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
Long-life energy storage batteries are integral to energy storage systems and electric vehicles,with lithium-ion batteries(LIBs)currently being the preferred option for extended usage-life energy storage.To further ex...Long-life energy storage batteries are integral to energy storage systems and electric vehicles,with lithium-ion batteries(LIBs)currently being the preferred option for extended usage-life energy storage.To further extend the life span of LIBs,it is essential to intensify investments in battery design,manufacturing processes,and the advancement of ancillary materials.The pursuit of long durability introduces new challenges for battery energy density.The advent of electrode material offers effective support in enhancing the battery’s long-duration performance.Often underestimated as part of the cathode composition,the binder plays a pivotal role in the longevity and electrochemical performance of the electrode.Maintaining the mechanical integrity of the electrode through judicious binder design is a fundamental requirement for achieving consistent long-life cycles and high energy density.This paper primarily concentrates on the commonly employed cathode systems in lithium-ion batteries,elucidates the significance of binders for both,discusses the application status,strengths,and weaknesses of novel binders,and ultimately puts forth corresponding optimization strategies.It underscores the critical function of binders in enhancing battery performance and advancing the sustainable development of lithium-ion batteries,aiming to offer fresh insights and perspectives for the design of high-performance LIBs.展开更多
Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including ...Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.展开更多
Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley a...Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.展开更多
Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longe...Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradien...In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradient method.Under the condition of standard Wolfe line search,the proposed search direction is the descent direction.For general nonlinear functions,the method is globally convergent.Finally,numerical results show that the proposed method is efficient.展开更多
With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electro...With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electronic instruments.Therefore,the design and preparation of electromagnetic absorbing composites represent an efficient approach to mitigate the current hazards of electromagnetic radiation.However,traditional electromagnetic absorbers are difficult to satisfy the demands of actual utilization in the face of new challenges,and emerging absorbents have garnered increasing attention due to their structure and performance-based advantages.In this review,several emerging composites of Mxene-based,biochar-based,chiral,and heat-resisting are discussed in detail,including their synthetic strategy,structural superiority and regulation method,and final optimization of electromagnetic absorption ca-pacity.These insights provide a comprehensive reference for the future development of new-generation electromagnetic-wave absorption composites.Moreover,the potential development directions of these emerging absorbers have been proposed as well.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
Rock fragmentation is an important indicator for assessing the quality of blasting operations.However,accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters ...Rock fragmentation is an important indicator for assessing the quality of blasting operations.However,accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties.For this reason,optimized by the Bayesian optimization algorithm(BOA),four hybrid machine learning models,including random forest,adaptive boosting,gradient boosting,and extremely randomized trees,were developed in this study.A total of 102 data sets with seven input parameters(spacing-to-burden ratio,hole depth-to-burden ratio,burden-to-hole diameter ratio,stemming length-to-burden ratio,powder factor,in situ block size,and elastic modulus)and one output parameter(rock fragment mean size,X_(50))were adopted to train and validate the predictive models.The root mean square error(RMSE),the mean absolute error(MAE),and the coefficient of determination(R^(2))were used as the evaluation metrics.The evaluation results demonstrated that the hybrid models showed superior performance than the standalone models.The hybrid model consisting of gradient boosting and BOA(GBoost-BOA)achieved the best prediction results compared with the other hybrid models,with the highest R^(2)value of 0.96 and the smallest values of RMSE and MAE of 0.03 and 0.02,respectively.Furthermore,sensitivity analysis was carried out to study the effects of input variables on rock fragmentation.In situ block size(XB),elastic modulus(E),and stemming length-to-burden ratio(T/B)were set as the main influencing factors.The proposed hybrid model provided a reliable prediction result and thus could be considered an alternative approach for rock fragment prediction in mining engineering.展开更多
High-resolution seeing through complex scattering media such as turbid water,biological tissues,and mist is a significant challenge because the strong scattering scrambles the light paths and forms the scattering wall...High-resolution seeing through complex scattering media such as turbid water,biological tissues,and mist is a significant challenge because the strong scattering scrambles the light paths and forms the scattering wall.We propose an active polarized iterative optimization approach for high-resolution imaging through complex scattering media.By acquiring a series of sub-polarized images,we can capture the diverse pattern-illuminated images with various high-frequency component information caused by the Brownian motion of complex scattering materials,which are processed using the common-mode rejection of polarization characteristics to extract target information from scattering medium information.Following that,our computational reconstruction technique employs an iterative optimization algorithm that commences with patternilluminated Fourier ptychography for reconstructing the high-resolution scene.It is extremely important that our approach for high-resolution imaging through complex scattering media is not limited by priori information and optical memory effect.The proposed approach is suitable for not only dynamic but also static scattering media,which may find applications in the biomedicine field,such as skin abnormalities,non-invasive blood flow,and superficial tumors.展开更多
Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ul...Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ultra-low sampling rates.We develop an alternative optimization with physics and a data-driven diffusion network(APD-Net).It features alternative optimization driven by the learned task-agnostic natural image prior and the task-specific physics prior.During the training stage,APD-Net harnesses the power of diffusion models to capture data-driven statistics of natural signals.In the inference stage,the physics prior is introduced as corrective guidance to ensure consistency between the physics imaging model and the natural image probability distribution.Through alternative optimization,APD-Net reconstructs data-efficient,high-fidelity images that are statistically and physically compliant.To accelerate reconstruction,initializing images with the inverse SPI physical model reduces the need for reconstruction inference from 100 to 30 steps.Through both numerical simulations and real prototype experiments,APD-Net achieves high-quality,full-color reconstructions of complex natural images at a low sampling rate of 1%.In addition,APD-Net’s tuning-free nature ensures robustness across various imaging setups and sampling rates.Our research offers a broadly applicable approach for various applications,including but not limited to medical imaging and industrial inspection.展开更多
Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,th...Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.展开更多
As battery technology evolves and demand for efficient energy storage solutions,aqueous zinc ion batteries(AZIBs)have garnered significant attention due to their safety and environmental benefits.However,the stability...As battery technology evolves and demand for efficient energy storage solutions,aqueous zinc ion batteries(AZIBs)have garnered significant attention due to their safety and environmental benefits.However,the stability of cathode materials under high-voltage conditions remains a critical challenge in improving its energy density.This review systematically explores the failure mechanisms of high-voltage cathode materials in AZIBs,including hydrogen evolution reaction,phase transformation and dissolution phenomena.To address these challenges,we propose a range of advanced strategies aimed at improving the stability of cathode materials.These strategies include surface coating and doping techniques designed to fortify the surface properties and structure integrity of the cathode materials under high-voltage conditions.Additionally,we emphasize the importance of designing antioxidant electrolytes,with a focus on understanding and optimizing electrolyte decomposition mechanisms.The review also highlights the significance of modifying conductive agents and employing innovative separators to further enhance the stability of AZIBs.By integrating these cutting-edge approaches,this review anticipates substantial advancements in the stability of high-voltage cathode materials,paving the way for the broader application and development of AZIBs in energy storage.展开更多
In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionall...In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionally,this optimization process was centered on a single objective,such as net present value,return on investment,cumulative oil production,or cumulative water production.However,the inherent complexity of reservoir exploration necessitates a departure from this single-objective approach.Mul-tiple conflicting production and economic indicators must now be considered to enable more precise and robust decision-making.In response to this challenge,researchers have embarked on a journey to explore field development optimization of multiple conflicting criteria,employing the formidable tools of multi-objective optimization algorithms.These algorithms delve into the intricate terrain of production strategy design,seeking to strike a delicate balance between the often-contrasting objectives.Over the years,a plethora of these algorithms have emerged,ranging from a priori methods to a posteriori approach,each offering unique insights and capabilities.This survey endeavors to encapsulate,catego-rize,and scrutinize these invaluable contributions to field development optimization,which grapple with the complexities of multiple conflicting objective functions.Beyond the overview of existing methodologies,we delve into the persisting challenges faced by researchers and practitioners alike.Notably,the application of multi-objective optimization techniques to production optimization is hin-dered by the resource-intensive nature of reservoir simulation,especially when confronted with inherent uncertainties.As a result of this survey,emerging opportunities have been identified that will serve as catalysts for pivotal research endeavors in the future.As intelligent and more efficient algo-rithms continue to evolve,the potential for addressing hitherto insurmountable field development optimization obstacles becomes increasingly viable.This discussion on future prospects aims to inspire critical research,guiding the way toward innovative solutions in the ever-evolving landscape of oil and gas production optimization.展开更多
Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power o...Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.展开更多
Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields.This paper attempts to carry out an update on PSO and gives a...Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields.This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications,but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms.Covering six strategic areas,which include Data Mining,Machine Learning,Engineering Design,Energy Systems,Healthcare,and Robotics,the study demonstrates the versatility and effectiveness of the PSO.Experimental results are,however,used to show the strong and weak parts of PSO,and performance results are included in tables for ease of comparison.The results stress PSO’s efficiency in providing optimal solutions but also show that there are aspects that need to be improved through combination with algorithms or tuning to the parameters of the method.The review of the advantages and limitations of PSO is intended to provide academics and practitioners with a well-rounded view of the methods of employing such a tool most effectively and to encourage optimized designs of PSO in solving theoretical and practical problems in the future.展开更多
The proposed hybrid optimization algorithm integrates particle swarm optimizatio(PSO)with Ant Colony Optimization(ACO)to improve a number of pitfalls within PSO methods traditionally considered and/or applied to indus...The proposed hybrid optimization algorithm integrates particle swarm optimizatio(PSO)with Ant Colony Optimization(ACO)to improve a number of pitfalls within PSO methods traditionally considered and/or applied to industrial robots.Particle Swarm Optimization may frequently suffer from local optima and inaccuracies in identifying the geometric parameters,which are necessary for applications requiring high-accuracy performances.The proposed approach integrates pheromone-based learning of ACO with the D-H method of developing an error model;hence,the global search effectiveness together with the convergence accuracy is further improved.Comparison studies of the hybrid PSO-ACO algorithm show higher precision and effectiveness in the optimization of geometric error parameters compared to the traditional methods.This is a remarkable reduction of localization errors,thus yielding accuracy and reliability in industrial robotic systems,as the results show.This approach improves performance in those applications that demand high geometric calibration by reducing the geometric error.The paper provides an overview of input for developing robotics and automation,giving importance to precision in industrial engineering.The proposed hybrid methodology is a good way to enhance the working accuracy and effectiveness of industrial robots and shall enable their wide application to complex tasks that require a high degree of accuracy.展开更多
This study investigates the potential of Prosopis cineraria Leaves Powder(PCLP)as a biosorbent for removing lead(Pb)and zinc(Zn)from aqueous solutions,optimizing the process using Response Surface Methodology(RSM).Pro...This study investigates the potential of Prosopis cineraria Leaves Powder(PCLP)as a biosorbent for removing lead(Pb)and zinc(Zn)from aqueous solutions,optimizing the process using Response Surface Methodology(RSM).Prosopis cineraria,commonly known as Khejri,is a drought-resistant tree with significant promise in environmental applications.The research employed a Central Composite Design(CCD)to examine the independent and combined effects of key process variables,including initial metal ion concentration,contact time,pH,and PCLP dosage.RSM was used to develop mathematical models that explain the relationship between these factors and the efficiency of metal removal,allowing the determination of optimal operating conditions.The experimental results indicated that the Langmuir isotherm model was the most appropriate for describing the biosorption of both metals,suggesting favorable adsorption characteristics.Additionally,the D-R isotherm confirmed that chemisorption was the primary mechanism involved in the biosorption process.For lead removal,the optimal conditions were found to be 312.23 K temperature,pH 4.72,58.5 mg L-1 initial concentration,and 0.27 g biosorbent dosage,achieving an 83.77%removal efficiency.For zinc,the optimal conditions were 312.4 K,pH 5.86,53.07 mg L-1 initial concentration,and the same biosorbent dosage,resulting in a 75.86%removal efficiency.These findings highlight PCLP’s potential as an effective,eco-friendly biosorbent for sustainable heavy metal removal in water treatment.展开更多
Predicting the productivity of multistage fractured horizontal wells plays an important role in exploiting unconventional resources.In recent years,machine learning(ML)models have emerged as a new approach for such st...Predicting the productivity of multistage fractured horizontal wells plays an important role in exploiting unconventional resources.In recent years,machine learning(ML)models have emerged as a new approach for such studies.However,the scarcity of sufficient real data for model training often leads to imprecise predictions,even though the models trained with real data better characterize geological and engineering features.To tackle this issue,we propose an ML model that can obtain reliable results even with a small amount of data samples.Our model integrates the synthetic minority oversampling technique(SMOTE)to expand the data volume,the support vector machine(SVM)for model training,and the particle swarm optimization(PSO)algorithm for optimizing hyperparameters.To enhance the model performance,we conduct feature fusion and dimensionality reduction.Additionally,we examine the influences of different sample sizes and ML models for training.The proposed model demonstrates higher prediction accuracy and generalization ability,achieving a predicted R^(2)value of up to 0.9 for the test set,compared to the traditional ML techniques with an R^(2)of 0.13.This model accurately predicts the production of fractured horizontal wells even with limited samples,supplying an efficient tool for optimizing the production of unconventional resources.Importantly,the model holds the potential applicability to address similar challenges in other fields constrained by scarce data samples.展开更多
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.
