.5%between the predicted and actual values.Based on this established model,the optimal scheme for process parameters corresponding to different target results can be rapidly obtained.The prepared coating exhibits a uniform structure,with no defects such as pores,cracks,and deformation.The surface roughness and microhardness of the coating are enhanced,the shaping quality of the coating is effectively improved,and the electrochemical corrosion performance of the coating in 3.5%NaCl solution is obviously better than that of the substrate,providing an important guide for engineering applications.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
The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant...The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively.展开更多
Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Althou...Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.展开更多
A microwave-H202 process for sludge pretreatment exhibited high efticiencies of releasing organics, nitrogen, and phosphorus, but large quantifies of H202 residues were detected. A uniform design method was thus emplo...A microwave-H202 process for sludge pretreatment exhibited high efticiencies of releasing organics, nitrogen, and phosphorus, but large quantifies of H202 residues were detected. A uniform design method was thus employed in this study to further optimize H202 dosage by investigating effects of pH and H202 dosage on the amount of 1-I202 residue and releases of organics, nitrogen, and phosphorus. A regression model was established with pH and H202 dosage as the independent variables, and H202 residue and releases of organics, nitrogen, and phosphorus as the dependent variables. In the optimized microwave-H202 process, the pH value of the sludge was firstly adjusted to 11.0, then the sludge was heated to 80~C and H202 was dosed at a H202 ：mixed liquor suspended solids （MLSS） ratio of 0.2, and the sludge was finally heated to 100~C by microwave irradiation. Compared to the microwave-H202 process without optimization, the H202 dosage and the utilization rate of H202 in the optimized microwave-H202 process were reduced by 80% and greatly improved by 3.87 times, respectively, when the H202：MLSS dosage ratio was decreased from 1.0 to 0.2, resulting in nearly the same release rate of soluble chemical oxygen demand in the microwave-H202 process without optimization at H202：MLSS ratio of 0.5.展开更多
基金partly supported by the National Natural Science Foundation of China(Grant No.52272225).
文摘Na_(3)V_(2)(PO_(4))_(3)(NVP)has garnered great attentions as a prospective cathode material for sodium-ion batteries(SIBs)by virtue of its decent theoretical capacity,superior ion conductivity and high structural stability.However,the inherently poor electronic conductivity and sluggish sodium-ion diffusion kinetics of NVP material give rise to inferior rate performance and unsatisfactory energy density,which strictly confine its further application in SIBs.Thus,it is of significance to boost the sodium storage performance of NVP cathode material.Up to now,many methods have been developed to optimize the electrochemical performance of NVP cathode material.In this review,the latest advances in optimization strategies for improving the electrochemical performance of NVP cathode material are well summarized and discussed,including carbon coating or modification,foreign-ion doping or substitution and nanostructure and morphology design.The foreign-ion doping or substitution is highlighted,involving Na,V,and PO_(4)^(3−)sites,which include single-site doping,multiple-site doping,single-ion doping,multiple-ion doping and so on.Furthermore,the challenges and prospects of high-performance NVP cathode material are also put forward.It is believed that this review can provide a useful reference for designing and developing high-performance NVP cathode material toward the large-scale application in SIBs.
基金funded by Shenzhen Science and Technology Program,grant number No.ZDSYS20230626091400001No.KCXST20221021111609024No.KCXFZ20240903093459001.
