Slag viscosity plays a crucial role in the smelting process.A slag viscosity prediction model was developed by integrating hyperparameter optimization algorithms,machine learning,and SHapley Additive exPlanations(SHAP...Slag viscosity plays a crucial role in the smelting process.A slag viscosity prediction model was developed by integrating hyperparameter optimization algorithms,machine learning,and SHapley Additive exPlanations(SHAP)analysis.The developed slag viscosity prediction models were evaluated using multiple statistical metrics,leading to the identification of the optimal model—Bayesian optimization-based categorical boosting(BO-CatBoost).And this model was further compared with existing models,including NPL model,FactSage+Roscoe-Einstein(RE)equation,artificial neural network model+RE equation,Riboud model+RE equation,and Zhang model.The results indicate that the slag viscosity prediction model based on BO-CatBoost outperforms all other models,achieving a coefficient of determination of 0.9897,a root mean square error of 1.0619,a mean absolute error of 0.6133,and a hit ratio of 95.1%.The global interpretability analysis of SHAP analysis was used to reveal the importance degree of different features on slag viscosity.The local interpretability analysis of SHAP analysis was used to obtain the quantitative influence of different features on slag viscosity in specific samples.The high-accuracy and interpretable slag viscosity prediction model developed is beneficial to the intelligent design of slag composition.展开更多
This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,suc...This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,such as adaptive boosting(AdaBoost),categorical boosting(CatBoost),gradient boosting regressor(GBR),hist gradient boosting regressor(HistGBR),and extreme gradient boosting(XGBoost),were developed and optimized using 729 high-quality dataset points incorporating seven input parameters,including cement,CO_(2),exposure time,water-binder ratio,fly ash,curing time,and compressive strength.Several performance evaluation metrics were used to compare the models.The GBR model emerged as the best-performing model,based on high coefficient of determination(R^(2))values and balanced error metrics across both validation and testing datasets.While all models performed exceptionally well on the training data,GBR demonstrated superior generalization capability,with R^(2) values of 0.9438 on the validation set and 0.9310 on the testing set.Furthermore,its low mean squared error(MSE),root mean square error(RMSE),mean absolute error(MAE),and median absolute error(MdAE)confirmed its robustness and accuracy.Moreover,shapley additive explanations(SHAP)analysis enhanced the interpretability of predictions,highlighting the curing time and exposure time as the most critical drivers of carbonation depth.展开更多
Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an in...Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an interpretable machine learning approach to UCS prediction is presented,pairing five models(Random Forest(RF),Gradient Boosting(GB),Extreme Gradient Boosting(XGB),CatBoost,and K-Nearest Neighbors(KNN))with SHapley Additive exPlanations(SHAP)for enhanced interpretability and to guide feature removal.A complete dataset of 12 geotechnical and chemical parameters,i.e.,Atterberg limits,compaction properties,stabilizer chemistry,dosage,curing time,was used to train and test the models.R2,RMSE,MSE,and MAE were used to assess performance.Initial results with all 12 features indicated that boosting-based models(GB,XGB,CatBoost)exhibited the highest predictive accuracy(R^(2)=0.93)with satisfactory generalization on test data,followed by RF and KNN.SHAP analysis consistently picked CaO content,curing time,stabilizer dosage,and compaction parameters as the most important features,aligning with established soil stabilization mechanisms.Models were then re-trained on the top 8 and top 5 SHAP-ranked features.Interestingly,GB,XGB,and CatBoost maintained comparable accuracy with reduced input sets,while RF was moderately sensitive and KNN was somewhat better owing to reduced dimensionality.The findings confirm that feature reduction through SHAP enables cost-effective UCS prediction through the reduction of laboratory test requirements without significant accuracy loss.The suggested hybrid approach offers an explainable,interpretable,and cost-effective tool for geotechnical engineering practice.展开更多