基金We would like to show gratitude to the Yunnan Province Basic Research Major Project(202501BC070006(Y.Wang))Key Industry Science and Technology Projects for University Services in Yunnan Province(FWCY ZNT2024002(Y.Wang))+3 种基金National Natural Science Foundation of China(22279070(L.Wang))and(U21A20170(X.He))the Ministry of Science and Technology of China(2019YFA0705703(L.Wang))Beijing Natural Science Foundation(L242005(X.He))Key Industry Science and Technology Projects for University Services in Yunnan Province(FWCY BSPY2024011(T.Lai)).
文摘Long-life energy storage batteries are integral to energy storage systems and electric vehicles,with lithium-ion batteries(LIBs)currently being the preferred option for extended usage-life energy storage.To further extend the life span of LIBs,it is essential to intensify investments in battery design,manufacturing processes,and the advancement of ancillary materials.The pursuit of long durability introduces new challenges for battery energy density.The advent of electrode material offers effective support in enhancing the battery’s long-duration performance.Often underestimated as part of the cathode composition,the binder plays a pivotal role in the longevity and electrochemical performance of the electrode.Maintaining the mechanical integrity of the electrode through judicious binder design is a fundamental requirement for achieving consistent long-life cycles and high energy density.This paper primarily concentrates on the commonly employed cathode systems in lithium-ion batteries,elucidates the significance of binders for both,discusses the application status,strengths,and weaknesses of novel binders,and ultimately puts forth corresponding optimization strategies.It underscores the critical function of binders in enhancing battery performance and advancing the sustainable development of lithium-ion batteries,aiming to offer fresh insights and perspectives for the design of high-performance LIBs.
基金Supported by Xuhui District Health Commission,No.SHXH202214.
文摘Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.
基金supported by the General Program of the National Natural Science Foundation of China(No.52274326)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202109)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553).
文摘Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.
基金supported by a Horizontal Project on the Development of a Hybrid Energy Storage Simulation Model for Wind Power Based on an RT-LAB Simulation System(PH2023000190)the Inner Mongolia Natural Science Foundation Project and the Optimization of Exergy Efficiency of a Hybrid Energy Storage System with Crossover Control for Wind Power(2023JQ04).
文摘Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
基金Supported by the Science and Technology Project of Guangxi(Guike AD23023002)。
文摘In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradient method.Under the condition of standard Wolfe line search,the proposed search direction is the descent direction.For general nonlinear functions,the method is globally convergent.Finally,numerical results show that the proposed method is efficient.
基金supported by the Surface Project of Local De-velopment in Science and Technology Guided by Central Govern-ment(No.2021ZYD0041)the National Natural Science Founda-tion of China(Nos.52377026 and 52301192)+3 种基金the Natural Science Foundation of Shandong Province(No.ZR2019YQ24)the Taishan Scholars and Young Experts Program of Shandong Province(No.tsqn202103057)the Special Financial of Shandong Province(Struc-tural Design of High-efficiency Electromagnetic Wave-absorbing Composite Materials and Construction of Shandong Provincial Tal-ent Teams)the“Sanqin Scholars”Innovation Teams Project of Shaanxi Province(Clean Energy Materials and High-Performance Devices Innovation Team of Shaanxi Dongling Smelting Co.,Ltd.).