文摘This study addresses the energy-intensive challenge of small-scale biogas upgrading by optimizing a chemical absorption process employing methyl diethanolamine(MDEA).Focusing on a typical distributed application of 300 Nm^(3)/d,we developed an integrated simulation-optimization framework using Aspen HYSYS 14.0 to systematically evaluate the effects of critical operating parameters—absorption pressure,MDEA concentration,flow rate,temperature,number of trays,and reboiler duty—on methane purity and energy consumption.The key finding is the identification of an optimal parameter set:absorption pressure of 1200 kPa,MDEA concentration of 20mol%,lean flow rate of 2.5 kmol/h,temperature of 298.15 K,20 absorber trays,10 regenerator trays,and a reboiler duty of 4 kW,which enabled the product gas to achieve a high CH4 concentration of 97mol%,compliant with pipeline standards.A detailed energy consumption analysis revealed that the reboiler is the most energy-intensive unit,accounting for 75.40%of the total 5.29 kW energy consumption,followed by the gas compressor(23.38%).The specific energy consumption for CH4 recovery and the Energy Consumption Index(ECI)were quantified at 0.8852 kWh/kg CH_(4)and 6.82,respectively.This work provides a validated optimization strategy and critical energy breakdown,offering practical guidance for enhancing the technical and economic viability of small-scale,centralized biogas purification systems.
基金supported by the Fundamental Research Funds for the Provincial Universities (Grant No.2024-KYYWF-0141)the National Natural Science Foundation of China (Grant Nos.52406076,52227813)+1 种基金the National Key Research and Development Program of China (Grant No.2022YFE0133900)the China Postdoctoral Science Foundation (Grant No.2023M740905)。
文摘Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.
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.
基金financial support provided by the National Key R&D Program of China(No.2023YFB4104203 and No.2022YFE0129900)financial support from the National Natural Science Foundation of China(No.U22B2075)The funding from the Shandong Postdoctoral Science Foundation(No.SDBX2023017)is also greatly appreciated.
文摘CO_(2) Water-Alternating-Gas(CO_(2)-WAG)injection is not only a method to enhance oil recovery but also a feasible way to achieve CO_(2) sequestration.However,inappropriate injection strategies would prevent the attainment of maximum oil recovery and cumulative CO_(2) storage.Furthermore,the optimization of CO_(2)-WAG is computationally expensive as it needs to frequently call the compositional simulation model that involves various CO_(2) storage mechanisms.Therefore,the surrogate-assisted evolutionary optimization is necessary,which replaces the compositional simulator with surrogate models.In this paper,a surrogate-based multi-objective optimization algorithm assisted by the single-objective pre-search method is proposed.The results of single-objective optimization will be used to initialize the solutions of multi-objective optimization,which accelerates the exploration of the entire Pareto front.In addition,a convergence criterion is also proposed for the single-objective optimization during pre-search,and the gradient of surrogate models is adopted as the convergence criterion.Finally,the method proposed in this work is applied to two benchmark reservoir models to prove its efficiency and correctness.The results show that the proposed algorithm achieves a better performance than the conventional ones for the multi-objective optimization of CO_(2)-WAG.
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supported by the National Natural Science Foundation of China(Nos.52374064,52274056)China Scholarship Council(No.202406450086).
文摘Carbon dioxide Enhanced Oil Recovery(CO_(2)-EOR)technology guarantees substantial underground CO_(2) sequestration while simultaneously boosting the production capacity of subsurface hydrocarbons(oil and gas).However,unreasonable CO_(2)-EOR strategies,encompassing well placement and well control parameters,will lead to premature gas channeling in production wells,resulting in large amounts of CO_(2) escape without any beneficial effect.Due to the lack of prediction and optimization tools that integrate complex geological and engineering information for the widely used CO_(2)-EOR technology in promising industries,it is imperative to conduct thorough process simulations and optimization evaluations of CO_(2)-EOR technology.In this paper,a novel optimization workflow that couples the AST-GraphTrans-based proxy model(Attention-based Spatio-temporal Graph Transformer)and multi-objective optimization algorithm MOPSO(Multi-objective Particle Swarm Optimization)is established to optimize CO_(2)-EOR strategies.The workflow consists of two outstanding components.The AST-GraphTrans-based proxy model is utilized to forecast the dynamics of CO_(2) flooding and sequestration,which includes cumulative oil production,CO_(2) sequestration volume,and CO_(2) plume front.And the MOPSO algorithm is employed for achieving maximum oil production and maximum sequestration volume by coordinating well placement and well control parameters with the containment of gas channeling.By the collaborative coordination of the two aforementioned components,the AST-GraphTrans proxy-assisted optimization workflow overcomes the limitations of rapid optimization in CO_(2)-EOR technology,which cannot consider high-dimensional spatio-temporal information.The effectiveness of the proposed workflow is validated on a 2D synthetic model and a 3D field-scale reservoir model.The proposed workflow yields optimizations that lead to a significant increase in cumulative oil production by 87%and 49%,and CO_(2) sequestration volume enhancement by 78%and 50%across various reservoirs.These findings underscore the superior stability and generalization capabilities of the AST-GraphTrans proxy-assisted framework.The contribution of this study is to provide a more efficient prediction and optimization tool that maximizes CO_(2) sequestration and oil recovery while mitigating CO_(2) gas channeling,thereby ensuring cleaner oil production.
