The dynamic analysis and optimal design of reactive extraction are challenging due to high nonlinearity of model equations and tough decision of judging criteria. In this work, a dynamic rate-based method is developed...The dynamic analysis and optimal design of reactive extraction are challenging due to high nonlinearity of model equations and tough decision of judging criteria. In this work, a dynamic rate-based method is developed on g PROMS platform to get easy access to the solutions of reactive extraction with phase splitting. Based on rigorous criteria, dynamic analysis from initial state to final equilibrium(e.g., evolution of phase composition, mass transfer rate and reaction rate) and optimal design of operating conditions(e.g., extractant dosage and feed molar ratio) are achieved. To illustrate the method, the esterification of n-hexyl acetate is taken as an example. The approach proves to be reliable in the analysis and optimization of the exemplified system, which provides instructive reference for further process design and simulation of reactive extraction.展开更多
Background:The accurate identification of cardiac abnormalities is essential for proper diagnosis and effective treatment of cardiovascular diseases.Method:This work introduces an advanced methodology for detecting ca...Background:The accurate identification of cardiac abnormalities is essential for proper diagnosis and effective treatment of cardiovascular diseases.Method:This work introduces an advanced methodology for detecting cardiac abnormalities and estimating electrocardiographic age(ECG Age)using sophisticated signal processing and deep learning techniques.This study looks at six main heart conditions found in 12-lead electrocardiogram(ECG)data.It addresses important issues like class imbalances,missing lead scenarios,and model generalizations.A modified residual neural network(ResNet)architecture was developed to enhance the detection of cardiac abnormalities.Results:The proposed ResNet demonst rated superior performance when compared with two linear models and an alternative ResNet architectures,achieving an overall classification accuracy of 91.25%and an F1 score of 93.9%,surpassing baseline models.A comprehensive lead loss analysis was conducted,evaluating model performance across 4096 combinations of missing leads.The results revealed that pulse rate-based factors remained robust with up to 75%lead loss,while block-based factors experienced significant performance declines beyond the loss of four leads.Conclusion:This analysis highlighted the importance of addressing lead loss impacts to maintain a robust model.To optimize performance,targeted training approaches were developed for different conditions.Based on these insights,a grouping strategy was implemented to train specialized models for pulse rate-based and block-based conditions.This approach resulted in notable improvements,achieving an overall classification accuracy of 95.12%and an F1 score of 95.79%.展开更多
GH4586是一种广泛应用于航空发动机结构件的高温合金材料,其裂纹扩展特性对于分析和评价结构寿命有重要工程意义。针对航空发动机涡轮盘的实际工作温度开展了480℃和620℃条件下的裂纹扩展速率试验(包括定K(Constant-Force-Amplitude Te...GH4586是一种广泛应用于航空发动机结构件的高温合金材料,其裂纹扩展特性对于分析和评价结构寿命有重要工程意义。针对航空发动机涡轮盘的实际工作温度开展了480℃和620℃条件下的裂纹扩展速率试验(包括定K(Constant-Force-Amplitude Test Procedure)和降K(K-Decreasing Procedure)试验)。基于Pairs公式,对两种温度下的Pairs参数n和C以及门槛值进行了分析和计算;通过对断口扫描电子显微镜(SEM)分析,发现试验温度越高,材料表现出的韧性特征越明显。降K试样由于ΔK(裂纹尖端有效驱动力)值不断减小,影响塑性区的形成,导致其韧性特征表现减弱,而定K试样由于ΔK值稳定,裂纹尖端塑性区较为明显,因此表现出更多的韧性特征。展开更多
基金Supported by the National Natural Science Foundation of China(21776074,21576081,2181101120).
文摘The dynamic analysis and optimal design of reactive extraction are challenging due to high nonlinearity of model equations and tough decision of judging criteria. In this work, a dynamic rate-based method is developed on g PROMS platform to get easy access to the solutions of reactive extraction with phase splitting. Based on rigorous criteria, dynamic analysis from initial state to final equilibrium(e.g., evolution of phase composition, mass transfer rate and reaction rate) and optimal design of operating conditions(e.g., extractant dosage and feed molar ratio) are achieved. To illustrate the method, the esterification of n-hexyl acetate is taken as an example. The approach proves to be reliable in the analysis and optimization of the exemplified system, which provides instructive reference for further process design and simulation of reactive extraction.
文摘Background:The accurate identification of cardiac abnormalities is essential for proper diagnosis and effective treatment of cardiovascular diseases.Method:This work introduces an advanced methodology for detecting cardiac abnormalities and estimating electrocardiographic age(ECG Age)using sophisticated signal processing and deep learning techniques.This study looks at six main heart conditions found in 12-lead electrocardiogram(ECG)data.It addresses important issues like class imbalances,missing lead scenarios,and model generalizations.A modified residual neural network(ResNet)architecture was developed to enhance the detection of cardiac abnormalities.Results:The proposed ResNet demonst rated superior performance when compared with two linear models and an alternative ResNet architectures,achieving an overall classification accuracy of 91.25%and an F1 score of 93.9%,surpassing baseline models.A comprehensive lead loss analysis was conducted,evaluating model performance across 4096 combinations of missing leads.The results revealed that pulse rate-based factors remained robust with up to 75%lead loss,while block-based factors experienced significant performance declines beyond the loss of four leads.Conclusion:This analysis highlighted the importance of addressing lead loss impacts to maintain a robust model.To optimize performance,targeted training approaches were developed for different conditions.Based on these insights,a grouping strategy was implemented to train specialized models for pulse rate-based and block-based conditions.This approach resulted in notable improvements,achieving an overall classification accuracy of 95.12%and an F1 score of 95.79%.
文摘GH4586是一种广泛应用于航空发动机结构件的高温合金材料,其裂纹扩展特性对于分析和评价结构寿命有重要工程意义。针对航空发动机涡轮盘的实际工作温度开展了480℃和620℃条件下的裂纹扩展速率试验(包括定K(Constant-Force-Amplitude Test Procedure)和降K(K-Decreasing Procedure)试验)。基于Pairs公式,对两种温度下的Pairs参数n和C以及门槛值进行了分析和计算;通过对断口扫描电子显微镜(SEM)分析,发现试验温度越高,材料表现出的韧性特征越明显。降K试样由于ΔK(裂纹尖端有效驱动力)值不断减小,影响塑性区的形成,导致其韧性特征表现减弱,而定K试样由于ΔK值稳定,裂纹尖端塑性区较为明显,因此表现出更多的韧性特征。
