Comparative Analysis: Trays versus Packed Columns in Pressure-Swing Distillation for the Separation of Tetrahydrofuran, Water and Ethanol Azeotropic Mixture

This paper delves into the comparative study of tray and packed column pressure swing distillation systems, focusing on the separation of a ternary mixture containing ethanol, tetrahydrofuran (THF), and water. The study particularly emphasizes the production of 99.5 w/w% tetrahydrofuran from the downstream product of 1,4-butanediol synthesis via diethyl maleate. Pro/II simulation software is utilized to explore various system configurations, including sieve trays, valve trays, and packed columns. Material and energy balances are performed to ascertain stream compositions and energy demands. The investigation encompasses the effects of column operating pressure on condenser and reboiler temperatures, as well as the implications of utility streams. A rigorous distillation model is employed to compare valve tray, sieve tray, and random packing (utilizing Norton Super Intalox) column designs by varying the number of trays, reflux ratio, and second distillation column pressure. Heat exchangers are integrated into the model, and their areas and utility flow rates are computed and integrated into the economic assessment. Economic analysis, guided by Net Present Value (NPV) calculations over a 20-year span, drives the selection of the most cost-effective design. Results demonstrate that while all designs are energy-efficient, the packed column system emerges as the most economical choice, offering a comprehensive framework for the separation process. Furthermore, optimal design configurations and operating conditions for both tray and packed column systems are outlined, providing valuable insights for industrial applications.

Economic and entropy production evaluation of extractive distillation and solvent-assisted pressure-swing distillation by multi-objective optimization

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.

Comprehensive analysis on the economy and energy demand of pressure-swing distillation and pervaporation for separating waste liquid containing multiple components

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.

Comparison of continuous homogenous azeotropic and pressure-swing distillation for a minimum azeotropic system ethyl acetate/nhexane separation

Continuous homogenous azeotropic distillation(CHAD) and pressure-swing distillation(PSD) are explored to separate a minimum-boiling azeotropic system of ethyl acetate and n-hexane. The CHAD process with acetone as the entrainer and the PSD process with the pressures of 0.1 MPa and 0.6 MPa in two columns are designed and simulated by Aspen Plus. The operating conditions of the two processes are optimized via a sequential modular approach to obtain the minimum total annual cost(TAC). The computational results show that the partially heat integrated pressure-swing distillation(HIPSD) has reduced in the energy cost and TAC by 40.79% and 35.94%, respectively, than the conventional PSD, and has more greatly reduced the energy cost and TAC by 62.61% and 49.26% respectively compared with the CHAD process. The comparison of CHAD process and partially HIPSD process illustrates that the partially HIPSD has more advantages in averting the product pollution, energy saving, and economy.

Separation process of butanol-butyl acetate-methyl isobutyl ketone system by the analysis to residual curve and the double effect pressure-swing distillation

The separation of ternary mixture of butanol, butyl acetate, and methyl isobutyl ketone(MIBK) was initially analyzed by the residual curve. In this process, MIBK was chosen as the azeotropic agent during the first step of separation. The optimum mass ratio of extra MIBK was 1.6 in the modified feed stream according to the residual curve. Thus on this condition the top product was butanol-MIBK azeotrope while the bottom product was butyl acetate in the preliminary separation of the mixture. Then the butanol and MIBK azeotrope was separated by the double effect pressureswing distillation with the low pressure column performing at 30 kPa and the atmospheric pressure column at 101 kPa. The optimal operating conditions were then obtained by using Aspen Plus to simulate and optimize the process. The results showed that the mass purities of butanol, butyl acetate, and MIBK were all more than 99% and reached the design requirements. Additionally, compared with the traditional distillation with outside heating, the double effect pressure swing distillation saved the reboiler duty by 48.6% and the condenser duty by 44.6%.

