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
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
A huge amount of energy is always consumed to separate the ternary azeotropic mixtures by distillations.The heterogeneous azeotropic distillation and the pressure-swing distillation are two kinds of effective technolo...A huge amount of energy is always consumed to separate the ternary azeotropic mixtures by distillations.The heterogeneous azeotropic distillation and the pressure-swing distillation are two kinds of effective technologies to separate heterogeneous azeotropes without entrainer addition.To give better play to the synergistic energy-saving effect of these two processes,a novel pressure-swing-assisted ternary heterogeneous azeotropic distillation(THAD)process is proposed firstly.In this process,the ternary heterogeneous azeotrope is decanted into two liquid phases before being refluxed into the azeotropic distillation column to avoid the aqueous phase remixing,and three columns'pressures are modified to decrease the flowrates of the recycle streams.Then the dividing wall column and heat integration technologies are introduced to further reduce its energy consumption,and the pressureswing-assisted ternary heterogeneous azeotropic dividing-wall column and its heat integration structure are achieved.A genetic algorithm procedure is used to optimize the proposed processes.The design results show that the proposed processes have higher energy efficiencies and lower CO_(2)emissions than the published THAD process.展开更多
Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computati...Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network(TDKD-Net) is proposed,where the TT-format TD(tensor decomposition) and equalweighted response-based KD(knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU(intersection of union) loss with optimal transport assignment(F-EIoU-OTA)mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.展开更多
A green and effective electrolytic process was developed to produce high-purity Mg metal using primary and secondary resources containing Mg O as a feedstock. The electrolysis of various Mg O resources was conducted u...A green and effective electrolytic process was developed to produce high-purity Mg metal using primary and secondary resources containing Mg O as a feedstock. The electrolysis of various Mg O resources was conducted using a Cu cathode in MgF2– LiF – KCl molten salt at 1043 K by applying an average current of 1.44 A for 12.5 h. The electrolysis of calcined North Korean magnesite and seawater Mg O clinker yielded Mg alloys of MgCu2and(Cu) phases with current efficiencies of 89.6–92.4%. The electrolysis of oxidized Mg O-C refractory brick, aged ferronickel slag, and ferronickel slag yielded Mg alloys of MgCu2and(Cu) phases with current efficiencies of 59.3–92.3%. The vacuum distillation of Mg alloys obtained was conducted at 1300 K for 10 h to produce high-purity Mg metal. After vacuum distillation, Mg metal with a purity of above 99.994% was obtained. Therefore, this study demonstrates the feasibility of the production of high-purity Mg metal from various Mg O resources using a novel electrolytic process with a Cu cathode, followed by vacuum distillation.展开更多
This wok proposed the extraction distillation coupled pervaporation(ED+PV) technology process using two different solvents to separate isopropanol(IPA) and diisopropyl ether(DIPE) from DIPE/IPA/H_(2)O ternary heteroge...This wok proposed the extraction distillation coupled pervaporation(ED+PV) technology process using two different solvents to separate isopropanol(IPA) and diisopropyl ether(DIPE) from DIPE/IPA/H_(2)O ternary heterogeneous azeotropes in industrial wastewater from the synthesis of isopropanol in this study.Based on strict design specifications, simulation and sequential iteration methods are used for process design and optimization. Compared to the ethylene glycol(EG)-EG+H_(2)O process and the 1,3-propanediol(PDO)-IPA+H_(2)O process, the total annual cost(TAC) of the EG-IPA+H_(2)O process decreased by 20.76% and 7.86%(PDO). Compared to the EG-EG+H_(2)O process, the TAC of the PDO-IPA+H_(2)O process reduced 14%, but the global warming potential(GWP) and human toxicity of the PDO-IPA+H_(2)O process increased 11.3% and 4.07% respectively. Compared to the PDO-IPA+H_(2)O process, the EG-IPA+H_(2)O process saves 7.86%(TAC), 9.78%(GWP) and 9.85%(human toxicity). The ED+PV process with EG is superior to PDO in factors of TAC, energy consumption, human toxicity and environment. The EG-IPA+H_(2)O process changed the separation order of the products of the multi-azeotropic system, reduced the cost and energy conservation of the system, and enhanced the environmental protection evaluation of the process, is the best process through life cycle assessment for analyzing the economy, energy conservation, environmental assessment and human toxicity, designing cleaner products, controlling waste discharge, and promoting the chemical purification industry. This work provides a new process design and optimized separation ideas, will have a good guiding significance for the research and application separation of multi-azeotropic mixture with mixed solvents in organic wastewater from the cleaner chemical production, has been up to standard wastewater discharge process, and realized the development goal of carbon peak and carbon neutrality in the sustainable development of chemical clean industry.展开更多
Extractive distillation(ED)and solvent-assisted pressure-swing distillation(SA-PSD)are both special distillation processes that perform good at separating pressure-insensitive azeotropes.However,few reported studies h...Extractive distillation(ED)and solvent-assisted pressure-swing distillation(SA-PSD)are both special distillation processes that perform good at separating pressure-insensitive azeotropes.However,few reported studies have compared the performance of the two processes.In this paper,ED processes with N-methylpyrrolidone(NMP)and dimethlac-etamide(DMCA)as entrainer,SA-PSD process with isopropyl-alcohol(IPA)as solvent and SA-PSD process with partial heat integration(PHI-PSD)are proposed to achieve high purity separation of a mixture of cyclohexane/2-butanol system.The optimal operating conditions of the processes are obtained after optimizing with NSGA-Ⅱ algorithm when total annual cost(TAC)and the entropy production of process are set as objectives.The optimal results show that the optimal PHI-PSD process has lower TAC by 28.7% and the lower entropy production by 39.5% than the optimal SA-PSD process while the ED process with NMP as entrainer has lower TAC by 50.9% and the lower entropy production by 56.1% than the optimal SA-PSD process.The optimal results show that the ED process with NMP as entrainer has the best economic and thermodynamic efficiency among the four proposed processes in this paper.展开更多