The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback ...The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory(TCN-BiLSTM)architecture incorporating a spatiotemporal attention mechanism.Firstly,during data preprocessing,the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients.Subsequently,a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term periodic patterns,with spatiotemporal attention dynamically weighting spatiotemporal dependencies.Finally,Shapley Additive explanations(SHAP)additive analysis quantifies feature contribution rates and provides optimization feedback to the model.Validation using operational data from a PV power station in Northeast China demonstrates that compared to conventional deep learning models,the proposed method achieves a 17.6%reduction in root mean square error(RMSE),a 5.4%decrease in training time consumption,and a 4.78%improvement in continuous ranked probability score(CRPS),exhibiting significant advantages in both prediction accuracy and generalization capability.This approach enhances the application effectiveness of ultra-short-term PV power forecasting while simultaneously improving prediction accuracy and computational efficiency.展开更多
Achieving the simultaneous enhancement of strength and ductility in laser powder bed fused (LPBF-ed) titanium (Ti) is challenging due to the complex, high-dimensional parameter space and interactions between parameter...Achieving the simultaneous enhancement of strength and ductility in laser powder bed fused (LPBF-ed) titanium (Ti) is challenging due to the complex, high-dimensional parameter space and interactions between parameters and powders. Herein, a hybrid intelligent framework for process parameter optimization of LPBF-ed Ti with improved ultimate tensile strength (UTS) and elongation (EL) was proposed. It combines the data augmentation method (AVG ± EC × SD), the multi-model fusion stacking ensemble learning model (GBDT-BPNN-XGBoost), the interpretable machine learning method and the non-dominated ranking genetic algorithm (NSGA-Ⅱ). The GBDT-BPNN-XGBoost outperforms single models in predicting UTS and EL across the accuracy, generalization ability and stability. The SHAP analysis reveals that laser power (P) is the most important feature affecting both UTS and EL, and it has a positive impact on them when P < 220 W. The UTS and EL of samples fabricated by the optimal process parameters were 718 ± 5 MPa and 27.9 % ± 0.1 %, respectively. The outstanding strength-ductility balance is attributable to the forward stresses in hard α'-martensite and back stresses in soft αm'-martensite induced by the strain gradients of hetero-microstructure. The back stresses strengthen the soft αm'-martensite, improving the overall UTS. The forward stresses stimulate the activation of dislocations in hard α'-martensite and the generation of 〈c + a〉 dislocations, allowing the plastic strain to occur in hard regions and enhancing the overall ductility. This work provides a feasible strategy for multi-objective optimization and valuable insights into tailoring the microstructure for improving mechanical properties.展开更多
Although lithium-ion batteries(LIBs)currently dominate a wide spectrum of energy storage applications,they face challenges such as fast cycle life decay and poor stability that hinder their further application.To addr...Although lithium-ion batteries(LIBs)currently dominate a wide spectrum of energy storage applications,they face challenges such as fast cycle life decay and poor stability that hinder their further application.To address these limitations,element doping has emerged as a prevalent strategy to enhance the discharge capacity and extend the durability of Li-Ni-Co-Mn(LNCM)ternary compounds.This study utilized a machine learning-driven feature screening method to effectively pinpoint four key features crucially impacting the initial discharge capacity(IC)of Li-Ni-Co-Mn(LNCM)ternary cathode materials.These features were also proved highly predictive for the 50^(th)cycle discharge capacity(EC).Additionally,the application of SHAP value analysis yielded an in-depth understanding of the interplay between these features and discharge performance.This insight offers valuable direction for future advancements in the development of LNCM cathode materials,effectively promoting this field toward greater efficiency and sustainability.展开更多