基金financial supports from the National Natural Science Foundation of China-Youth Project(51801076)the Provincial Colleges and Universities Natural Science Research Project of Jiangsu Province(18KJB430009)+1 种基金the Postdoctoral Research Support Project of Jiangsu Province(1601055C)the Senior Talents Research Startup of Jiangsu University(14JDG126)。
文摘To solve the problems of deformation,micro-cracks,and residual tensile stress in laser cladding coatings,the technique of laser cladding with Fe-based memory alloy can be considered.However,the process of in-situ synthesis of Fe-based memory alloy coatings is extremely complex.At present,there is no clear guidance scheme for its preparation process,which limits its promotion and application to some extent.Therefore,in this study,response surface methodology(RSM)was used to model the response surface between the target values and the cladding process parameters.The NSGA-2 algorithm was employed to optimize the process parameters.The results indicate that the composite optimization method consisting of RSM and the NSGA-2 algorithm can establish a more accurate model,with an error of less than 4.5%between the predicted and actual values.Based on this established model,the optimal scheme for process parameters corresponding to different target results can be rapidly obtained.The prepared coating exhibits a uniform structure,with no defects such as pores,cracks,and deformation.The surface roughness and microhardness of the coating are enhanced,the shaping quality of the coating is effectively improved,and the electrochemical corrosion performance of the coating in 3.5%NaCl solution is obviously better than that of the substrate,providing an important guide for engineering applications.
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supported in part by the National Research Foundation of Korea (NRF-2021H1D3A2A01082705).
文摘The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively.
基金supported by the National Natural Science Foundation of China (61873079,51707050)
文摘Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.
基金supported by the National Natural Science Foundation of China (No. 51008297)the Hi-Tech Research and Development Program (863) of China(No. 2007AA06Z347)the National Major Science & Technology Projects for Water Pollution Control and Management (No. 2012ZX07202-005)
文摘A microwave-H202 process for sludge pretreatment exhibited high efticiencies of releasing organics, nitrogen, and phosphorus, but large quantifies of H202 residues were detected. A uniform design method was thus employed in this study to further optimize H202 dosage by investigating effects of pH and H202 dosage on the amount of 1-I202 residue and releases of organics, nitrogen, and phosphorus. A regression model was established with pH and H202 dosage as the independent variables, and H202 residue and releases of organics, nitrogen, and phosphorus as the dependent variables. In the optimized microwave-H202 process, the pH value of the sludge was firstly adjusted to 11.0, then the sludge was heated to 80~C and H202 was dosed at a H202 ：mixed liquor suspended solids （MLSS） ratio of 0.2, and the sludge was finally heated to 100~C by microwave irradiation. Compared to the microwave-H202 process without optimization, the H202 dosage and the utilization rate of H202 in the optimized microwave-H202 process were reduced by 80% and greatly improved by 3.87 times, respectively, when the H202：MLSS dosage ratio was decreased from 1.0 to 0.2, resulting in nearly the same release rate of soluble chemical oxygen demand in the microwave-H202 process without optimization at H202：MLSS ratio of 0.5.