文摘With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electronic instruments.Therefore,the design and preparation of electromagnetic absorbing composites represent an efficient approach to mitigate the current hazards of electromagnetic radiation.However,traditional electromagnetic absorbers are difficult to satisfy the demands of actual utilization in the face of new challenges,and emerging absorbents have garnered increasing attention due to their structure and performance-based advantages.In this review,several emerging composites of Mxene-based,biochar-based,chiral,and heat-resisting are discussed in detail,including their synthetic strategy,structural superiority and regulation method,and final optimization of electromagnetic absorption ca-pacity.These insights provide a comprehensive reference for the future development of new-generation electromagnetic-wave absorption composites.Moreover,the potential development directions of these emerging absorbers have been proposed as well.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金National Natural Science Foundation of China,Grant/Award Number:52374153。
文摘Rock fragmentation is an important indicator for assessing the quality of blasting operations.However,accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties.For this reason,optimized by the Bayesian optimization algorithm(BOA),four hybrid machine learning models,including random forest,adaptive boosting,gradient boosting,and extremely randomized trees,were developed in this study.A total of 102 data sets with seven input parameters(spacing-to-burden ratio,hole depth-to-burden ratio,burden-to-hole diameter ratio,stemming length-to-burden ratio,powder factor,in situ block size,and elastic modulus)and one output parameter(rock fragment mean size,X_(50))were adopted to train and validate the predictive models.The root mean square error(RMSE),the mean absolute error(MAE),and the coefficient of determination(R^(2))were used as the evaluation metrics.The evaluation results demonstrated that the hybrid models showed superior performance than the standalone models.The hybrid model consisting of gradient boosting and BOA(GBoost-BOA)achieved the best prediction results compared with the other hybrid models,with the highest R^(2)value of 0.96 and the smallest values of RMSE and MAE of 0.03 and 0.02,respectively.Furthermore,sensitivity analysis was carried out to study the effects of input variables on rock fragmentation.In situ block size(XB),elastic modulus(E),and stemming length-to-burden ratio(T/B)were set as the main influencing factors.The proposed hybrid model provided a reliable prediction result and thus could be considered an alternative approach for rock fragment prediction in mining engineering.
基金supported by the National Natural Science Foundation of China(Grant Nos.62205259,62075175,62105254,and 62375212)the National Key Laboratory of Infrared Detection Technologies(Grant No.IRDT-23-06)+1 种基金the Fundamental Research Funds for the Central Universities(Grant Nos.XJSJ24028,XJS222202,ZYTS24097,and ZYTS24095)the Open Research Fund of Beijing Key Laboratory of Advanced Optical Remote Sensing Technology.
文摘High-resolution seeing through complex scattering media such as turbid water,biological tissues,and mist is a significant challenge because the strong scattering scrambles the light paths and forms the scattering wall.We propose an active polarized iterative optimization approach for high-resolution imaging through complex scattering media.By acquiring a series of sub-polarized images,we can capture the diverse pattern-illuminated images with various high-frequency component information caused by the Brownian motion of complex scattering materials,which are processed using the common-mode rejection of polarization characteristics to extract target information from scattering medium information.Following that,our computational reconstruction technique employs an iterative optimization algorithm that commences with patternilluminated Fourier ptychography for reconstructing the high-resolution scene.It is extremely important that our approach for high-resolution imaging through complex scattering media is not limited by priori information and optical memory effect.The proposed approach is suitable for not only dynamic but also static scattering media,which may find applications in the biomedicine field,such as skin abnormalities,non-invasive blood flow,and superficial tumors.
基金upported by the National Natural Science Foundation of China(Grant No.62305184)the Major Key Project of Pengcheng Laboratory(Grant No.PCL2024A1)+1 种基金the Basic and Applied Basic Research Foundation of Guangdong Province(Grant No.2023A1515012932)the Science,Technology and Innovation Commission of Shenzhen Municipality(Grant No.WDZC20220818100259004).
文摘Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ultra-low sampling rates.We develop an alternative optimization with physics and a data-driven diffusion network(APD-Net).It features alternative optimization driven by the learned task-agnostic natural image prior and the task-specific physics prior.During the training stage,APD-Net harnesses the power of diffusion models to capture data-driven statistics of natural signals.In the inference stage,the physics prior is introduced as corrective guidance to ensure consistency between the physics imaging model and the natural image probability distribution.Through alternative optimization,APD-Net reconstructs data-efficient,high-fidelity images that are statistically and physically compliant.To accelerate reconstruction,initializing images with the inverse SPI physical model reduces the need for reconstruction inference from 100 to 30 steps.Through both numerical simulations and real prototype experiments,APD-Net achieves high-quality,full-color reconstructions of complex natural images at a low sampling rate of 1%.In addition,APD-Net’s tuning-free nature ensures robustness across various imaging setups and sampling rates.Our research offers a broadly applicable approach for various applications,including but not limited to medical imaging and industrial inspection.