Data-driven machine learning methods have been proven highly successful in predicting glass properties,but hampered when dealing with small datasets,such as oxynitride glasses with excellent mechanical properties and ...Data-driven machine learning methods have been proven highly successful in predicting glass properties,but hampered when dealing with small datasets,such as oxynitride glasses with excellent mechanical properties and chemical stability.Here,a data augmentation method based on the Wasserstein Generative Adversarial Network with Gradient Penalty(GP)and Content Constraint Penalty(CP)terms,a generative deep-learning model via the adversarial training of a generator and a discriminator,was established,in which the GP and CP terms ensure training stability and the physical rationality of the generated samples.The results indicate that the generated samples improve the performance of the oxynitride glass composition-property models trained with the XGBoost algorithm in terms of prediction accuracy and generalization capability.Furthermore,the augmented models outperform the general glass prediction model,GlassNet,over 101 experimental samples not included in the training datasets.Based on SHAP's single feature analysis and feature interaction analysis,the interpretability study further sheds light on the contributions of elements and the interactive effects of element pairs on the properties of oxynitride glasses.These achievements not only provide reliable models for the composition-property studies of oxynitride glasses but also offer a novel strategy for developing high-performance data-driven models under data scarcity scenarios.展开更多
Piano Key Weir(PKW)is an advanced hydraulic structure that enhances water discharge efficiency and flood control through its innovative design,which allows for higher flow rates at lower upstream levels.Accurate disch...Piano Key Weir(PKW)is an advanced hydraulic structure that enhances water discharge efficiency and flood control through its innovative design,which allows for higher flow rates at lower upstream levels.Accurate discharge prediction is crucial for PKW performance within various water management systems.This study assesses the efficacy of Artificial-Neural-Network(ANN)and Gene-Expression-Programming(GEP)models in improving discharge prediction for symmetrical PKWs.A comprehensive dataset comprising 476 experimental records from previously published studies was utilized,considering a range of geometric and fluid parameters(PKW key widths,PKW height,and upstream head).In the training stage,the ANN model demonstrated a superior determination coefficient(R^(2))of 0.9997 alongside a lower Mean Absolute Percentage Error(MAPE)of 0.74%,whereas the GEP model yielded an R^(2) of 0.9971 and a MAPE of 2.36%.In the subsequent testing stage,both models displayed a high degree of accuracy in comparison to the experimental data,attaining an R^(2) value of 0.9376.Furthermore,SHapley-Additive-exPlanations and Partial-Dependence-Plot analyses were incorporated,revealing that the upstream head exerted the greatest influence on the discharge prediction,followed by PKW height and PKW key width.Therefore,these models are recommended as reliable,robust,and efficient tools for forecasting the discharge of symmetrical PKWs.Additionally,the mathematical expressions and associated script codes developed in this study are made accessible,thus providing hydraulic engineers and researchers with the means to perform rapid and accurate discharge predictions.展开更多
The global shift towards sustainable and environmentally friendly transportation options has led to the increasing adoption of electric buses(Ebuses).To optimize the deployment and operational strategies of Ebuses,it ...The global shift towards sustainable and environmentally friendly transportation options has led to the increasing adoption of electric buses(Ebuses).To optimize the deployment and operational strategies of Ebuses,it is imperative to accurately predict their energy consumption under varying conditions,particularly in cold climates where battery life is typically degraded.The exploration of this aspect within the Canadian context has been limited.In addition,we have found that existing models in the literature perform poorly in the Canadian environment,giving rise to the need for new models using Canadian data.This paper focuses on the development,comparison,and evaluation of various data-driven models designed to predict the energy consumption of different Ebuses with different heating technologies under a wide range of climate conditions.We specifically use Canadian data as a good representative of cold climates in general.The results show that the performance of the different bus types varies substantially under the exact same conditions.In addition,tree-based family of models proves to be the most suitable approach for predicting the Ebus consumption rate.The results indicate that the Random Forest method emerges as the superior choice for predicting the energy consumption rate,with a resulting mean absolute error of 0.09–0.1 kWh/km observed across the different models.Furthermore,SHAP analysis shows that the main variables influencing the energy consumption rate depend on the type of heating system(using the battery for heating or using an auxiliary system that utilizes diesel for heating)adopted.展开更多
