Effect of Different Raw Material Processing Methods on the Quality of Apple Distilled Spirits

In this study,the effect of yeast strains(X16 and RMS2),fruit seed,and pectinase on the quality of apple distilled spirits were investigated with apple as material.The results showed that pectinase shortened the fermentation period.The strain X16,fruit seed remaining,and pectinase addition groups had higher yields of crude distilled spirits than the strain RMS2,fruit seed removal,and without pectinase groups,respectively.Regarding the first-grade distilled spirits quality,the X16 group had higher content of total acids,total esters,higher alcohols,and alcohol content than the RMS2 group;the group with fruit seeds had higher total acids but lower alcohol content and total esters than the group with fruit seed removal;the group with pectinase addition had higher total acids and alcohol content than the group without pectinase addition.Regarding the second-grade distilled spirits quality,the X16 group had higher total acids,total esters,and alcohol content than the RMS2 group;the group without fruit seeds had higher alcohol content than the group with fruit seeds;the group with pectinase addition showcased higher total acids but lower alcohol content than the group without pectinase addition.In summary,yeast strains and pectinase affected the quality of apple distilled spirits,and strain X16 was more suitable for brewing apple distilled spirits.Pectinase affected the quality of apple distilled spirits by affecting fermentation rate and temperature.

Anticlastogenic Effect of Redistilled Cow's Urine Distillate in Human Peripheral Lymphocytes Challenged With Manganese Dioxide and Hexavalent Chromium

Objective To study the anticlastogenic effect of redistilled cow＇s urine distillate （RCUD） in human peripheral lymphocytes （HLC） challenged with manganese dioxide and hexavalent chromium. Methods The anticlastogenic activity of redistilled cow＇s urine distillate was studied in human polymorphonuclear leukocytes （HPNLs） and human peripheral lymphocytes in vitro challenged with manganese dioxide and hexavalent chromium as established genotoxicants and clastogens which could cause induction of DNA strand break, chromosomal aberration and micronucleus. Three different levels of RCUD： 1 μL/mL, 50 μL/mL and 100 μL/mL, were used in the study. Results Manganese dioxide and hexavalent chromium caused statistically significant DNA strand break, chromosomal aberration and micronucleus formation, which could be protected by redisfilled cow＇s urine distillate. Conclusion The redistilled cow＇s urine distillate posseses strong anfigenotoxic and antielastogenic properties against HPNLs and HLC treated with Cr^＋6 and MnO2. This property is mainly due to the antioxidants present in RCUD.

Effective Sodium Metabisulfite (Na₂S₂O₅), HCl, Sulfur and Distilled Water for the Removal of Pb, Zn and Cr Contaminated Soil in the Columns Method

This paper presents an evaluation of different dose of Sodium Metabisulfite (0.01 M Na2S2O5), (Na2S2O5) + (0.1 HCl), and Distilled water for the removal of soil contaminated with Pb, Zn and Cr by the column mode. The field soil contained concentrations of Pb (307.31 mg⋅kg−1), Zn (207.77 mg⋅kg−1) and Cr (447.50 mg⋅kg−1). Both (0.01 M Na2S2O5), (Na2S2O5) + (0.1 HCl), and Distilled water were found to be effective on removing (Na2S2O5) Pb, Cr and Zn respectively. (Na2S2O5) + (0.1 HCl) Cr, Zn and Pb respectively. Sulfur Pb, Cr and Zn respectively. The removal rate of Pb, Zn, and Cr varied from 10.35% - 26%, 3.4% - 21.60% and 4.97% - 23.88% for (0.01 M Na2S2O5 respectively. The removal rate of Pb, Zn, and Cr varied from 16.13% - 20.07%, 8.20% - 23.48%, 5.42% - 28.93% for (0.01 M Na2S2O5 + 0.1 M HCl) respectively. The removal rate of Pb, Zn, and Cr varied from 10.20% - 25.5%, 9.55% - 25.13% and 6.04% - 25.54% for (S) respectively.