Design and control of methyl acetate-methanol separation via heat-integrated pressure-swing distillation

Design and control of pressure-swing distillation(PSD) with different heat integration modes for the separation of methyl acetate/methanol azeotrope are explored using Aspen Plus and Aspen Dynamics. First, an optimum steady-state separation configuration conditions are obtained via taking the total annual cost(TAC) or total reboiler heat duty as the objective functions. The results show that about 27.68% and 25.40% saving in TAC can be achieved by the PSD with full and partial heat integration compared to PSD without heat integration. Second,temperature control tray locations are obtained according to the sensitivity criterion and singular value decomposition(SVD) analysis and the single-end control structure is effective based on the feed composition sensitivity analysis. Finally, the comparison of dynamic controllability is made among various control structures for PSD with partial and full heat integration. It is shown that both control structures of composition/temperature cascade and pressure-compensated temperature have a good dynamic response performance for PSD with heat integration facing feed flowrate and composition disturbances. However, PSD with full heat integration performs the poor controllability despite of a little bit of economy.

Dynamic control analysis of interconnected pressure-swing distillation process with and without heat integration for separating azeotrope

Dynamic controls of pressure-swing distillation with an intermediate connection(PSDIC) process of ethyl acetate and ethanol separation were investigated.The double temperature/composition cascade control structure can perfectly implement effective control when ±20% feed disturbances were introduced.This control structure did not require the control of the flowrate of the side stream.The dynamic controllability of PSDIC with partial heat integration(PHIPSDIC) was also explored.The improved control structure can effectively control ±20% feed disturbances.However,in industrial production,simple controller,sensitive and easy to operate,is the optimal target.To avoid the use of component controllers or complex control structure,the original product purities could be maintained using the basic control structure for the PSDIC process if the product purities in steady state were properly increased,albeit by incurring a slight rise in the total annual cost(TAC).This alternative method without a composition controller combined with the energy-saving PSDIC process provides a simple and effective control scheme in industrial production.

Comparison of Extractive Distillation and Pressure-Swing Distillation for Methanol and Acetonitrile Separation

In the present work,a comparative study of the extractive distillation and pressure swing distillation for methanol-acetonitrile separation is performed for the first time.Different separation alternatives,including the conventional extractive distillation,the extractive distillation with vapor or liquid side-stream,the pressure-swing distillation with or without full heat integration,and the heat-pump assisted pressure-swing distillation are rigorously simulated and optimized based on the minimum total annual cost(TAC)via the sequential iterative strategy.The results show that TAC and CO2 emission of the new extractive distillation with vapor side-stream(Vapor-SED)are similar to those of the extractive distillation with liquid side-stream(Liquid-SED).Furthermore,the Vapor-SED and Liquid-SED can achieve 30.01%and 30.56%reduction in TAC and 23.32%and 23.49%reduction in CO2 emission,respectively,over the most competitive fully heat-integrated PSD configuration.Hence,the extractive distillation with vapor or liquid side-stream appears to be a better option economically and environmentally for the separation of methanol and acetonitrile.

Energy-saving design and optimization of pressure-swing-assisted ternary heterogenous azeotropic distillations

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.

LDAS&ET-AD:Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation

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.

Environmental,economic and exergy analysis of separation of ternary azeotrope by variable pressure extractive distillation based on multi-objective optimization

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.

Improving the accuracy of mechanistic models for dynamic batch distillation enabled by neural network:An industrial plant case

Neural networks are often viewed as pure‘black box’models,lacking interpretability and extrapolation capabilities of pure mechanistic models.This work proposes a new approach that,with the help of neural networks,improves the conformity of the first-principal model to the actual plant.The final result is still a first-principal model rather than a hybrid model,which maintains the advantage of the high interpretability of first-principal model.This work better simulates industrial batch distillation which separates four components:water,ethylene glycol,diethylene glycol,and triethylene glycol.GRU(gated recurrent neural network)and LSTM(long short-term memory)were used to obtain empirical parameters of mechanistic model that are difficult to measure directly.These were used to improve the empirical processes in mechanistic model,thus correcting unreasonable model assumptions and achieving better predictability for batch distillation.The proposed method was verified using a case study from one industrial plant case,and the results show its advancement in improving model predictions and the potential to extend to other similar systems.