This study constructs 196 transition metals(TM)@S_(x)N_(y) single-atom catalysts(SACs)(x=0-4 and y=0-4)and employs the eXtreme Gradient Boosting(XGBoost)classification model in machine learning(ML)for effectively dist...This study constructs 196 transition metals(TM)@S_(x)N_(y) single-atom catalysts(SACs)(x=0-4 and y=0-4)and employs the eXtreme Gradient Boosting(XGBoost)classification model in machine learning(ML)for effectively distinguishing qualified and unqualified catalysts.The prediction accuracy rate is high,up to 95%.The SHapley Additive exPlanations(SHAP)analysis reveals that the N≡N bond length and the number of outermost d electrons(N_(d))can well describe the nitrogen(N2)reduction reaction(NRR)activity.The relationships between N≡N,N_(d),the adsorption energies of different intermediates(ΔE_(*N_(2)),ΔE_(*N_(2)H),and ΔE_(*NH_(2))),the general descriptor(φ),and the Gibbs free energy of key steps(ΔG_(*N_(2)),ΔG_(*N_(2)-*N_(2)H),and ΔG_(*N_H(2)-*NH_(3)))indicate that moderate nitrogen activation can enhance the reaction activity.Among the 17 screened SACs,Mo@S3N1,and W@S_(3)N_(1) demonstrate the best catalytic performance,with limiting potential(U_(L))values of only-0.26 and-0.25 V under implicit solvation conditions.The electronic properties and variations in N≡N and TM-N bond lengths are investigated to reveal the origin of NRR activity.This study provides the decisive features and NRR dataset for ML research,as well as a feasible strategy for rational design of NRR SACs.展开更多
基金funded by the National Natural Science Foundation of China(No.52374321)the National Key Research and Development Program of China(No.2024YFB3713602)the Youth Science and Technology Innovation Fund of Jianlong Group-University of Science and Technology Beijing(No.20231235).
文摘Slag viscosity plays a crucial role in the smelting process.A slag viscosity prediction model was developed by integrating hyperparameter optimization algorithms,machine learning,and SHapley Additive exPlanations(SHAP)analysis.The developed slag viscosity prediction models were evaluated using multiple statistical metrics,leading to the identification of the optimal model—Bayesian optimization-based categorical boosting(BO-CatBoost).And this model was further compared with existing models,including NPL model,FactSage+Roscoe-Einstein(RE)equation,artificial neural network model+RE equation,Riboud model+RE equation,and Zhang model.The results indicate that the slag viscosity prediction model based on BO-CatBoost outperforms all other models,achieving a coefficient of determination of 0.9897,a root mean square error of 1.0619,a mean absolute error of 0.6133,and a hit ratio of 95.1%.The global interpretability analysis of SHAP analysis was used to reveal the importance degree of different features on slag viscosity.The local interpretability analysis of SHAP analysis was used to obtain the quantitative influence of different features on slag viscosity in specific samples.The high-accuracy and interpretable slag viscosity prediction model developed is beneficial to the intelligent design of slag composition.
文摘This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,such as adaptive boosting(AdaBoost),categorical boosting(CatBoost),gradient boosting regressor(GBR),hist gradient boosting regressor(HistGBR),and extreme gradient boosting(XGBoost),were developed and optimized using 729 high-quality dataset points incorporating seven input parameters,including cement,CO_(2),exposure time,water-binder ratio,fly ash,curing time,and compressive strength.Several performance evaluation metrics were used to compare the models.The GBR model emerged as the best-performing model,based on high coefficient of determination(R^(2))values and balanced error metrics across both validation and testing datasets.While all models performed exceptionally well on the training data,GBR demonstrated superior generalization capability,with R^(2) values of 0.9438 on the validation set and 0.9310 on the testing set.Furthermore,its low mean squared error(MSE),root mean square error(RMSE),mean absolute error(MAE),and median absolute error(MdAE)confirmed its robustness and accuracy.Moreover,shapley additive explanations(SHAP)analysis enhanced the interpretability of predictions,highlighting the curing time and exposure time as the most critical drivers of carbonation depth.