The conventional distillation is hard to accomplish the separation of acetonitrile/ethyl acetate/n-hexane mixture. Herein, a heterogeneous azeotropic distillation(HAD) without adding entrainer is proposed to separate ...The conventional distillation is hard to accomplish the separation of acetonitrile/ethyl acetate/n-hexane mixture. Herein, a heterogeneous azeotropic distillation(HAD) without adding entrainer is proposed to separate ternary mixture. The proposed scheme is optimized via the simulated annealing algorithm and minimum total annual cost(TAC) is used as objective functions. To minimize energy consumption,heat pump is added on the basis of optimal heterogeneous azeotropic distillation and heat integration technology is used to further improve the energy recovery. The TAC, gas emission, energy consumption and exergy destruction are used to discuss the economy and environmental protection of processes.Among all the processes, the heat pump with higher preheating temperature(HPT) assisted HAD process by combining with heat integration(HAD-HPT-HI) has best performances on economic, environment,energy and exergy. Compared with conventional HAD process, the HAD-HPT-HI achieves the reductions of 52.17%, 68.86%, 65.87% and 65.46% on TAC, total energy consumption, gas emissions and exergy destruction, respectively.展开更多
A large amount of waste liquids containing methanol/acetone/water mixtures are produced in the synthesis of methyl methacrylate(MMA).Under the advocacy of green chemical industry,it is urgent to develop an efficient,e...A large amount of waste liquids containing methanol/acetone/water mixtures are produced in the synthesis of methyl methacrylate(MMA).Under the advocacy of green chemical industry,it is urgent to develop an efficient,economic and energy-saving mixture separation process.Through thermodynamic azeotropic behavior and pressure sensitivity analysis,pressure-swing distillation was determined and the optimal separation pressure of each column in the process was obtained.Due to the composition of waste liquids produced were quite different in MMA production,the pressure-swing distillation separation process was designed to fully achieve the accurate waste liquids treatment.Taking the total annual cost(TAC)as the target,the sequential iteration method was used to optimize the process,and the impact of composition on economy was compared.In order to further realize the energy-saving of the separation process,the pervaporation membrane module was introduced to pretreat the waste liquid in the pressure-swing distillation.The results showed that the TAC of the coupling process was 46% higher than that of the pressure-swing distillation process,and the thermodynamic efficiency was 30% higher.This study provides waste liquid treatment technology for enterprises and analyzes its economic and energy efficiency,which has reference significance for the development of coupled separation technology.展开更多
In this paper,to deal with the heterogeneity in federated learning(FL)systems,a knowledge distillation(KD)driven training framework for FL is proposed,where each user can select its neural network model on demand and ...In this paper,to deal with the heterogeneity in federated learning(FL)systems,a knowledge distillation(KD)driven training framework for FL is proposed,where each user can select its neural network model on demand and distill knowledge from a big teacher model using its own private dataset.To overcome the challenge of train the big teacher model in resource limited user devices,the digital twin(DT)is exploit in the way that the teacher model can be trained at DT located in the server with enough computing resources.Then,during model distillation,each user can update the parameters of its model at either the physical entity or the digital agent.The joint problem of model selection and training offloading and resource allocation for users is formulated as a mixed integer programming(MIP)problem.To solve the problem,Q-learning and optimization are jointly used,where Q-learning selects models for users and determines whether to train locally or on the server,and optimization is used to allocate resources for users based on the output of Q-learning.Simulation results show the proposed DT-assisted KD framework and joint optimization method can significantly improve the average accuracy of users while reducing the total delay.展开更多
With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant trend.The existing fault diagnosismethods have the characteristics of heavy computing and st...With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant trend.The existing fault diagnosismethods have the characteristics of heavy computing and storage load,and most of them have computational redundancy,which is not suitable for deployment on edge devices with limited resources and capabilities.This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation.First,we offer a clustering-based self-knowledge distillation approach(Cluster KD),which takes the mean value of the sample diagnosis results,clusters them,and takes the clustering results as the terms of the loss function.It utilizes the correlations between faults of the same type to improve the accuracy of the teacher model,especially for fault categories with high similarity.Then,the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweightmodel for edge-side deployment.We propose a two-stage edge-side fault diagnosismethod(TSM)that separates fault detection and fault diagnosis into different stages:in the first stage,a fault detection model based on a denoising auto-encoder(DAE)is adopted to achieve fast fault responses;in the second stage,a diverse convolutionmodel with variance weighting(DCMVW)is used to diagnose faults in detail,extracting features frommicro andmacro perspectives.Through comparison experiments conducted on two fault datasets,it is proven that the proposed method has high accuracy,low delays,and small computation,which is suitable for intelligent edge-side fault diagnosis.In addition,experiments show that our approach has a smooth training process and good balance.展开更多
The catalytic packing is the core component of the catalytic distillation,and how the catalyst exists in the packing has significant influence on the process.To investigate the effect of catalyst packings on the catal...The catalytic packing is the core component of the catalytic distillation,and how the catalyst exists in the packing has significant influence on the process.To investigate the effect of catalyst packings on the catalytic distillation process,the classical ethyl acetate reactive distillation system was utilized,and a supported catalytic packing(SCP)was prepared in comparison with the conventional tea-bag catalytic packing(TBP).Laboratory scale experiments showed that the ethyl acetate conversion of the SCP was superior to the TBP at a low catalyst loading.The effects of reaction kinetics,mass transfer performance and actual catalytic efficiency of the packings on this process were regarded as reasons and studied by combining the experiments and numerical simulation.Results suggested that the relatively immediate“in-situ separation”caused by the rapid reaction kinetics and better mass transfer performance of SCP may be a main reason for the difference of the conversion.展开更多
