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.

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.

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.

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.

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.

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.

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.

A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme

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.

Experimental and Analytical Study of a Single Effect Distillation Using Electrical Evaporator Powered by Solar Energy

The experimental and analytical investigation was conducted on a solar-powered single-effect distillation (SED). The evaporator was designed to be an electrical evaporator as opposed to the steam evaporator that existed previously. Using sun-tracking solar panels, the electrical evaporator in the designed distillation unit was powered by solar energy. Approximately 20 kWh was utilized by the small-scale distillation apparatus. This type of design is mobile, so remote areas and countries with fragile economies can utilize it on a small or large scale. Utilizing the principles of energy and mass conservation, the amount of distillate water and power required for a single unit was determined, at the low salinity (2200 PPM) with fixed boiling point temperature (Tb = 75˚C), the unit performance is approx. 98.4%. The experimental results and those derived from a mathematical model were compared, and both showed strong accord. Using engineering equation solver (EES) software, a computer program was developed for this research scenario.

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.

Thermodynamics of removing impurities from crude lead by vacuum distillation refining

A novel technique was developed to remove impurities from crude lead by vacuum distillation.The thermodynamics on vacuum distillation refining process of crude lead was studied by means of saturated vapor pressure of main components of crude lead,separation coefficients and vapor-liquid equilibrium composition of Pb-i（i stands for an impurity） system at different temperatures.The behaviors of impurities in the vacuum distillation refining process were investigated.The results show that the vacuum distillation should be taken to obtain lead from crude lead,in which Zn,As and partial Sb are volatilized at lower temperature of 923-1023 K.Lead is distilled from the residue containing Cu,Sn,Ag and Bi at higher temperature of 1323-1423 K,but the impurity Bi is also volatilized along with lead and cannot be separated from lead.

Application of molecular interaction volume model in separation of Pb-Sn-Sb ternary alloy by vacuum distillation

Based on the molecular interaction volume model （MIVM）, the activities of components of Pb Sn Sb ternary alloy were predicted. The vapo^liquid phase equilibrium of Pb-Sn-Sb alloy system was calculated using the activity coefficients of Pb Sn-Sb alloy system in the process of vacuum distillation. The calculated results show that the content of Sn in vapor phase increases with the increasing distillation temperature and content of Sn in liquid phase. However, the content of Sn in vapor phase is only 0.45% （mass fraction） while 97% in liquid phase at 1100 ℃, which shows that the separating effect is very well. Experimental investigations on the separation of Pb-Sn-Sb ternary alloy were carried out in the distillation temperature range of 1100-1300 ℃ under vacuum condition. It is found that the Sn content in vapor phase is 0.54% while 97% in liquid phase at 1100 ℃. Finally, the predicted data were compared with the experimental results showing good agreement with each other.

DOWNCOMER MIXING AND ITS EFFECT ON DISTILLATION TRAY EFFICIENCY

In this paper, the measurement of liquid mixing in a downcomer of segmental type of distillation column is presented. The extent of liquid mixing is calculated by means of a mixing pool model. The results indicate that liquid mixing in a downcomer is actually incomplete. It is a significant correction to the assumption of complete downcomer mixing or no downcomer mixing which is generally adopted in many distillation calculations. Besides, the present results are used in a two dimensional eddy diffusion model to calculate the distillation tray efficiency. It is shown that the assumption of complete downcomer mixing is closer to the actual situation than that of no downcomer mixing.

Multi-effect distillation system for seawater desalination driven by tidal energy and low grade energy

A multi-effect distillation technology for seawater desalination driven by tidal energy and low grade energy is presented.In the system,tidal energy is utilized to supply power instead of coventional electric pumps during the operation,resulting in the decrease of dependence on steady electric power supply and a reduction in the running costs.According to the technological principle,a testing unit is designed and built.The effects of the feed seawater temperature and the heat source temperature on the unit performance are tested and analyzed.The experimental results show that the fresh water output is 27 kg/h when the heating water temperature is 65 ℃ and the absolute pressure is 25 kPa.The experimental and theoretical analysis results indicate that the appropriate heating water temperature is a key factor in ensuring the steady operation of the system.

A new combination process of distillation with salt extraction for separating organic solvent-water azeotropes

A novel process which can purify the organic solvents from their azeotropes with water is proposed. In this process,water can be drained off both from bottom and overhead of tower at the same time,and the organic solvent is concentrated in the tower and accumulated in the middle vessel at last. So the progress is time-shortened and energy-saving. The product purity is 99. 8% and the product yield is more than 99.5%. Simulation of liquid-liquid equilibrium (LLE) and the equipment operation data agree well with the experiment.

STUDY ON CONCENTRATING SULFURIC ACID SOLUTION BY VACUUM MEMBRANE DISTILLATION

Concentrating sulfuric acid solution by vacuum membrane distillation with flat PEFE membrane is explored. The effects of sulfuric acid concentration, temperature of the feed, the vacuum degree of the vacuum side on the flux of membrane distillation and the separation efficiency of acid are investigated. The results illustrate that the flux of the membrane distillation increases with the rise of feed temperature and the vacuum degree of the vacuum side, but it decreases with the rise of the sulfuric acid concentration of the feed. The separation efficiency of acid is correlated with the flux of membrane distillation; the separation efficiency of the acid can amount to 100% in the process, when operative conditions are properly controlled. It can also been obtained from the experiment that, compared with other methods of membrane distillation, the vacuum membrane distillation can obtain greater distillation flux.

Extractive distillation: Advances in conceptual design, solvent selection,and separation strategies

Extractive distillation(ED) is one of the most promising approaches for the separation of the azeotropic or closeboiling mixtures in the chemical industry. The purpose of this paper is to provide a broad overview of the recent development of key aspects in the ED process involving conceptual design, solvent selection, and separation strategies. To obtain the minimum entrainer feed flow rate and reflux ratio for the ED process, the conceptual design of azeotropic mixture separation based on a topological analysis via thermodynamic feasibility insights involving residue curve maps, univolatility lines, and unidistribution curves is presented. The method is applicable to arbitrary multicomponent mixtures and allows direct screening of design alternatives. The determination of a suitable solvent is one of the key steps to ensure an effective and economical ED process. Candidate entrainers can be obtained from heuristics or literature studies while computer aided molecular design(CAMD) has superiority in efficiency and reliability. To achieve optimized extractive distillation systems, a brief review of evaluation method for both entrainer design and selection through CAMD is presented. Extractive distillation can be operated either in continuous extractive distillation(CED) or batch extractive distillation(BED), and both modes have been well-studied depending on the advantages in flexibility and low capital costs. To improve the energy efficiency, several configurations and technological alternatives can be used for both CED and BED depending on strategies and main azeotropic feeds. The challenge and chance of the further ED development involving screening the best potential solvents and exploring the energy-intensive separation strategies are discussed aiming at promoting the industrial application of this environmentally friendly separation technique.