文摘Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an interpretable machine learning approach to UCS prediction is presented,pairing five models(Random Forest(RF),Gradient Boosting(GB),Extreme Gradient Boosting(XGB),CatBoost,and K-Nearest Neighbors(KNN))with SHapley Additive exPlanations(SHAP)for enhanced interpretability and to guide feature removal.A complete dataset of 12 geotechnical and chemical parameters,i.e.,Atterberg limits,compaction properties,stabilizer chemistry,dosage,curing time,was used to train and test the models.R2,RMSE,MSE,and MAE were used to assess performance.Initial results with all 12 features indicated that boosting-based models(GB,XGB,CatBoost)exhibited the highest predictive accuracy(R^(2)=0.93)with satisfactory generalization on test data,followed by RF and KNN.SHAP analysis consistently picked CaO content,curing time,stabilizer dosage,and compaction parameters as the most important features,aligning with established soil stabilization mechanisms.Models were then re-trained on the top 8 and top 5 SHAP-ranked features.Interestingly,GB,XGB,and CatBoost maintained comparable accuracy with reduced input sets,while RF was moderately sensitive and KNN was somewhat better owing to reduced dimensionality.The findings confirm that feature reduction through SHAP enables cost-effective UCS prediction through the reduction of laboratory test requirements without significant accuracy loss.The suggested hybrid approach offers an explainable,interpretable,and cost-effective tool for geotechnical engineering practice.
基金funded by the National Natural Science Foundation of China(NSFC)(No.62066024)funded by Basic Scientific Research Projects of Higher Education Institutions in Liaoning Province(LJ212411632063)the National Undergraduate Training Program for Innovation and Entrepreneurship(S202511632045).
文摘The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory(TCN-BiLSTM)architecture incorporating a spatiotemporal attention mechanism.Firstly,during data preprocessing,the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients.Subsequently,a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term periodic patterns,with spatiotemporal attention dynamically weighting spatiotemporal dependencies.Finally,Shapley Additive explanations(SHAP)additive analysis quantifies feature contribution rates and provides optimization feedback to the model.Validation using operational data from a PV power station in Northeast China demonstrates that compared to conventional deep learning models,the proposed method achieves a 17.6%reduction in root mean square error(RMSE),a 5.4%decrease in training time consumption,and a 4.78%improvement in continuous ranked probability score(CRPS),exhibiting significant advantages in both prediction accuracy and generalization capability.This approach enhances the application effectiveness of ultra-short-term PV power forecasting while simultaneously improving prediction accuracy and computational efficiency.
基金supported by the National Natural Sci-ence Foundation of China(Nos.52274359 and 52304379)the China National Postdoctoral Program for Innovative Talents(No.BX20220034)+2 种基金the China Postdoctoral Science Foundation(No.2022M720403)the AECC University Research Cooperation Project(No.HFZL2021CXY021)the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities)(No.FRF-IDRY-23-025).