Atmospheric distillation is the first step in separating crude oil into by-products. It uses the different boiling temperatures of the components of crude oil to separate them. But crude oil contains a large quantity ...Atmospheric distillation is the first step in separating crude oil into by-products. It uses the different boiling temperatures of the components of crude oil to separate them. But crude oil contains a large quantity of acids and corrosive gases, including sulfur compounds, naphthenic acids, carbon dioxide, oxygen, etc. However, the temperature has an important influence on the aggressiveness of the corrosion factors in the atmospheric distillation column. This paper aims to investigate the role of temperature on corrosive products in the atmospheric distillation column. The results of the developed model show that the temperature increases the corrosion rate in the atmospheric distillation column but above a certain temperature value (about 600 K), it decreases. This illustrates the dual role played by temperature in the study of corrosion within the atmospheric distillation column.展开更多
Load forecasting is a crucial aspect of intelligent Virtual Power Plant(VPP)management and ameans of balancing the relationship between distributed power grids and traditional power grids.However,due to the continuous...Load forecasting is a crucial aspect of intelligent Virtual Power Plant(VPP)management and ameans of balancing the relationship between distributed power grids and traditional power grids.However,due to the continuous emergence of power consumption peaks,the power supply quality of the power grid cannot be guaranteed.Therefore,an intelligent calculation method is required to effectively predict the load,enabling better power grid dispatching and ensuring the stable operation of the power grid.This paper proposes a decentralized heterogeneous federated distillation learning algorithm(DHFDL)to promote trusted federated learning(FL)between different federates in the blockchain.The algorithm comprises two stages:common knowledge accumulation and personalized training.In the first stage,each federate on the blockchain is treated as ameta-distribution.After aggregating the knowledge of each federate circularly,the model is uploaded to the blockchain.In the second stage,other federates on the blockchain download the trained model for personalized training,both of which are based on knowledge distillation.Experimental results demonstrate that the DHFDL algorithmproposed in this paper can resist a higher proportion of malicious code compared to FedAvg and a Blockchain-based Federated Learning framework with Committee consensus(BFLC).Additionally,by combining asynchronous consensus with the FL model training process,the DHFDL training time is the shortest,and the training efficiency of decentralized FL is improved.展开更多
In this study,a novel Mg production process for producing high-purity Mg metal from dolomite was developed.When the electrolysis of calcined dolomite was conducted using Cu cathode and C anode in MgF_(2)–LiF molten s...In this study,a novel Mg production process for producing high-purity Mg metal from dolomite was developed.When the electrolysis of calcined dolomite was conducted using Cu cathode and C anode in MgF_(2)–LiF molten salt at 1083–1173 K by applying an average current of 1.42–1.46 A for 9.50–21.0 h,the current efficiency of 66.4–88.6%was obtained.The produced Mg alloys consisted of MgCu_(2)and Cu(Mg)or MgCu_(2)and CuMg_(2)phases,depending on the Mg concentration in the Mg alloy.When the electrolysis of calcined dolomite was conducted in MgF_(2)–LiF–CaF_(2)molten salt at 1083 K,the current efficiency was 40.9–71.4%,owing to undesired reactions such as electroreduction of Ca^(2+)or/and CO_(3)^(2−)ions.Meanwhile,the current efficiency increased from 40.9%to 63.2%by utilizing a Pt anode,because the occurrence of CO_(3)^(2−)ions in the molten salt was prevented.After vacuum distillation of the obtained Mg alloys at 1300 K for 10 h,Mg metal with a purity of 99.9996–99.9998%was produced.Therefore,the feasibility of this novel process for the production of high-purity Mg metal from dolomite was demonstrated.展开更多
Nervonic acid is the world’s first and only potent substance that can repair damaged nerve fibers and promote nerve cell regeneration with high nutritional value.The wide variety of fatty acids in plant oils and fats...Nervonic acid is the world’s first and only potent substance that can repair damaged nerve fibers and promote nerve cell regeneration with high nutritional value.The wide variety of fatty acids in plant oils and fats with similar structures makes the large-scale separation and purification of high-purity nervonic acid very difficult.A new combined process of molecular distillation,urea inclusion and solvent crystallization was established to prepare high-purity nervonic acid with the mixed fatty acids obtained after saponification and acidification of Acer truncatum Bunge oil as raw materials.First,according to the difference in the mean free path of fatty acids,molecular distillation was used to separate and remove C16 saturated fatty acid of palmitic acid and four C18-C20 fatty acids of stearic,oleic,linoleic,and linolenic acids.The content of C16-C20 fatty acids decreased from 72.92% to 19.22% after two-stage molecular distillation processes,in which the contents of saturated fatty acid of palmitic acid decreased to about 0.5%.Then,according to the difference in carbon chain length and saturation of fatty acid,the contents of C22-C24 saturated fatty acids of tetracosanoic and docosanoic acids decreased to 0.21% and 0.07% by urea inclusion with urea/free fatty acid preparation by saponification(SPOMFs)ratio as 0.6.In addition,all saturated fatty acids were basically separated.Finally,according to the difference in the solubility of fatty acids in solvents,the C18-C20 unsaturated fatty acids of oleic,linoleic,and linolenic acids and C22 unsaturated fatty acid of erucic acid were removed by solvent crystallization.The content of C18-C20 unsaturated fatty acids decreased to less than 5% with pentanol as the solvent after the first stage solvent crystallization.The content of erucic acid decreased to 3.47% with anhydrous ethanol as the solvent after the second to fifth stage solvent crystallization.The combined process of molecular distillation,urea inclusion and low temperature crystallization innovatively adopted an efficient,simple and easy-toindustrial solvent crystallization method to separate erucic and nervonic acids,obtaining nervonic acid with purity of 96.53% and final yield of 47.99%.展开更多
