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.

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.

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.

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.

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.

Replacement of Forage Fiber Sources with Dried Distillers Grains with Solubles and Corn Germ Meal in Holstein Calf Diets

This study was carried out to determine the effect of replacement of forage fiber sources from alfalfa and Leymus chinensis with nonforage fiber sources（NFFS） from dried distillers grains with solubles and corn germ meal on calves growth, rumen development and blood parameters. 48 female and 12 male calves（（110.55±15.36） kg of body weight and 12 wk of age） were assigned randomly to four dietary treatments（15 calves/treatment） in a completely randomized design. Experimental diets were： 0% NFFS（control）, 9% NFFS（group 1）, 18% NFFS（group 2）, 27% NFFS（group 3）, and contained equivalent neutral detergent fiber and total digestible nutrients, respectively. The dry matter intake was similar among diets, averaging 3.33 kg d-1, and no differences were detected for body weight, withers height, body length and heart girth. In addition, the development of rumen, reticulum, omasum and abomasum also were similar among diets. Dry matter, crude protein, and neutral detergent fiber digestibilities increased with the increasing levels of NFFS in the experimental diets, but had no significant effect. Blood urea nitrogen, total protein and glubulin were not affected by the dietary treatment, but group 3 resulted in the highest（P〈0.05） concentrations of glucose and the lowest（P〈0.05） concentrations of triglycerides and albumin. In conclusion, dried distillers grains with solubles（DDGS） and corn germ meal（CGM） were available and alternative fiber sources for Holstein calf diets.

Amino acid digestibility of heat damaged distillers dried grains with solubles fed to pigs

The primary objective of this experiment was to determine the effects of heat treatment on the standardized ileal digestibility （SID） of amino acids （AA） in corn distillers dried grains with solubles （DDGS） fed to growing pigs. The second objective was to develop regression equations that may be used to predict the concentration of SID AA in corn DDGS. A source of corn DDGS was divided into 4 batches that were either not autoclaved or autoclaved at 130℃ for 10, 20, or 30 min. Four diets containing DDGS from each of the 4 batches were formulated with DDGS being the only source of AA and CP in the diets. A N-free diet also was formulated and used to determine the basal endogenous losses of CP and AA. Ten growing pigs （initial BW： 53.5 ＋ 3.9 kg） were surgically equipped with a T-cannula in the distal ileum and allotted to a replicated 5 x4 Youden square design with 5 diets and 4 periods in each square. The SID of CP decreased linearly （P〈 0.05） from 77.9% in non-autoclaved DDGS to 72.1, 66.1, and 68.5% in the DDGS samples that were autoclaved for 10, 20, or 30 min, respectively. The SID of lysine was quadratically reduced （P〈 0.05） from 66.8% in the non-autoclaved DDGS to 54.9, 55.3, and 51.9% in the DDGS autoclaved for 10, 20, or 30 min, respectively. The concentrations of SID Arginine, Histidine, Leucine, Lysine, Methionine, Phenylalanine, or Threonine may be best predicted by equations that include the concentration of acid detergent insoluble N in the model （r2 = 0.76, 0.68, 0.67, 0.84, 0.76, 0.73, or 0.54, respectively）. The concentrations of SID Isoleucine and Valine were predicted （r2 = 0.58 and 0.54, respectively） by the Lysine：CP ratio, whereas the concentration of SID Tryptophan was predicted （r2 = 0.70） by the analyzed concentration of Tryptophan in DDGS. In conclusion, the SID of AA is decreased as a result of heat damage and the concentration of SID AA in heat-damaged DDGS may be predicted by regression equations developed in this experiment.

Use of a post-production fractionation process improves the nutritional value of wheat distillers grains with solubles for young broiler chicks

Background： Post-production fractionation of wheat distillers grains with solubles（DDGS） increases their crude protein content and reduces their fiber content.This experiment was conducted to determine the effects of fractionation of wheat DDGS on apparent total tract digestibility（ATTD） and performance when fed to broiler chicks（0–21 d）.Methods： A total of 150,day-old,male broiler chicks（Ross-308 line;Lilydale Hatchery,Wynyard,Saskatchewan） weighing an average of 49.6 ± 0.8 g were assigned to one of five dietary treatments in a completely randomized design.The control diet was based on wheat and soybean meal and contained 20% regular wheat DDGS.The experimental diets contained 5,10,15 or 20% fractionated wheat DDGS added at the expense of regular wheat DDGS.Results： The ATTD of dry matter and gross energy were linearly increased（P 0.01） as the level of fractionated wheat DDGS in the diet increased.Nitrogen retention was unaffected by level of fractionated wheat DDGS（P 0.05）.Weight gain increased linearly（P = 0.05） as the level of fractionated wheat DDGS in the diet increased.Feed intake,feed conversion and mortality were unaffected by level of fractionated wheat DDGS in the diet（P 0.05）.Conclusions： Post-production fractionation of wheat DDGS improves their nutritional value by lowering their fiber content and increasing their content of crude protein and energy.These changes in chemical composition supported increased weight gain of broilers fed wheat DDGS.