文摘Achieving the simultaneous enhancement of strength and ductility in laser powder bed fused (LPBF-ed) titanium (Ti) is challenging due to the complex, high-dimensional parameter space and interactions between parameters and powders. Herein, a hybrid intelligent framework for process parameter optimization of LPBF-ed Ti with improved ultimate tensile strength (UTS) and elongation (EL) was proposed. It combines the data augmentation method (AVG ± EC × SD), the multi-model fusion stacking ensemble learning model (GBDT-BPNN-XGBoost), the interpretable machine learning method and the non-dominated ranking genetic algorithm (NSGA-Ⅱ). The GBDT-BPNN-XGBoost outperforms single models in predicting UTS and EL across the accuracy, generalization ability and stability. The SHAP analysis reveals that laser power (P) is the most important feature affecting both UTS and EL, and it has a positive impact on them when P < 220 W. The UTS and EL of samples fabricated by the optimal process parameters were 718 ± 5 MPa and 27.9 % ± 0.1 %, respectively. The outstanding strength-ductility balance is attributable to the forward stresses in hard α'-martensite and back stresses in soft αm'-martensite induced by the strain gradients of hetero-microstructure. The back stresses strengthen the soft αm'-martensite, improving the overall UTS. The forward stresses stimulate the activation of dislocations in hard α'-martensite and the generation of 〈c + a〉 dislocations, allowing the plastic strain to occur in hard regions and enhancing the overall ductility. This work provides a feasible strategy for multi-objective optimization and valuable insights into tailoring the microstructure for improving mechanical properties.
基金supported by the National Natural Science Foundation of China(Nos.52122408,52071023)the Program for Science&Technology Innovation Talents in the University of Henan Province(No.22HASTIT1006)+2 种基金the Program for Central Plains Talents(No.ZYYCYU202012172)the Ministry of Education,Singapore(No.RG70/20)the Opening Project of National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials,Henan University of Science and Technology(No.HKDNM201906).
文摘Although lithium-ion batteries(LIBs)currently dominate a wide spectrum of energy storage applications,they face challenges such as fast cycle life decay and poor stability that hinder their further application.To address these limitations,element doping has emerged as a prevalent strategy to enhance the discharge capacity and extend the durability of Li-Ni-Co-Mn(LNCM)ternary compounds.This study utilized a machine learning-driven feature screening method to effectively pinpoint four key features crucially impacting the initial discharge capacity(IC)of Li-Ni-Co-Mn(LNCM)ternary cathode materials.These features were also proved highly predictive for the 50^(th)cycle discharge capacity(EC).Additionally,the application of SHAP value analysis yielded an in-depth understanding of the interplay between these features and discharge performance.This insight offers valuable direction for future advancements in the development of LNCM cathode materials,effectively promoting this field toward greater efficiency and sustainability.
基金supported by the 14th Five-Year National Key R&D Program[No.2022YFB3603300]the Research and development on big data model and material simulation platform for advanced glass composition and performance[No.22520730500].
文摘Data-driven machine learning methods have been proven highly successful in predicting glass properties,but hampered when dealing with small datasets,such as oxynitride glasses with excellent mechanical properties and chemical stability.Here,a data augmentation method based on the Wasserstein Generative Adversarial Network with Gradient Penalty(GP)and Content Constraint Penalty(CP)terms,a generative deep-learning model via the adversarial training of a generator and a discriminator,was established,in which the GP and CP terms ensure training stability and the physical rationality of the generated samples.The results indicate that the generated samples improve the performance of the oxynitride glass composition-property models trained with the XGBoost algorithm in terms of prediction accuracy and generalization capability.Furthermore,the augmented models outperform the general glass prediction model,GlassNet,over 101 experimental samples not included in the training datasets.Based on SHAP's single feature analysis and feature interaction analysis,the interpretability study further sheds light on the contributions of elements and the interactive effects of element pairs on the properties of oxynitride glasses.These achievements not only provide reliable models for the composition-property studies of oxynitride glasses but also offer a novel strategy for developing high-performance data-driven models under data scarcity scenarios.