For dividing-wall distillation columns(DWDCs) separating a heavy-component dominated and wide boiling-point ternary(HCDWBT) mixture, a significant amount of excessive heat exists inevitably in stripping the heavy-comp...For dividing-wall distillation columns(DWDCs) separating a heavy-component dominated and wide boiling-point ternary(HCDWBT) mixture, a significant amount of excessive heat exists inevitably in stripping the heavy-component from the intermediate-component and it can be employed to initiate the development of vapor recompression heat pump(VRHP) assisted DWDC(VRHP-DWDC). Despite dividing wall may locate in the top, middle, and bottom, the optimum VRHP-DWDC is found to involve uniformlytwo VRHP circles. While the first one serves to compress and transform the excessive heat resulted from the separation of the heavy-component from the intermediate-component, the second one to compress and transform the overhead vapor stream of the light-component pre-heated sequentially with the condensate from the first one and the bottom product stream of the heavy-component, both releasing the temperature-elevated latent heat to the pre-fractionator's or common stripping section. The processing of two HCDWBT mixtures of benzene/toluene/o-xylene and n-pentane/n-hexane/n-heptane are selected to assess the derived optimum topological configurations of the VRHP-DWDC and their optimality is confirmed through detailed comparisons with the DWDC and two VRHP-DWDCs involving only one VRHP circle. The proposed strategy helps to tap the full potential of the VRHP-DWDC with considerably alleviated complication in process development.展开更多
Monte Carlo simulation of gamma photon transport and interaction with the distillation column and its contents was performed in order to predict the effects of gamma photons when they interact with matter. The results...Monte Carlo simulation of gamma photon transport and interaction with the distillation column and its contents was performed in order to predict the effects of gamma photons when they interact with matter. The results of the interaction and transport of gamma photons are presented as energy deposition on the distillation column and its contents. Energy attenuation was more pronounced on the column walls and trays as compared to the region between the trays, where there is mostly vapour space. Gamma column scanning was then used to verify the Monte Carlo simulation results by scanning and investigating the integrity of two laboratory prototype distillation and industrial distillation columns. One of the prototype distillation columns was 1 m tall with four trays and the other one was 1.8 m tall consisting of six trays and a packed bed. Commonly encountered distillation column malfunctions such as collapsed tray, weeping, flooding and foaming were simulated in the two prototype distillation columns and scanned. The industrial distillation column was a 26 m tall benzole prefractionator column, consisting of 60 single pass trays and a diameter of 0.8 m. A 10 mCi <sup>60</sup>Co gamma radiation source and NaI(Tl) scintillation detector were used to scan the distillation columns. The results from the two prototypes showed that all the simulated malfunctions were clearly detected except for foaming. The results from industrial distillation column showed that all the trays were in their correct position although tray number 32 could be partially damaged and just below tray 41, the scan revealed that there was a loss of column wall thickness. The obtained density profile for the industrial distillation column showed some small variations from the expected density profile and this was attributed to external features on the distillation column and wind bursts that shifted the source and detector from the chosen scan line orientation.展开更多
Monte Carlo simulation of gamma photon transport and interaction with the distillation column and its contents was performed in order to predict the effects of gamma photons when they interact with matter. The results...Monte Carlo simulation of gamma photon transport and interaction with the distillation column and its contents was performed in order to predict the effects of gamma photons when they interact with matter. The results of the interaction and transport of gamma photons are presented as energy deposition on the distillation column and its contents. Energy attenuation was more pronounced on the column walls and trays as compared to the region between the trays, where there is mostly vapour space. Gamma column scanning was then used to verify the Monte Carlo simulation results by scanning and investigating the integrity of two laboratory prototype distillation and industrial distillation columns. One of the prototype distillation columns was 1 m tall with four trays and the other one was 1.8 m tall consisting of six trays and a packed bed. Commonly encountered distillation column malfunctions such as collapsed tray, weeping, flooding and foaming were simulated in the two prototype distillation columns and scanned. The industrial distillation column was a 26 m tall benzole prefractionator column, consisting of 60 single pass trays and a diameter of 0.8 m. A 10 mCi <sup>60</sup>Co gamma radiation source and NaI(Tl) scintillation detector were used to scan the distillation columns. The results from the two prototypes showed that all the simulated malfunctions were clearly detected except for foaming. The results from industrial distillation column showed that all the trays were in their correct position although tray number 32 could be partially damaged and just below tray 41, the scan revealed that there was a loss of column wall thickness. The obtained density profile for the industrial distillation column showed some small variations from the expected density profile and this was attributed to external features on the distillation column and wind bursts that shifted the source and detector from the chosen scan line orientation.展开更多
Recently, deep convolutional neural networks (DCNNs) have achieved remarkable results in image classification tasks. Despite convolutional networks’ great successes, their training process relies on a large amount of...Recently, deep convolutional neural networks (DCNNs) have achieved remarkable results in image classification tasks. Despite convolutional networks’ great successes, their training process relies on a large amount of data prepared in advance, which is often challenging in real-world applications, such as streaming data and concept drift. For this reason, incremental learning (continual learning) has attracted increasing attention from scholars. However, incremental learning is associated with the challenge of catastrophic forgetting: the performance on previous tasks drastically degrades after learning a new task. In this paper, we propose a new strategy to alleviate catastrophic forgetting when neural networks are trained in continual domains. Specifically, two components are applied: data translation based on transfer learning and knowledge distillation. The former translates a portion of new data to reconstruct the partial data distribution of the old domain. The latter uses an old model as a teacher to guide a new model. The experimental results on three datasets have shown that our work can effectively alleviate catastrophic forgetting by a combination of the two methods aforementioned.展开更多
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.
