Treating acid gases contained in natural gas by MDEA is used widely. But the efficiency of regeneration of the MDEA solution limited the development of this technology. An optimal temperature is necessary for regenera...Treating acid gases contained in natural gas by MDEA is used widely. But the efficiency of regeneration of the MDEA solution limited the development of this technology. An optimal temperature is necessary for regeneration of the MDEA solution using membrane distillation. The experiment results showed that the regeneration rate of MDEA rose with an increasing temperature. But the rate increased slowly after the regeneration temperature arrived at a certain value. This study can confirm that regeneration of the MDEA solution using membrane distillation is feasible. This technology provides more advantages as compared to conventional regeneration process.展开更多
基于反应力场(reactive force field,ReaxFF)的反应分子动力学模拟的结果分析具有挑战性。国际首个ReaxFF MD化学反应分析及可视化工具VARxMD(visulization and analysis of ReaxFF molecular dynamics)可自动生成不同时刻之间完整的化...基于反应力场(reactive force field,ReaxFF)的反应分子动力学模拟的结果分析具有挑战性。国际首个ReaxFF MD化学反应分析及可视化工具VARxMD(visulization and analysis of ReaxFF molecular dynamics)可自动生成不同时刻之间完整的化学反应列表,通过物种检索进一步对反应路径进行分类。但VARxMD目前的反应分析针对的是某一确定条件下单一的ReaxFF MD模拟轨迹,利用VARxMD分析获得一次模拟的完整反应列表需要消耗大量计算资源和时间。本文提出基于数据库来储存VARxMD反应分析结果数据,基于数据库检索进一步分析反应的思路,并采用MVVM(model-view-view model)的系统设计模式、结合渐进式框架Vue.js建立了ReaxFF MD模拟的化学反应数据系统ReaxMDDB(reaction database of ReaxFF MD simulation)。系统应用于多个RP-3模型热解和氧化模拟反应数据的结果表明:该系统不仅实现了多个ReaxFF MD模拟的详细反应的统一分析和化学反应的2D分子结构显示,而且可永久保存模拟获得的反应数据集以备后续进一步分析反应机理。ReaxMDDB具有很好的通用性,为认识不同反应模拟所揭示的共性化学反应机理提供了方便的平台。展开更多
In this work,the ternary azeotrope of tert-butyl alcohol/ethyl acetate/water is separated by extractive distillation(ED)to recover the available constituents and protect the environment.Based on the conductor like shi...In this work,the ternary azeotrope of tert-butyl alcohol/ethyl acetate/water is separated by extractive distillation(ED)to recover the available constituents and protect the environment.Based on the conductor like shielding model and relative volatility method,ethylene glycol was selected as the extractant in the separation process.In addition,in view of the characteristic that the relative volatility between components changes with pressure,the multi-objective optimization method based on nondominated sorting genetic algorithm II optimizes the pressure and the amount of solvent cooperatively to avoid falling into the optimal local solution.Based on the optimal process parameters,the proposed heat-integrated process can reduce the gas emissions by 29.30%.The heat-integrated ED,further coupled with the pervaporation process,can reduce gas emission by 42.36%and has the highest exergy efficiency of 47.56%.In addition,based on the heat-integrated process,the proposed two heat pump assisted heat-integrated ED processes show good economic and environmental performance.The double heat pump assisted heat-integrated ED can reduce the total annual cost by 28.78%and the gas emissions by 55.83%compared with the basis process,which has a good application prospect.This work provides a feasible approach for the separation of ternary azeotropes.展开更多
Adversarial distillation(AD)has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial training.However,fixed sample-agnostic and student-egocentric atta...Adversarial distillation(AD)has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial training.However,fixed sample-agnostic and student-egocentric attack strategies are unsuitable for distillation.Additionally,the reliability of guidance from static teachers diminishes as target models become more robust.This paper proposes an AD method called Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation(LDAS&ET-AD).Firstly,a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation.A strategy model is introduced to produce attack strategies that enable adversarial examples(AEs)to be created in areas where the target model significantly diverges from the teachers by competing with the target model in minimizing or maximizing the AD loss.Secondly,a teacher evolution strategy is introduced to enhance the reliability and effectiveness of knowledge in improving the generalization performance of the target model.By calculating the experimentally updated target model’s validation performance on both clean samples and AEs,the impact of distillation from each training sample and AE on the target model’s generalization and robustness abilities is assessed to serve as feedback to fine-tune standard and robust teachers accordingly.Experiments evaluate the performance of LDAS&ET-AD against different adversarial attacks on the CIFAR-10 and CIFAR-100 datasets.The experimental results demonstrate that the proposed method achieves a robust precision of 45.39%and 42.63%against AutoAttack(AA)on the CIFAR-10 dataset for ResNet-18 and MobileNet-V2,respectively,marking an improvement of 2.31%and 3.49%over the baseline method.In comparison to state-of-the-art adversarial defense techniques,our method surpasses Introspective Adversarial Distillation,the top-performing method in terms of robustness under AA attack for the CIFAR-10 dataset,with enhancements of 1.40%and 1.43%for ResNet-18 and MobileNet-V2,respectively.These findings demonstrate the effectiveness of our proposed method in enhancing the robustness of deep learning networks(DNNs)against prevalent adversarial attacks when compared to other competing methods.In conclusion,LDAS&ET-AD provides reliable and informative soft labels to one of the most promising defense methods,AT,alleviating the limitations of untrusted teachers and unsuitable AEs in existing AD techniques.We hope this paper promotes the development of DNNs in real-world trust-sensitive fields and helps ensure a more secure and dependable future for artificial intelligence systems.展开更多
文摘Treating acid gases contained in natural gas by MDEA is used widely. But the efficiency of regeneration of the MDEA solution limited the development of this technology. An optimal temperature is necessary for regeneration of the MDEA solution using membrane distillation. The experiment results showed that the regeneration rate of MDEA rose with an increasing temperature. But the rate increased slowly after the regeneration temperature arrived at a certain value. This study can confirm that regeneration of the MDEA solution using membrane distillation is feasible. This technology provides more advantages as compared to conventional regeneration process.