Effects of dietary replacement of soybean meal with dried distillers grains with solubles on the microbiota occupying different ecological niches in the rumen of growing Hu lambs

Background:Diet has a profound impact on the rumen microbiota,and the impact can vary among the different rumen ecological niches(REN).This study investigated the effects of dietary replacement of soybean meal(SBM)with dried distillers grains with solubles(DDGS)on the rumen microbiota occupying different REN of growing Hu lambs.After a 9-week feeding trial,6 lambs from each dietary treatment(SBM vs.DDGS-based diets)were slaughtered for sample collection.The microbiota of the rumen solid,liquid,and epithelium fractions was examined using amplicon sequencing analysis of bacterial 16S rRNA gene,functional prediction,and qPCR.Results:No interaction of dietary protein source(PS)and REN were detected for virtually all the measurements made in this study.The DDGS substitution resulted in very limited influence on bacterial community structure.However,the metabolic pathways predicted from 16S rRNA gene sequences varied greatly between SBM-and DDGS-based diets.The populations of rumen total bacteria,fungi,sulfate-reducing bacteria(SRB),and methanogens were not influenced by DDGS substitution,but the population of protozoa was reduced.The bacterial communities in rumen solid(RS)and liquid(RL)were similar in taxonomic composition but were different in relative abundance of some taxa.In contrast,the bacterial composition and relative abundance of rumen epithelium(RE)were greatly distinct from those of the RS and the RL.In alignment with the bacterial relative abundance,the metabolic pathways predicted from 16S rRNA genes also varied greatly among the different REN.The populations of total bacteria,protozoa,and methanogens attached to the RE were smaller than those in the RS and RL,and the fungal population on the rumen epitheliumwas smaller than that in the RS but similar to that in the RL.On the contrary,the SRB population on the RE was greater than that in the RS and RL.Conclusions:Substitution of SBM with DDGS had greater impact to the protozoa than to the other microbes,and the microbial community structure and functions at different REN are distinct and niche-adapted.

Amino acid digestibility in low-fat distillers dried grains with solubles fed to growing pigs

The objective of this experiment was to determine the standardized ileal digestibility（SID） of amino acids（AA） in 3sources of distillers dried grains with solubles（DDGS） with different concentrations of fat.Twelve growing barrows（initial body weight：76.1 ± 6.2 kg） were randomly allotted to a replicated 6×4 Youden square design with 6 diets and 4 periods.The fat content of the 3 sources of DDGS were 11.5,7.5,and 6.9%respectively.Diets contained 60%DDGS and fat concentration of the diets were 7.5,52,and 5.2%,respectively.Two additional diets containing the 2sources of DDGS with 75 and 6.9%fat were also formulated,and corn oil was added to these diets to increase the concentration of fat in the diets to levels that were calculated to be similar to the diet containing conventional DDGS with 11.5%fat.A N-free diet was also formulated to calculate endogenous losses of crude protein（CP） and AA from the pigs.Pigs were fed experimental diets during four 7-d periods.The first 5 d of each period were an adaptation period and ileal digesta were collected on d 6 and 7 of each period.The apparent ileal digestibililty（AID） and SID of CP and all indispensable AA,except AID Pro and SID of Trp,were greater（P 〈 0.01） in conventional DDGS than in the 2 sources of DDGS with reduced fat.Adding oil to the diets containing the 2 sources of DDGS with reduced fat did not consistently increase SID of AA.In conclusion,conventional DDGS has greater SID values for most AA compared with DDGS that contains less fat and inclusion of additional oil to diets containing low-fat DDGS does not increase AID or SID of AA.The lower AA digestibility in low-fat DDGS could not be overcome by the inclusion of additional fat to the diets.

Effect of Graded Levels of Wheat Distillers Grains with Solubles on Nutrient Digestibility,Performance and Carcass Traits of Growing-Finishing Pigs Fed Diets Based on Wheat and Field Peas

This trial was conducted to determine the effects of graded levels of wheat distillers dried grains with solubles （DDGS） on nutrient digestibility, performance and carcass characteristics of pigs （21.8 to 112.3 kg） fed diets based on wheat and field peas. Forty crossbred pigs were assigned on the basis of sex and weight to one of five dietary treatments in a 5 x 2 （treatment x sex） factorial arrangement. The experimental diets were based on wheat and field peas and were formulated to contain 0, 4.86%, 9.71%, 14.57% or 19.42% wheat DDGS during the growing period and 0, 4.01%, 8.05%, 12.10% and 16.14% wheat DDGS during the finishing period. At the highest levels, ~vheat DDGS supplied 100% of the supplementary protein. The digestibility coefficient for gross energy showed a tendency towards a linear decline （P 〈 0.08 ） as the level of wheat DDGS in the diet increased while digestibility coefficients for dry matter and crude protein were unaffected （P 〉 0. 05 ） by inclusion level of wheat DDGS. During the growing period （21.8 to 60.8 kg）, weight gain of pigs fed wheat DDGS declined （P〈0.01） linearly. The impairment in growth appeared to be associated with a decrease in feed in- take which also declined linearly （P 〈 0.01 ） with increasing inclusion level of wheat DDGS. Feed conversion was unaffected by level of wheat DDGS. During the finishing period （60.8 to 112.3 kg）, and over the entire experimental period （21.8 to 112.3 kg ）, weight gain and feed conversion declined linearly as the dietary inclusion level of wheat DDGS increased. Carcass traits were generally unaffected by the inclusion of wheat DDGS with the exception of a trend towards a linear （P = 0.10） reduction in lean yield and a linear increase （ P = 0.09） in loin fat as the level of wheat DDGS in the diet increased. Overall, these results suggest that wheat DDGS are inferior to field peas as a supplementary protein source for use in swine rations.