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supported by the National Natural Science Foundation of China(21406170)the State Key Laboratory of Chemical Engineering(SKL-ChE-22B02).
文摘A huge amount of energy is always consumed to separate the ternary azeotropic mixtures by distillations.The heterogeneous azeotropic distillation and the pressure-swing distillation are two kinds of effective technologies to separate heterogeneous azeotropes without entrainer addition.To give better play to the synergistic energy-saving effect of these two processes,a novel pressure-swing-assisted ternary heterogeneous azeotropic distillation(THAD)process is proposed firstly.In this process,the ternary heterogeneous azeotrope is decanted into two liquid phases before being refluxed into the azeotropic distillation column to avoid the aqueous phase remixing,and three columns'pressures are modified to decrease the flowrates of the recycle streams.Then the dividing wall column and heat integration technologies are introduced to further reduce its energy consumption,and the pressureswing-assisted ternary heterogeneous azeotropic dividing-wall column and its heat integration structure are achieved.A genetic algorithm procedure is used to optimize the proposed processes.The design results show that the proposed processes have higher energy efficiencies and lower CO_(2)emissions than the published THAD process.
基金supported in part by the National Natural Science Foundation of China (62073271)the Natural Science Foundation for Distinguished Young Scholars of the Fujian Province of China (2023J06010)the Fundamental Research Funds for the Central Universities of China(20720220076)。
文摘Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network(TDKD-Net) is proposed,where the TT-format TD(tensor decomposition) and equalweighted response-based KD(knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU(intersection of union) loss with optimal transport assignment(F-EIoU-OTA)mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.
基金supported by the Korea Evaluation Institute of Industrial Technology funded by the Korean Ministry of Industry in Korea (Project No.:20000970, 20–9805)Basic Research Project (22–3803) of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Science and ICT of Korea。
文摘A green and effective electrolytic process was developed to produce high-purity Mg metal using primary and secondary resources containing Mg O as a feedstock. The electrolysis of various Mg O resources was conducted using a Cu cathode in MgF2– LiF – KCl molten salt at 1043 K by applying an average current of 1.44 A for 12.5 h. The electrolysis of calcined North Korean magnesite and seawater Mg O clinker yielded Mg alloys of MgCu2and(Cu) phases with current efficiencies of 89.6–92.4%. The electrolysis of oxidized Mg O-C refractory brick, aged ferronickel slag, and ferronickel slag yielded Mg alloys of MgCu2and(Cu) phases with current efficiencies of 59.3–92.3%. The vacuum distillation of Mg alloys obtained was conducted at 1300 K for 10 h to produce high-purity Mg metal. After vacuum distillation, Mg metal with a purity of above 99.994% was obtained. Therefore, this study demonstrates the feasibility of the production of high-purity Mg metal from various Mg O resources using a novel electrolytic process with a Cu cathode, followed by vacuum distillation.
基金supported by the National Natural Science Foundation of China (21776145 and 21808117)。
文摘This wok proposed the extraction distillation coupled pervaporation(ED+PV) technology process using two different solvents to separate isopropanol(IPA) and diisopropyl ether(DIPE) from DIPE/IPA/H_(2)O ternary heterogeneous azeotropes in industrial wastewater from the synthesis of isopropanol in this study.Based on strict design specifications, simulation and sequential iteration methods are used for process design and optimization. Compared to the ethylene glycol(EG)-EG+H_(2)O process and the 1,3-propanediol(PDO)-IPA+H_(2)O process, the total annual cost(TAC) of the EG-IPA+H_(2)O process decreased by 20.76% and 7.86%(PDO). Compared to the EG-EG+H_(2)O process, the TAC of the PDO-IPA+H_(2)O process reduced 14%, but the global warming potential(GWP) and human toxicity of the PDO-IPA+H_(2)O process increased 11.3% and 4.07% respectively. Compared to the PDO-IPA+H_(2)O process, the EG-IPA+H_(2)O process saves 7.86%(TAC), 9.78%(GWP) and 9.85%(human toxicity). The ED+PV process with EG is superior to PDO in factors of TAC, energy consumption, human toxicity and environment. The EG-IPA+H_(2)O process changed the separation order of the products of the multi-azeotropic system, reduced the cost and energy conservation of the system, and enhanced the environmental protection evaluation of the process, is the best process through life cycle assessment for analyzing the economy, energy conservation, environmental assessment and human toxicity, designing cleaner products, controlling waste discharge, and promoting the chemical purification industry. This work provides a new process design and optimized separation ideas, will have a good guiding significance for the research and application separation of multi-azeotropic mixture with mixed solvents in organic wastewater from the cleaner chemical production, has been up to standard wastewater discharge process, and realized the development goal of carbon peak and carbon neutrality in the sustainable development of chemical clean industry.