Research on Energy Saving Optimization of MVR Distilled Water Machine

Compared with the multi-effect distilled water machine,the mechanical vapor recompression(MVR)distilled water machine has many advantages,and has been a trend to replace the traditional distilled water machine.Based on the conservation equations of mass and energy,this paper designs an MVR distilled water production process and establishes the corresponding thermodynamic model.Effects of the produced water temperature,heat exchange temperature difference,compressor adiabatic efficiency,and pipeline loss to the produced water energy consumption,energy saving rate and coefficient of performance(COP)were studied.Research shows that the MVR distilled water machine is more suitable for occasions with low water production temperature.Appropriate heat exchangers and compressors should be selected to improve comprehensive performance through heat exchange temperature differences and improving adiabatic efficiency.

Physicochemical and Chromatographic Profiles of Distilled Sugarcane Spirits Produced in Mozambique

Knowledge of the chemical composition of Mozambican spirits is extremely important because the studies of potentially toxic compounds have been a determinant factor in quality control. The objectives of this work were to analyze and physicochemically characterize distilled sugarcane spirits and other raw materials from Mozambique on the basis of the standards established by the Brazilian Ministry of Agriculture, Livestock and Food Supply (MAPA). The samples came from five provinces of southern and central Mozambique, provided by the owners of the stills in the localities of Manhica, Xinavane, Chókwe, Massinga, Mafambisse, Beira and Chimoio. The four samples from Brazil were randomly collected from different regions of Minas Gerais. The parameters analyzed were ethanol, methanol, higher alcohols, aldehydes, volatile acidity, furfural, esters and copper concentrations. The Mozambican spirits produced from different raw materials, compared to Brazilian spirits, contained very variable concentrations of the components analyzed. Mozambican spirits are of much lower quality than Brazilian beverages and are not suitable for consumption.

Distilled Relic

Anyone who has been in China for any length of time will no doubt have experienced the rituals of the banquet. Most notably the endless display of friendship and cordiality shown by caring hosts, whase sole focus it seems is getting as much alcohol down your throat as is humanly possible in the space of 60 minutes.

Miscibility of light oil and flue gas under thermal action

The miscibility of flue gas and different types of light oils is investigated through slender-tube miscible displacement experiment at high temperature and high pressure.Under the conditions of high temperature and high pressure,the miscible displacement of flue gas and light oil is possible.At the same temperature,there is a linear relationship between oil displacement efficiency and pressure.At the same pressure,the oil displacement efficiency increases gently and then rapidly to more than 90% to achieve miscible displacement with the increase of temperature.The rapid increase of oil displacement efficiency is closely related to the process that the light components of oil transit in phase state due to distillation with the rise of temperature.Moreover,at the same pressure,the lighter the oil,the lower the minimum miscibility temperature between flue gas and oil,which allows easier miscibility and ultimately better performance of thermal miscible flooding by air injection.The miscibility between flue gas and light oil at high temperature and high pressure is more typically characterized by phase transition at high temperature in supercritical state,and it is different from the contact extraction miscibility of CO_(2) under conventional high pressure conditions.

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.

Cascade equilibrium stage relaxation method by introducing equilibrium efficiency parameter

Optimizing multistage processes,such as distillation or absorption,is a complex mixed-integer nonlinear programming(MINLP)problem.Relaxing integer into continuous variables and solving the easier nonlinear programming(NLP)problem is an optimization idea for the multistage process.In this article,we propose a relaxation method based on the efficiency parameter.When the efficiency parameter is 1or 0,the proposed model is equivalent to the complete existence or inexistence of the equilibrium stage.And non-integer efficiency represents partial existence.A multi-component absorption case shows a natural penalty for non-integer efficiency,which can assist the efficiency parameter converging to 0 or 1.However,its penalty is weaker than the existing relaxation models,such as the bypass efficiency model.In a simple distillation case,we show that this property can weaken the nonconvexity of the optimization problem and increase the probability of obtaining better optimization results.