基金supported by Yunnan Provincial Basic Research Project(202401AT070344,202301AT070443)National Natural Science Foundation of China(62263014,52207105)+1 种基金Yunnan Lancang-Mekong International Electric Power Technology Joint Laboratory(202203AP140001)Major Science and Technology Projects in Yunnan Province(202402AG050006).
文摘Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.
基金supported by the Exchange Program of Highend Foreign Experts of Ministry of Science and Technology of People’s Republic of China(No.G2023041003L)the Natural Science Foundation of Shaanxi Provincial Department of Education(No.23JK0367)+1 种基金the Scientific Research Startup Program for Introduced Talents of Shaanxi University of Technology(Nos.SLGRCQD2208,SLGRCQD2306,SLGRCQD2133)Contaminated Soil Remediation and Resource Utilization Innovation Team at Shaanxi University of Technology。
文摘As battery technology evolves and demand for efficient energy storage solutions,aqueous zinc ion batteries(AZIBs)have garnered significant attention due to their safety and environmental benefits.However,the stability of cathode materials under high-voltage conditions remains a critical challenge in improving its energy density.This review systematically explores the failure mechanisms of high-voltage cathode materials in AZIBs,including hydrogen evolution reaction,phase transformation and dissolution phenomena.To address these challenges,we propose a range of advanced strategies aimed at improving the stability of cathode materials.These strategies include surface coating and doping techniques designed to fortify the surface properties and structure integrity of the cathode materials under high-voltage conditions.Additionally,we emphasize the importance of designing antioxidant electrolytes,with a focus on understanding and optimizing electrolyte decomposition mechanisms.The review also highlights the significance of modifying conductive agents and employing innovative separators to further enhance the stability of AZIBs.By integrating these cutting-edge approaches,this review anticipates substantial advancements in the stability of high-voltage cathode materials,paving the way for the broader application and development of AZIBs in energy storage.
基金the support of EPIC - Energy Production Innovation Center, hosted by the University of Campinas (UNICAMP) and sponsored by Equinor Brazil and FAPESP - Sao Paulo Research Foundation (2021/04878- 7 and 2017/15736-3)financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nível Superior Brasil (CAPES) - Financing Code 001
文摘In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionally,this optimization process was centered on a single objective,such as net present value,return on investment,cumulative oil production,or cumulative water production.However,the inherent complexity of reservoir exploration necessitates a departure from this single-objective approach.Mul-tiple conflicting production and economic indicators must now be considered to enable more precise and robust decision-making.In response to this challenge,researchers have embarked on a journey to explore field development optimization of multiple conflicting criteria,employing the formidable tools of multi-objective optimization algorithms.These algorithms delve into the intricate terrain of production strategy design,seeking to strike a delicate balance between the often-contrasting objectives.Over the years,a plethora of these algorithms have emerged,ranging from a priori methods to a posteriori approach,each offering unique insights and capabilities.This survey endeavors to encapsulate,catego-rize,and scrutinize these invaluable contributions to field development optimization,which grapple with the complexities of multiple conflicting objective functions.Beyond the overview of existing methodologies,we delve into the persisting challenges faced by researchers and practitioners alike.Notably,the application of multi-objective optimization techniques to production optimization is hin-dered by the resource-intensive nature of reservoir simulation,especially when confronted with inherent uncertainties.As a result of this survey,emerging opportunities have been identified that will serve as catalysts for pivotal research endeavors in the future.As intelligent and more efficient algo-rithms continue to evolve,the potential for addressing hitherto insurmountable field development optimization obstacles becomes increasingly viable.This discussion on future prospects aims to inspire critical research,guiding the way toward innovative solutions in the ever-evolving landscape of oil and gas production optimization.
基金funded by the“Research and Application Project of Collaborative Optimization Control Technology for Distribution Station Area for High Proportion Distributed PV Consumption(4000-202318079A-1-1-ZN)”of the Headquarters of the State Grid Corporation.
文摘Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.