基金supported by the National Natural Science Foundation of China(22178030,21878025,22078026)。
文摘Extractive distillation(ED)and solvent-assisted pressure-swing distillation(SA-PSD)are both special distillation processes that perform good at separating pressure-insensitive azeotropes.However,few reported studies have compared the performance of the two processes.In this paper,ED processes with N-methylpyrrolidone(NMP)and dimethlac-etamide(DMCA)as entrainer,SA-PSD process with isopropyl-alcohol(IPA)as solvent and SA-PSD process with partial heat integration(PHI-PSD)are proposed to achieve high purity separation of a mixture of cyclohexane/2-butanol system.The optimal operating conditions of the processes are obtained after optimizing with NSGA-Ⅱ algorithm when total annual cost(TAC)and the entropy production of process are set as objectives.The optimal results show that the optimal PHI-PSD process has lower TAC by 28.7% and the lower entropy production by 39.5% than the optimal SA-PSD process while the ED process with NMP as entrainer has lower TAC by 50.9% and the lower entropy production by 56.1% than the optimal SA-PSD process.The optimal results show that the ED process with NMP as entrainer has the best economic and thermodynamic efficiency among the four proposed processes in this paper.
基金financial support provided by the National Natural Science Foundation of China (22178030, 21878025, and 22078026)。
文摘The conventional distillation is hard to accomplish the separation of acetonitrile/ethyl acetate/n-hexane mixture. Herein, a heterogeneous azeotropic distillation(HAD) without adding entrainer is proposed to separate ternary mixture. The proposed scheme is optimized via the simulated annealing algorithm and minimum total annual cost(TAC) is used as objective functions. To minimize energy consumption,heat pump is added on the basis of optimal heterogeneous azeotropic distillation and heat integration technology is used to further improve the energy recovery. The TAC, gas emission, energy consumption and exergy destruction are used to discuss the economy and environmental protection of processes.Among all the processes, the heat pump with higher preheating temperature(HPT) assisted HAD process by combining with heat integration(HAD-HPT-HI) has best performances on economic, environment,energy and exergy. Compared with conventional HAD process, the HAD-HPT-HI achieves the reductions of 52.17%, 68.86%, 65.87% and 65.46% on TAC, total energy consumption, gas emissions and exergy destruction, respectively.
基金supported by the National Natural Science Foundation of China(22078166)。
文摘A large amount of waste liquids containing methanol/acetone/water mixtures are produced in the synthesis of methyl methacrylate(MMA).Under the advocacy of green chemical industry,it is urgent to develop an efficient,economic and energy-saving mixture separation process.Through thermodynamic azeotropic behavior and pressure sensitivity analysis,pressure-swing distillation was determined and the optimal separation pressure of each column in the process was obtained.Due to the composition of waste liquids produced were quite different in MMA production,the pressure-swing distillation separation process was designed to fully achieve the accurate waste liquids treatment.Taking the total annual cost(TAC)as the target,the sequential iteration method was used to optimize the process,and the impact of composition on economy was compared.In order to further realize the energy-saving of the separation process,the pervaporation membrane module was introduced to pretreat the waste liquid in the pressure-swing distillation.The results showed that the TAC of the coupling process was 46% higher than that of the pressure-swing distillation process,and the thermodynamic efficiency was 30% higher.This study provides waste liquid treatment technology for enterprises and analyzes its economic and energy efficiency,which has reference significance for the development of coupled separation technology.
基金supported by the National Key Research and Development Program of China (2020YFB1807700)the National Natural Science Foundation of China (NSFC)under Grant No.62071356the Chongqing Key Laboratory of Mobile Communications Technology under Grant cqupt-mct202202。
文摘In this paper,to deal with the heterogeneity in federated learning(FL)systems,a knowledge distillation(KD)driven training framework for FL is proposed,where each user can select its neural network model on demand and distill knowledge from a big teacher model using its own private dataset.To overcome the challenge of train the big teacher model in resource limited user devices,the digital twin(DT)is exploit in the way that the teacher model can be trained at DT located in the server with enough computing resources.Then,during model distillation,each user can update the parameters of its model at either the physical entity or the digital agent.The joint problem of model selection and training offloading and resource allocation for users is formulated as a mixed integer programming(MIP)problem.To solve the problem,Q-learning and optimization are jointly used,where Q-learning selects models for users and determines whether to train locally or on the server,and optimization is used to allocate resources for users based on the output of Q-learning.Simulation results show the proposed DT-assisted KD framework and joint optimization method can significantly improve the average accuracy of users while reducing the total delay.
基金supported by the National Key R&D Program of China(2019YFB2103202).
文摘With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant trend.The existing fault diagnosismethods have the characteristics of heavy computing and storage load,and most of them have computational redundancy,which is not suitable for deployment on edge devices with limited resources and capabilities.This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation.First,we offer a clustering-based self-knowledge distillation approach(Cluster KD),which takes the mean value of the sample diagnosis results,clusters them,and takes the clustering results as the terms of the loss function.It utilizes the correlations between faults of the same type to improve the accuracy of the teacher model,especially for fault categories with high similarity.Then,the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweightmodel for edge-side deployment.We propose a two-stage edge-side fault diagnosismethod(TSM)that separates fault detection and fault diagnosis into different stages:in the first stage,a fault detection model based on a denoising auto-encoder(DAE)is adopted to achieve fast fault responses;in the second stage,a diverse convolutionmodel with variance weighting(DCMVW)is used to diagnose faults in detail,extracting features frommicro andmacro perspectives.Through comparison experiments conducted on two fault datasets,it is proven that the proposed method has high accuracy,low delays,and small computation,which is suitable for intelligent edge-side fault diagnosis.In addition,experiments show that our approach has a smooth training process and good balance.
基金support provided by National Natural Science Foundation of China(21978243).