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.

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.

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.

Process synthesis for the separation of coal-to-ethanol products

The coal-to-ethanol process,as the clean coal utilization,faces challenges from the energy-intensive distillation that separates multi-component effluents for pure ethanol.Referring to at least eight columns,the synthesis of the ethanol distillation system is impracticable for exhaustive comparison and difficult for conventional superstructure-based optimization as rigorous models are used.This work adopts a superstructure-based framework,which combines the strategy that adaptively selects branches of the state-equipment network and the parallel stochastic algorithm for process synthesis.High-performance computing significantly reduces time consumption,and the adaptive strategy substantially lowers the complexity of the superstructure model.Moreover,parallel computing,elite search,population redistribution,and retention strategies for irrelevant parameters are used to improve the optimization efficiency further.The optimization terminates after 3000 generations,providing a flowsheet solution that applies two non-sharp splitting options in its distillation sequence.As a result,the 59-dimension superstructure-based optimization was solved efficiently via a differential evolution algorithm,and a high-quality solution with a 28.34%lower total annual cost than the benchmark was obtained.Meanwhile,the solution of the superstructure-based optimization is comparable to that obtained by optimizing a single specific configuration one by one.It indicates that the superstructure-based optimization that combines the adaptive strategy can be a promising approach to handling the process synthesis of large-scale and complex chemical processes.

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.

Learning Epipolar Line Window Attention for Stereo Image Super-Resolution Reconstruction

Transformer-based stereo image super-resolution reconstruction(Stereo SR)methods have significantly improved image quality.However,existing methods have deficiencies in paying attention to detailed features and do not consider the offset of pixels along the epipolar lines in complementary views when integrating stereo information.To address these challenges,this paper introduces a novel epipolar line window attention stereo image super-resolution network(EWASSR).For detail feature restoration,we design a feature extractor based on Transformer and convolutional neural network(CNN),which consists of(shifted)window-based self-attention((S)W-MSA)and feature distillation and enhancement blocks(FDEB).This combination effectively solves the problem of global image perception and local feature attention and captures more discriminative high-frequency features of the image.Furthermore,to address the problem of offset of complementary pixels in stereo images,we propose an epipolar line window attention(EWA)mechanism,which divides windows along the epipolar direction to promote efficient matching of shifted pixels,even in pixel smooth areas.More accurate pixel matching can be achieved using adjacent pixels in the window as a reference.Extensive experiments demonstrate that our EWASSR can reconstruct more realistic detailed features.Comparative quantitative results show that in the experimental results of our EWASSR on the Middlebury and Flickr1024 data sets for 2×SR,compared with the recent network,the Peak signal-to-noise ratio(PSNR)increased by 0.37 dB and 0.34 dB,respectively.

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.

A Real-time Prediction System for Molecular-level Information of Heavy Oil Based on Machine Learning

Acquiring accurate molecular-level information about petroleum is crucial for refining and chemical enterprises to implement the“selection of the optimal processing route”strategy.With the development of data prediction systems represented by machine learning,it has become possible for real-time prediction systems of petroleum fraction molecular information to replace analyses such as gas chromatography and mass spectrometry.However,the biggest difficulty lies in acquiring the data required for training the neural network.To address these issues,this work proposes an innovative method that utilizes the Aspen HYSYS and full two-dimensional gas chromatography-time-of-flight mass spectrometry to establish a comprehensive training database.Subsequently,a deep neural network prediction model is developed for heavy distillate oil to predict its composition in terms of molecular structure.After training,the model accurately predicts the molecular composition of catalytically cracked raw oil in a refinery.The validation and test sets exhibit R2 values of 0.99769 and 0.99807,respectively,and the average relative error of molecular composition prediction for raw materials of the catalytic cracking unit is less than 7%.Finally,the SHAP(SHapley Additive ExPlanation)interpretation method is used to disclose the relationship among different variables by performing global and local weight comparisons and correlation analyses.