文摘Piano Key Weir(PKW)is an advanced hydraulic structure that enhances water discharge efficiency and flood control through its innovative design,which allows for higher flow rates at lower upstream levels.Accurate discharge prediction is crucial for PKW performance within various water management systems.This study assesses the efficacy of Artificial-Neural-Network(ANN)and Gene-Expression-Programming(GEP)models in improving discharge prediction for symmetrical PKWs.A comprehensive dataset comprising 476 experimental records from previously published studies was utilized,considering a range of geometric and fluid parameters(PKW key widths,PKW height,and upstream head).In the training stage,the ANN model demonstrated a superior determination coefficient(R^(2))of 0.9997 alongside a lower Mean Absolute Percentage Error(MAPE)of 0.74%,whereas the GEP model yielded an R^(2) of 0.9971 and a MAPE of 2.36%.In the subsequent testing stage,both models displayed a high degree of accuracy in comparison to the experimental data,attaining an R^(2) value of 0.9376.Furthermore,SHapley-Additive-exPlanations and Partial-Dependence-Plot analyses were incorporated,revealing that the upstream head exerted the greatest influence on the discharge prediction,followed by PKW height and PKW key width.Therefore,these models are recommended as reliable,robust,and efficient tools for forecasting the discharge of symmetrical PKWs.Additionally,the mathematical expressions and associated script codes developed in this study are made accessible,thus providing hydraulic engineers and researchers with the means to perform rapid and accurate discharge predictions.
文摘The global shift towards sustainable and environmentally friendly transportation options has led to the increasing adoption of electric buses(Ebuses).To optimize the deployment and operational strategies of Ebuses,it is imperative to accurately predict their energy consumption under varying conditions,particularly in cold climates where battery life is typically degraded.The exploration of this aspect within the Canadian context has been limited.In addition,we have found that existing models in the literature perform poorly in the Canadian environment,giving rise to the need for new models using Canadian data.This paper focuses on the development,comparison,and evaluation of various data-driven models designed to predict the energy consumption of different Ebuses with different heating technologies under a wide range of climate conditions.We specifically use Canadian data as a good representative of cold climates in general.The results show that the performance of the different bus types varies substantially under the exact same conditions.In addition,tree-based family of models proves to be the most suitable approach for predicting the Ebus consumption rate.The results indicate that the Random Forest method emerges as the superior choice for predicting the energy consumption rate,with a resulting mean absolute error of 0.09–0.1 kWh/km observed across the different models.Furthermore,SHAP analysis shows that the main variables influencing the energy consumption rate depend on the type of heating system(using the battery for heating or using an auxiliary system that utilizes diesel for heating)adopted.
基金supported by National Natural Science Foundation of China(Nos.52271136 and 22373063)the Natural Science Foundation of Shaanxi Province in China(Nos.2021JC-06 and 2019TD-020)Fundamental Research Funds for the Central Universities of China(No.GK202203002).
文摘This study constructs 196 transition metals(TM)@S_(x)N_(y) single-atom catalysts(SACs)(x=0-4 and y=0-4)and employs the eXtreme Gradient Boosting(XGBoost)classification model in machine learning(ML)for effectively distinguishing qualified and unqualified catalysts.The prediction accuracy rate is high,up to 95%.The SHapley Additive exPlanations(SHAP)analysis reveals that the N≡N bond length and the number of outermost d electrons(N_(d))can well describe the nitrogen(N2)reduction reaction(NRR)activity.The relationships between N≡N,N_(d),the adsorption energies of different intermediates(ΔE_(*N_(2)),ΔE_(*N_(2)H),and ΔE_(*NH_(2))),the general descriptor(φ),and the Gibbs free energy of key steps(ΔG_(*N_(2)),ΔG_(*N_(2)-*N_(2)H),and ΔG_(*N_H(2)-*NH_(3)))indicate that moderate nitrogen activation can enhance the reaction activity.Among the 17 screened SACs,Mo@S3N1,and W@S_(3)N_(1) demonstrate the best catalytic performance,with limiting potential(U_(L))values of only-0.26 and-0.25 V under implicit solvation conditions.The electronic properties and variations in N≡N and TM-N bond lengths are investigated to reveal the origin of NRR activity.This study provides the decisive features and NRR dataset for ML research,as well as a feasible strategy for rational design of NRR SACs.