Microchannel reactive distillation for the conversion of aqueous ethanol to ethylene

Here we demonstrate the proof-of-concept for microchannel reactive distillation for alcohol-to-jet application:combining ethanol/water separation and ethanol dehydration in one unit operation.Ethanol is first distilled into the vapor phase,converted to ethylene and water,and then the water co-product is condensed to shift the reaction equilibrium.Process intensification is achieved through rapid mass transfer-ethanol stripping from thin wicks using novel microchannel architectures-leading to lower residence time and improved separation efficiency.Energy savings are realized with integration of unit operations.For example,heat of condensing water can offset vaporizing ethanol.Furthermore,the dehydration reaction equilibrium shifts towards completion by immediate removal of the water byproduct upon formation while maintaining aqueous feedstock in the condensed phase.For aqueous ethanol feedstock(40%_w),71% ethanol conversion with 91% selectivity to ethylene was demonstrated at 220℃,600psig,and 0.28 h^(-1) wt hour space velocity.2.7 stages of separation were also demonstrated,under these conditions,using a device length of 8.3 cm.This provides a height equivalent of a theoretical plate(HETP),a measure of separation efficiency,of ^(3).3 cm.By comparison,conventional distillation packing provides an HETP of ^(3)0 cm.Thus,9,1 × reduction in HETP was demonstrated over conventional technology,providing a means for significant energy savings and an example of process intensification.Finally,preliminary process economic analysis indicates that by using microchannel reactive distillation technology,the operating and capital costs for the ethanol separation and dehydration portion of an envisioned alcoholto-jet process could be reduced by at least 35% and 55%,respectively,relative to the incumbent technology,provided future improvements to microchannel reactive distillation design and operability are made.

De-biased knowledge distillation framework based on knowledge infusion and label de-biasing techniques

Knowledge distillation,as a pivotal technique in the field of model compression,has been widely applied across various domains.However,the problem of student model performance being limited due to inherent biases in the teacher model during the distillation process still persists.To address the inherent biases in knowledge distillation,we propose a de-biased knowledge distillation framework tailored for binary classification tasks.For the pre-trained teacher model,biases in the soft labels are mitigated through knowledge infusion and label de-biasing techniques.Based on this,a de-biased distillation loss is introduced,allowing the de-biased labels to replace the soft labels as the fitting target for the student model.This approach enables the student model to learn from the corrected model information,achieving high-performance deployment on lightweight student models.Experiments conducted on multiple real-world datasets demonstrate that deep learning models compressed under the de-biased knowledge distillation framework significantly outperform traditional response-based and feature-based knowledge distillation models across various evaluation metrics,highlighting the effectiveness and superiority of the de-biased knowledge distillation framework in model compression.

Co-pyrolysis of Sewage Sludge with Distillation Residue: Kinetics Analysis via Iso-conversional Methods

This study explored the synergistic interaction of sewage sludge(SS)and distillation residue(DR)during co-pyrolysis for the optimized treatment of sewage sludge in cement kiln systems,utilizing thermogravimetric analysis(TGA)and thermogravimetric analysis with mass spectrometry(TGA-MS).The results reveal the coexisting synergistic and antagonistic effects in the co-pyrolysis of SS/DR.The synergistic effect arises from hydrogen free radicals in SS and catalytic components in ash fractions,while the antagonistic effect is mainly due to the melting of DR on the surface of SS particles during pyrolysis and the reaction of SS ash with alkali metals to form inert substances.SS/DR co-pyrolysis reduces the yielding of coke and gas while increasing tar production.This study will promote the reduction,recycling,and harmless treatment of hazardous solid waste.