文摘The catalytic packing is the core component of the catalytic distillation,and how the catalyst exists in the packing has significant influence on the process.To investigate the effect of catalyst packings on the catalytic distillation process,the classical ethyl acetate reactive distillation system was utilized,and a supported catalytic packing(SCP)was prepared in comparison with the conventional tea-bag catalytic packing(TBP).Laboratory scale experiments showed that the ethyl acetate conversion of the SCP was superior to the TBP at a low catalyst loading.The effects of reaction kinetics,mass transfer performance and actual catalytic efficiency of the packings on this process were regarded as reasons and studied by combining the experiments and numerical simulation.Results suggested that the relatively immediate“in-situ separation”caused by the rapid reaction kinetics and better mass transfer performance of SCP may be a main reason for the difference of the conversion.
文摘Atmospheric distillation is the first step in separating crude oil into by-products. It uses the different boiling temperatures of the components of crude oil to separate them. But crude oil contains a large quantity of acids and corrosive gases, including sulfur compounds, naphthenic acids, carbon dioxide, oxygen, etc. However, the temperature has an important influence on the aggressiveness of the corrosion factors in the atmospheric distillation column. This paper aims to investigate the role of temperature on corrosive products in the atmospheric distillation column. The results of the developed model show that the temperature increases the corrosion rate in the atmospheric distillation column but above a certain temperature value (about 600 K), it decreases. This illustrates the dual role played by temperature in the study of corrosion within the atmospheric distillation column.
基金supported by the Research and application of Power Business Data Security and Trusted Collaborative Sharing Technology Based on Blockchain and Multi-Party Security Computing(J2022057).
文摘Load forecasting is a crucial aspect of intelligent Virtual Power Plant(VPP)management and ameans of balancing the relationship between distributed power grids and traditional power grids.However,due to the continuous emergence of power consumption peaks,the power supply quality of the power grid cannot be guaranteed.Therefore,an intelligent calculation method is required to effectively predict the load,enabling better power grid dispatching and ensuring the stable operation of the power grid.This paper proposes a decentralized heterogeneous federated distillation learning algorithm(DHFDL)to promote trusted federated learning(FL)between different federates in the blockchain.The algorithm comprises two stages:common knowledge accumulation and personalized training.In the first stage,each federate on the blockchain is treated as ameta-distribution.After aggregating the knowledge of each federate circularly,the model is uploaded to the blockchain.In the second stage,other federates on the blockchain download the trained model for personalized training,both of which are based on knowledge distillation.Experimental results demonstrate that the DHFDL algorithmproposed in this paper can resist a higher proportion of malicious code compared to FedAvg and a Blockchain-based Federated Learning framework with Committee consensus(BFLC).Additionally,by combining asynchronous consensus with the FL model training process,the DHFDL training time is the shortest,and the training efficiency of decentralized FL is improved.
基金supported by the National Research Councile of Science and Technology funded by the Korean Ministry of Industry in Korea(Project Nos.:1711173260,22-3803)the Korea Evaluation Institute of Industrial Technology funded by the Korean Ministry of Industry in Korea(Project Nos.:1415179713,20011157).
文摘In this study,a novel Mg production process for producing high-purity Mg metal from dolomite was developed.When the electrolysis of calcined dolomite was conducted using Cu cathode and C anode in MgF_(2)–LiF molten salt at 1083–1173 K by applying an average current of 1.42–1.46 A for 9.50–21.0 h,the current efficiency of 66.4–88.6%was obtained.The produced Mg alloys consisted of MgCu_(2)and Cu(Mg)or MgCu_(2)and CuMg_(2)phases,depending on the Mg concentration in the Mg alloy.When the electrolysis of calcined dolomite was conducted in MgF_(2)–LiF–CaF_(2)molten salt at 1083 K,the current efficiency was 40.9–71.4%,owing to undesired reactions such as electroreduction of Ca^(2+)or/and CO_(3)^(2−)ions.Meanwhile,the current efficiency increased from 40.9%to 63.2%by utilizing a Pt anode,because the occurrence of CO_(3)^(2−)ions in the molten salt was prevented.After vacuum distillation of the obtained Mg alloys at 1300 K for 10 h,Mg metal with a purity of 99.9996–99.9998%was produced.Therefore,the feasibility of this novel process for the production of high-purity Mg metal from dolomite was demonstrated.
基金supported by the National Natural Science Foundation of China(22125802 and 22078010)Beijing Natural Science Foundation(2222017)Big Science Project from BUCT(XK180301).
文摘Nervonic acid is the world’s first and only potent substance that can repair damaged nerve fibers and promote nerve cell regeneration with high nutritional value.The wide variety of fatty acids in plant oils and fats with similar structures makes the large-scale separation and purification of high-purity nervonic acid very difficult.A new combined process of molecular distillation,urea inclusion and solvent crystallization was established to prepare high-purity nervonic acid with the mixed fatty acids obtained after saponification and acidification of Acer truncatum Bunge oil as raw materials.First,according to the difference in the mean free path of fatty acids,molecular distillation was used to separate and remove C16 saturated fatty acid of palmitic acid and four C18-C20 fatty acids of stearic,oleic,linoleic,and linolenic acids.The content of C16-C20 fatty acids decreased from 72.92% to 19.22% after two-stage molecular distillation processes,in which the contents of saturated fatty acid of palmitic acid decreased to about 0.5%.Then,according to the difference in carbon chain length and saturation of fatty acid,the contents of C22-C24 saturated fatty acids of tetracosanoic and docosanoic acids decreased to 0.21% and 0.07% by urea inclusion with urea/free fatty acid preparation by saponification(SPOMFs)ratio as 0.6.In addition,all saturated fatty acids were basically separated.Finally,according to the difference in the solubility of fatty acids in solvents,the C18-C20 unsaturated fatty acids of oleic,linoleic,and linolenic acids and C22 unsaturated fatty acid of erucic acid were removed by solvent crystallization.The content of C18-C20 unsaturated fatty acids decreased to less than 5% with pentanol as the solvent after the first stage solvent crystallization.The content of erucic acid decreased to 3.47% with anhydrous ethanol as the solvent after the second to fifth stage solvent crystallization.The combined process of molecular distillation,urea inclusion and low temperature crystallization innovatively adopted an efficient,simple and easy-toindustrial solvent crystallization method to separate erucic and nervonic acids,obtaining nervonic acid with purity of 96.53% and final yield of 47.99%.