文摘基于反应力场(reactive force field,ReaxFF)的反应分子动力学模拟的结果分析具有挑战性。国际首个ReaxFF MD化学反应分析及可视化工具VARxMD(visulization and analysis of ReaxFF molecular dynamics)可自动生成不同时刻之间完整的化学反应列表,通过物种检索进一步对反应路径进行分类。但VARxMD目前的反应分析针对的是某一确定条件下单一的ReaxFF MD模拟轨迹,利用VARxMD分析获得一次模拟的完整反应列表需要消耗大量计算资源和时间。本文提出基于数据库来储存VARxMD反应分析结果数据,基于数据库检索进一步分析反应的思路,并采用MVVM(model-view-view model)的系统设计模式、结合渐进式框架Vue.js建立了ReaxFF MD模拟的化学反应数据系统ReaxMDDB(reaction database of ReaxFF MD simulation)。系统应用于多个RP-3模型热解和氧化模拟反应数据的结果表明:该系统不仅实现了多个ReaxFF MD模拟的详细反应的统一分析和化学反应的2D分子结构显示,而且可永久保存模拟获得的反应数据集以备后续进一步分析反应机理。ReaxMDDB具有很好的通用性,为认识不同反应模拟所揭示的共性化学反应机理提供了方便的平台。
基金supported by the National Natural Science Foundation of China(22178188).
文摘In this work,the ternary azeotrope of tert-butyl alcohol/ethyl acetate/water is separated by extractive distillation(ED)to recover the available constituents and protect the environment.Based on the conductor like shielding model and relative volatility method,ethylene glycol was selected as the extractant in the separation process.In addition,in view of the characteristic that the relative volatility between components changes with pressure,the multi-objective optimization method based on nondominated sorting genetic algorithm II optimizes the pressure and the amount of solvent cooperatively to avoid falling into the optimal local solution.Based on the optimal process parameters,the proposed heat-integrated process can reduce the gas emissions by 29.30%.The heat-integrated ED,further coupled with the pervaporation process,can reduce gas emission by 42.36%and has the highest exergy efficiency of 47.56%.In addition,based on the heat-integrated process,the proposed two heat pump assisted heat-integrated ED processes show good economic and environmental performance.The double heat pump assisted heat-integrated ED can reduce the total annual cost by 28.78%and the gas emissions by 55.83%compared with the basis process,which has a good application prospect.This work provides a feasible approach for the separation of ternary azeotropes.
基金the National Key Research and Development Program of China(2021YFB1006200)Major Science and Technology Project of Henan Province in China(221100211200).Grant was received by S.Li.
文摘Adversarial distillation(AD)has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial training.However,fixed sample-agnostic and student-egocentric attack strategies are unsuitable for distillation.Additionally,the reliability of guidance from static teachers diminishes as target models become more robust.This paper proposes an AD method called Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation(LDAS&ET-AD).Firstly,a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation.A strategy model is introduced to produce attack strategies that enable adversarial examples(AEs)to be created in areas where the target model significantly diverges from the teachers by competing with the target model in minimizing or maximizing the AD loss.Secondly,a teacher evolution strategy is introduced to enhance the reliability and effectiveness of knowledge in improving the generalization performance of the target model.By calculating the experimentally updated target model’s validation performance on both clean samples and AEs,the impact of distillation from each training sample and AE on the target model’s generalization and robustness abilities is assessed to serve as feedback to fine-tune standard and robust teachers accordingly.Experiments evaluate the performance of LDAS&ET-AD against different adversarial attacks on the CIFAR-10 and CIFAR-100 datasets.The experimental results demonstrate that the proposed method achieves a robust precision of 45.39%and 42.63%against AutoAttack(AA)on the CIFAR-10 dataset for ResNet-18 and MobileNet-V2,respectively,marking an improvement of 2.31%and 3.49%over the baseline method.In comparison to state-of-the-art adversarial defense techniques,our method surpasses Introspective Adversarial Distillation,the top-performing method in terms of robustness under AA attack for the CIFAR-10 dataset,with enhancements of 1.40%and 1.43%for ResNet-18 and MobileNet-V2,respectively.These findings demonstrate the effectiveness of our proposed method in enhancing the robustness of deep learning networks(DNNs)against prevalent adversarial attacks when compared to other competing methods.In conclusion,LDAS&ET-AD provides reliable and informative soft labels to one of the most promising defense methods,AT,alleviating the limitations of untrusted teachers and unsuitable AEs in existing AD techniques.We hope this paper promotes the development of DNNs in real-world trust-sensitive fields and helps ensure a more secure and dependable future for artificial intelligence systems.
