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
精确构建了试剂体系,采用分阶段调控pH值策略制备镍基催化剂前驱体。在污水处理优化阶段,使用四维参数响应曲面优化方法,通过湿式浸渍法分散催化剂,利用响应曲面法设计实验,实施动态调控策略。结果表明:在温度为25℃、pH值为10和次氯酸...精确构建了试剂体系,采用分阶段调控pH值策略制备镍基催化剂前驱体。在污水处理优化阶段,使用四维参数响应曲面优化方法,通过湿式浸渍法分散催化剂,利用响应曲面法设计实验,实施动态调控策略。结果表明:在温度为25℃、pH值为10和次氯酸钠用量为15 m L时,去除率最高,达99.5%。镍基催化剂累计补加量在30~40 g范围内的去除率最高,达98.5%。研究提出的尿素共沉淀法合成的镍基催化剂能优化次氯酸钠分解速率和氧化性能,提高污水处理效果。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
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.
文摘精确构建了试剂体系,采用分阶段调控pH值策略制备镍基催化剂前驱体。在污水处理优化阶段,使用四维参数响应曲面优化方法,通过湿式浸渍法分散催化剂,利用响应曲面法设计实验,实施动态调控策略。结果表明:在温度为25℃、pH值为10和次氯酸钠用量为15 m L时,去除率最高,达99.5%。镍基催化剂累计补加量在30~40 g范围内的去除率最高,达98.5%。研究提出的尿素共沉淀法合成的镍基催化剂能优化次氯酸钠分解速率和氧化性能,提高污水处理效果。
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%.