Strabismus Detection Based on Uncertainty Estimation and Knowledge Distillation

Strabismus significantly impacts human health as a prevalent ophthalmic condition.Early detection of strabismus is crucial for effective treatment and prognosis.Traditional deep learning models for strabismus detection often fail to estimate prediction certainty precisely.This paper employed a Bayesian deep learning algorithm with knowledge distillation,improving the model's performance and uncertainty estimation ability.Trained on 6807 images from two tertiary hospitals,the model showed significantly higher diagnostic accuracy than traditional deep-learning models.Experimental results revealed that knowledge distillation enhanced the Bayesian model’s performance and uncertainty estimation ability.These findings underscore the combined benefits of using Bayesian deep learning algorithms and knowledge distillation,which improve the reliability and accuracy of strabismus diagnostic predictions.

Application of sparse S transform network with knowledge distillation in seismic attenuation delineation

Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficulty in selecting parameters,and the low computational efficiency.Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis.The sparse S transform network(SSTNet)is first built to map the relationship between synthetic traces and sparse S transform spectra,which can be easily pre-trained by using synthetic traces and training labels.Next,we introduce knowledge distillation(KD)based transfer learning to re-train SSTNet by using a field data set without training labels,which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way,we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels.To test the availability of the suggested KD-SSTNet,we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.

Border Sensitive Knowledge Distillation for Rice Panicle Detection in UAV Images

Research on panicle detection is one of the most important aspects of paddy phenotypic analysis.A phenotyping method that uses unmanned aerial vehicles can be an excellent alternative to field-based methods.Nevertheless,it entails many other challenges,including different illuminations,panicle sizes,shape distortions,partial occlusions,and complex backgrounds.Object detection algorithms are directly affected by these factors.This work proposes a model for detecting panicles called Border Sensitive Knowledge Distillation(BSKD).It is designed to prioritize the preservation of knowledge in border areas through the use of feature distillation.Our feature-based knowledge distillation method allows us to compress the model without sacrificing its effectiveness.An imitation mask is used to distinguish panicle-related foreground features from irrelevant background features.A significant improvement in Unmanned Aerial Vehicle(UAV)images is achieved when students imitate the teacher’s features.On the UAV rice imagery dataset,the proposed BSKD model shows superior performance with 76.3%mAP,88.3%precision,90.1%recall and 92.6%F1 score.

Two-Stage Approach for Targeted Knowledge Transfer in Self-Knowledge Distillation

Knowledge distillation(KD) enhances student network generalization by transferring dark knowledge from a complex teacher network. To optimize computational expenditure and memory utilization, self-knowledge distillation(SKD) extracts dark knowledge from the model itself rather than an external teacher network. However, previous SKD methods performed distillation indiscriminately on full datasets, overlooking the analysis of representative samples. In this work, we present a novel two-stage approach to providing targeted knowledge on specific samples, named two-stage approach self-knowledge distillation(TOAST). We first soften the hard targets using class medoids generated based on logit vectors per class. Then, we iteratively distill the under-trained data with past predictions of half the batch size. The two-stage knowledge is linearly combined, efficiently enhancing model performance. Extensive experiments conducted on five backbone architectures show our method is model-agnostic and achieves the best generalization performance.Besides, TOAST is strongly compatible with existing augmentation-based regularization methods. Our method also obtains a speedup of up to 2.95x compared with a recent state-of-the-art method.

Anomaly Detection Method Using Feature Reconstruction Based Knowledge Distillation

In recent years,anomaly detection has attracted much attention in industrial production.As traditional anomaly detection methods usually rely on direct comparison of samples,they often ignore the intrinsic relationship between samples,resulting in poor accuracy in recognizing anomalous samples.To address this problem,a knowledge distillation anomaly detection method based on feature reconstruction was proposed in this study.Knowledge distillation was performed after inverting the structure of the teacher-student network to avoid the teacher-student network sharing the same inputs and similar structure.Representability was improved by using feature splicing to unify features at different levels,and the merged features were processed and reconstructed using an improved Transformer.The experimental results show that the proposed method achieves better performance on the MVTec dataset,verifying its effectiveness and feasibility in anomaly detection tasks.This study provides a new idea to improve the accuracy and efficiency of anomaly detection.