文摘Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields.This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications,but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms.Covering six strategic areas,which include Data Mining,Machine Learning,Engineering Design,Energy Systems,Healthcare,and Robotics,the study demonstrates the versatility and effectiveness of the PSO.Experimental results are,however,used to show the strong and weak parts of PSO,and performance results are included in tables for ease of comparison.The results stress PSO’s efficiency in providing optimal solutions but also show that there are aspects that need to be improved through combination with algorithms or tuning to the parameters of the method.The review of the advantages and limitations of PSO is intended to provide academics and practitioners with a well-rounded view of the methods of employing such a tool most effectively and to encourage optimized designs of PSO in solving theoretical and practical problems in the future.
文摘The proposed hybrid optimization algorithm integrates particle swarm optimizatio(PSO)with Ant Colony Optimization(ACO)to improve a number of pitfalls within PSO methods traditionally considered and/or applied to industrial robots.Particle Swarm Optimization may frequently suffer from local optima and inaccuracies in identifying the geometric parameters,which are necessary for applications requiring high-accuracy performances.The proposed approach integrates pheromone-based learning of ACO with the D-H method of developing an error model;hence,the global search effectiveness together with the convergence accuracy is further improved.Comparison studies of the hybrid PSO-ACO algorithm show higher precision and effectiveness in the optimization of geometric error parameters compared to the traditional methods.This is a remarkable reduction of localization errors,thus yielding accuracy and reliability in industrial robotic systems,as the results show.This approach improves performance in those applications that demand high geometric calibration by reducing the geometric error.The paper provides an overview of input for developing robotics and automation,giving importance to precision in industrial engineering.The proposed hybrid methodology is a good way to enhance the working accuracy and effectiveness of industrial robots and shall enable their wide application to complex tasks that require a high degree of accuracy.
文摘This study investigates the potential of Prosopis cineraria Leaves Powder(PCLP)as a biosorbent for removing lead(Pb)and zinc(Zn)from aqueous solutions,optimizing the process using Response Surface Methodology(RSM).Prosopis cineraria,commonly known as Khejri,is a drought-resistant tree with significant promise in environmental applications.The research employed a Central Composite Design(CCD)to examine the independent and combined effects of key process variables,including initial metal ion concentration,contact time,pH,and PCLP dosage.RSM was used to develop mathematical models that explain the relationship between these factors and the efficiency of metal removal,allowing the determination of optimal operating conditions.The experimental results indicated that the Langmuir isotherm model was the most appropriate for describing the biosorption of both metals,suggesting favorable adsorption characteristics.Additionally,the D-R isotherm confirmed that chemisorption was the primary mechanism involved in the biosorption process.For lead removal,the optimal conditions were found to be 312.23 K temperature,pH 4.72,58.5 mg L-1 initial concentration,and 0.27 g biosorbent dosage,achieving an 83.77%removal efficiency.For zinc,the optimal conditions were 312.4 K,pH 5.86,53.07 mg L-1 initial concentration,and the same biosorbent dosage,resulting in a 75.86%removal efficiency.These findings highlight PCLP’s potential as an effective,eco-friendly biosorbent for sustainable heavy metal removal in water treatment.
基金supported by the National Natural Science Foundation of China(52274055)the Shandong Provincial Natural Science Foundation(ZR2022YQ50)the Taishan Scholar Program of Shandong Province(tsqn202408088)。
文摘Predicting the productivity of multistage fractured horizontal wells plays an important role in exploiting unconventional resources.In recent years,machine learning(ML)models have emerged as a new approach for such studies.However,the scarcity of sufficient real data for model training often leads to imprecise predictions,even though the models trained with real data better characterize geological and engineering features.To tackle this issue,we propose an ML model that can obtain reliable results even with a small amount of data samples.Our model integrates the synthetic minority oversampling technique(SMOTE)to expand the data volume,the support vector machine(SVM)for model training,and the particle swarm optimization(PSO)algorithm for optimizing hyperparameters.To enhance the model performance,we conduct feature fusion and dimensionality reduction.Additionally,we examine the influences of different sample sizes and ML models for training.The proposed model demonstrates higher prediction accuracy and generalization ability,achieving a predicted R^(2)value of up to 0.9 for the test set,compared to the traditional ML techniques with an R^(2)of 0.13.This model accurately predicts the production of fractured horizontal wells even with limited samples,supplying an efficient tool for optimizing the production of unconventional resources.Importantly,the model holds the potential applicability to address similar challenges in other fields constrained by scarce data samples.