基金The financial support from National Natural Science Foundation of China (21878011)。
文摘For dividing-wall distillation columns(DWDCs) separating a heavy-component dominated and wide boiling-point ternary(HCDWBT) mixture, a significant amount of excessive heat exists inevitably in stripping the heavy-component from the intermediate-component and it can be employed to initiate the development of vapor recompression heat pump(VRHP) assisted DWDC(VRHP-DWDC). Despite dividing wall may locate in the top, middle, and bottom, the optimum VRHP-DWDC is found to involve uniformlytwo VRHP circles. While the first one serves to compress and transform the excessive heat resulted from the separation of the heavy-component from the intermediate-component, the second one to compress and transform the overhead vapor stream of the light-component pre-heated sequentially with the condensate from the first one and the bottom product stream of the heavy-component, both releasing the temperature-elevated latent heat to the pre-fractionator's or common stripping section. The processing of two HCDWBT mixtures of benzene/toluene/o-xylene and n-pentane/n-hexane/n-heptane are selected to assess the derived optimum topological configurations of the VRHP-DWDC and their optimality is confirmed through detailed comparisons with the DWDC and two VRHP-DWDCs involving only one VRHP circle. The proposed strategy helps to tap the full potential of the VRHP-DWDC with considerably alleviated complication in process development.
文摘Monte Carlo simulation of gamma photon transport and interaction with the distillation column and its contents was performed in order to predict the effects of gamma photons when they interact with matter. The results of the interaction and transport of gamma photons are presented as energy deposition on the distillation column and its contents. Energy attenuation was more pronounced on the column walls and trays as compared to the region between the trays, where there is mostly vapour space. Gamma column scanning was then used to verify the Monte Carlo simulation results by scanning and investigating the integrity of two laboratory prototype distillation and industrial distillation columns. One of the prototype distillation columns was 1 m tall with four trays and the other one was 1.8 m tall consisting of six trays and a packed bed. Commonly encountered distillation column malfunctions such as collapsed tray, weeping, flooding and foaming were simulated in the two prototype distillation columns and scanned. The industrial distillation column was a 26 m tall benzole prefractionator column, consisting of 60 single pass trays and a diameter of 0.8 m. A 10 mCi <sup>60</sup>Co gamma radiation source and NaI(Tl) scintillation detector were used to scan the distillation columns. The results from the two prototypes showed that all the simulated malfunctions were clearly detected except for foaming. The results from industrial distillation column showed that all the trays were in their correct position although tray number 32 could be partially damaged and just below tray 41, the scan revealed that there was a loss of column wall thickness. The obtained density profile for the industrial distillation column showed some small variations from the expected density profile and this was attributed to external features on the distillation column and wind bursts that shifted the source and detector from the chosen scan line orientation.
文摘Monte Carlo simulation of gamma photon transport and interaction with the distillation column and its contents was performed in order to predict the effects of gamma photons when they interact with matter. The results of the interaction and transport of gamma photons are presented as energy deposition on the distillation column and its contents. Energy attenuation was more pronounced on the column walls and trays as compared to the region between the trays, where there is mostly vapour space. Gamma column scanning was then used to verify the Monte Carlo simulation results by scanning and investigating the integrity of two laboratory prototype distillation and industrial distillation columns. One of the prototype distillation columns was 1 m tall with four trays and the other one was 1.8 m tall consisting of six trays and a packed bed. Commonly encountered distillation column malfunctions such as collapsed tray, weeping, flooding and foaming were simulated in the two prototype distillation columns and scanned. The industrial distillation column was a 26 m tall benzole prefractionator column, consisting of 60 single pass trays and a diameter of 0.8 m. A 10 mCi <sup>60</sup>Co gamma radiation source and NaI(Tl) scintillation detector were used to scan the distillation columns. The results from the two prototypes showed that all the simulated malfunctions were clearly detected except for foaming. The results from industrial distillation column showed that all the trays were in their correct position although tray number 32 could be partially damaged and just below tray 41, the scan revealed that there was a loss of column wall thickness. The obtained density profile for the industrial distillation column showed some small variations from the expected density profile and this was attributed to external features on the distillation column and wind bursts that shifted the source and detector from the chosen scan line orientation.
文摘Recently, deep convolutional neural networks (DCNNs) have achieved remarkable results in image classification tasks. Despite convolutional networks’ great successes, their training process relies on a large amount of data prepared in advance, which is often challenging in real-world applications, such as streaming data and concept drift. For this reason, incremental learning (continual learning) has attracted increasing attention from scholars. However, incremental learning is associated with the challenge of catastrophic forgetting: the performance on previous tasks drastically degrades after learning a new task. In this paper, we propose a new strategy to alleviate catastrophic forgetting when neural networks are trained in continual domains. Specifically, two components are applied: data translation based on transfer learning and knowledge distillation. The former translates a portion of new data to reconstruct the partial data distribution of the old domain. The latter uses an old model as a teacher to guide a new model. The experimental results on three datasets have shown that our work can effectively alleviate catastrophic forgetting by a combination of the two methods aforementioned.