Design and Prototype Test of 9FQM1000 Branch and Straw Hammer Mill

[Objectives]To better design and test 9FQM1000 branch and straw hammer mill in view of the problems of large output,low utilization rate,traditional incineration and easily polluting environment of the new agricultural economic organization's straw and waste branches.[Methods]This hammer mill adopts dual-channel feeding method.It adopts the basic working principle of disc shredding and hammer crushing.Besides,it makes design of the structure and driving system of the branch and straw hammer mill.9FQM1000 type branch and straw hammer mill can finely crush the branches and straws.Finally,it makes a trial production of 9FQM1000 type branch and straw hammer mill.[Results]The prototype test showed that the combined crushing structure of 9FQM1000 type branch and straw hammer mill is reliable,and the production capacity is 3000 kg/h.[Conclusions]The automatic feeder makes the crushing operation more stable,the labor intensity is reduced,the structure is simple,and it can be moved by traction.It is environmentally friendly and pollution-free.It has the characteristics of high safety,automation and high production efficiency.Also,crushed materials can be used as edible fungus culture medium,animal feed,organic fertilizer,etc.,and can be further compressed into biomass fuel,and the crushed branches can also be returned to the field.

Kinetic model of vibration screening for granular materials based on biological neural network

The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices.In this paper,a biological neurodynamic equation and neural connections were established according to the motion and interaction properties of the material under vibration excitation.The material feeding to the screen and the material passing through apertures were considered as excitatory and inhibitory inputs,respectively,and the generated stable neural activity landscape was used to describe the material distribution on the 2D screen surface.The dynamic process of material vibration screening was simulated using discrete element method(DEM).By comparing the similarity between the material distribution established using biological neural network(BNN)and that obtained using DEM simulation,the optimum coefficients of BNN model under a certain screening parameter were determined,that is,one relationship between the BNN model coefficients and the screening operation parameters was established.Different screening parameters were randomly selected,and the corresponding relationships were established as a database.Then,with straw/grain ratio,aperture diameter,inclination angle,vibration strength in normal and tangential directions as inputs,five independent adaptive neuro-fuzzy inference systems(ANFIS)were established to predict the optimum BNN model coefficients,respectively.The training results indicated that ANFIS models had good stability and accuracy.The flexibility and adaptability of the proposed BNN method was demonstrated by modeling material distribution under complex feeding conditions such as multiple regions and non-uniform rate.

Design and experiment analysis of the small maize harvester with attitude adjustment in the hilly and mountainous areas of China

To solve the problem of small planting plots and large sloping land for mechanized maize harvesting in China hilly and mountainous areas,a small maize harvester with attitude adjustment was designed to realize maize snapping,peeling,straw crushing and attitude adjusting at on time in this study.The basic structure and working principle of the small maize harvester were described,and the key components were designed as follows.The maize snapping device adopted the combination form of maize snapping plates and straw pulling rollers,and the gap of the straw pulling rollers can be adjusted to adapt to different maize varieties.Two pairs of peeling rollers formed a groove arrangement to improve peeling rate and reduced ear grain loss.The pressure feeding device mainly comprised drive chain and three grade pressure feeding rollers to increase the friction between ears and the peeling rollers,and help ears slide.The attitude adjustment advice was designed according to the high point stationary pursuit leveling method.When the attitude angle of the rack approached 0,the small maize harvester reached the level state.The actual range of attitude adjustment was obtained and the accuracy of static attitude adjustment was verified through attitude adjustment test.The influencing factors of ear loss rate and bract peeling rate were determined by orthogonal test,including the rotational speed of straw pulling rollers,peeling rollers and pressure feeding rollers.The mathematical regression model between the experimental factors and indicators was established by using Design Expert,and through the analysis variance to verify the significance of the evaluation indicators,the best combination of operation parameters was determined that the rotational speed of straw pulling rollers,peeling rollers and pressure feeding rollers were 1440 r/min,1535 r/min and 406 r/min.Under the optimal combination of the operation parameters,the ear loss rate and bract peeling rate were 1.33%and 93.98%.The design indicators of the small maize harvester can meet the relevant national standards,and can satisfy the need of maize mechanized harvesting in China hilly and mountainous areas.

Optimization design and experiment on feeding and chopping device of silage maize harvester

Horizontal feeding devices and plate hob chopping devices are the key component of silage maize harvester.To solve the problem of feeding blockage,reduce energy consumption,and improve the chopping quality of the chopping device a horizontal different diameter five-rollers device(HDDFD)was designed and the plate hob chopping device was simultaneously optimized and analyzed.Through the dynamic analysis,the feeding conveying speed was determined to be 2.0-4.5 m/s.The distance equation of the actual and theoretical cutting-edge curve and the position of the fixed blade were finally obtained.Single factor and response surface orthogonal tests in the bench site were carried out with feeding speed,rotating speed of chopping cylinder,feeding amount,and feeding direction as influencing factors,standard grass length rate(SGLR),and energy consumption per unit mass(ECPUM)as evaluation indexes.The optimal working parameters for chopping performance could be concluded as a feeding speed of 3.39 m/s,rotating speed of the chopping cylinder of 1016.17 r/min,feeding amount of 8.04 kg/s,and feeding direction of 52.2°.In addition,the SGLR and ECPUM were obtained as 95.35%and 37.63 kJ/kg,respectively.The relative error between the experimental results with round parameter combination and the predicted value was verified to be less than 5%.Field tests verified the reliability of the optimized feeding and chopping device.It can be seen that the HDDFD and optimized plate hob chopping device can meet the requirements of mechanized silage harvesting which obviously improves the working quality and reduce the energy consumption of chopping.

Detection and recognition of veterinary drug residues in beef using hyperspectral discrete wavelet transform and deep learning

A fast,non-destructive recognition method for veterinary drug residues in beef was proposed to mitigate the laborious sample preparation and long detection times associated with conventional chemical detection techniques.Control beef samples free of veterinary drug residues and four groups of beef sprayed with relevant concentrations of metronidazole,ofloxacin,salbutamol,and dexamethasone under ambient conditions were analyzed by 400-1000 nm hyperspectral imaging followed by multiplicative scatter correction preprocessing.Data dimension reduction was performed using Competitive Adaptive Reweighted Sampling(CARS),Principal Component Analysis(PCA),and Discrete Wavelet Transform(DWT)based on Haar,db3,bior1.5,sym5,and rbio1.3 wavelet basis functions.Treated data were subjected to Convolutional Neural Network(CNN),Multilayer Perceptron(MLP),Random Forest(RF),and Support Vector Machine(SVM)modelling.CNN,MLP,SVM,and RF algorithms achieved overall accuracies of 91.6%,88.6%,87.6%,and 86.2%,respectively,when combined with DWT(wavelet basis functions and numbers of transform layers being Haar-4,db3-2,bior1.5-4,and sym5-3,respectively).The algorithm Kappa coefficients(0.89,0.86,0.85,and 0.83,respectively)and time consumption for prediction(140.60 ms,57.85 ms,70.67 ms,and 87.16 ms,respectively)were also superior to models based on CARS and PCA.DWT combined with deep learning can shorten prediction times,considerably improve the accuracy of classification and recognition,and alleviate the Hughes phenomenon,thus providing a new method for the fast,non-destructive detection and recognition of veterinary drug residues in beef.