This paper illustrates a simple yet effective spectroscopic technique for the prediction of soil organic matter (SOM) from moist soil through the synchronous 2D correlation spectroscopy (2D-COS) analysis. In the moist soil system, the strong overlap between the water absorption peaks and the SOM characteristic features in the visible-near infrared (Vis-NIR) spectral region have long been recognised as one of the main factors that causes significant errors in the prediction of the SOM content. The aim of the paper is to illustrate how the tangling effects due to the moisture and the SOM can be unveiled under 2D-COS through a sequential correlogram analysis of the two perturbation variables (i.e., the moisture and the SOM) independently. The main outcome from the 2D-COS analysis is the discovery of SOM-related bands at the 597 nm, 1646 nm and 2138 nm, together with the predominant water absorbance feature at the 1934 nm and the relatively less important ones at 1447 nm and 2210 nm. This information is then utilised to build partial least square regression (PLSR) models for the prediction of the SOM content. The experiment has shown that by discarding noisy bands adjacent to the SOM features, and the removal of the water absorption bands, the determination coefficient of prediction (Rp2) and the ratio of prediction to deviation (RPD) for the prediction of SOM from moist soil have achieved Rp2 = 0.92 and the RPD = 3.19, both of which are about 5% better than that of using all bands for building the PLSR model. The very high RPD (=3.19) obtained in this study may suggest that the 2D-COS technique is effective for the analysis of complex system like the prediction of SOM from moist soil.
To improve the operating effect of buckwheat classifying equipment and meanwhile reduce the dependence on tests in the process of operating parameter optimization of the equipment, this paper designed a three-level classifying screen for buckwheat, confirmed the structure and parameters of upper and lower sieves, established a three-level screening discrete element model for buckwheat with the EDEM software, and conducted the numerical simulation for the sieving processes at an amplitude of 24 mm, 28 mm and 32 mm, respectively. The results indicated that when the inclination angle of screen surface was 3°, the vibrational direction angle was 30° and the vibrational frequency was 4.5 HZ, as the amplitude increased, the conveying capacity of the classifying screen increased and at the same time the seed loss rate also increased, of which at 16 s, the loss rate was 0.03%, 0.37% and 1.42%, respectively; the proportion of medium particles in the collecting box of screen overflow was 2.88%, 8.65% and 17.65%, respectively; and the proportion of small particles in the collecting box of screen residue was 0.58%, 6.06% and 19.14%, respectively. Through comprehensive analysis of conveying capacity, screening loss and classifying effect, when the amplitude of the classifying screen was 28 mm, the classifying operating effect was good. This study can provide reference for the design and operating parameter optimization of buckwheat classifying equipment.
Since a combined harvester’s grain-cleaning method depends on the pneumatic separation of grain and chaff, the airflow’s aerodynamic forces significantly affect cleaning efficiency. Based on buckwheat’s theoretical and mechanical properties, a new threshing drum with cleaning key parts was developed to reduce the variability of cleaning efficiency of buckwheat community threshers caused by inefficient threshing and accumulation of residue within the threshing system. This cleaning arrangement includes two wind speed inlets, each composed of four thin pipes of the same length as the threshing drum. The computational fluid dynamics modelling approach simulated the threshing and cleaning performance at different wind velocities within the threshing unit. The results showed that when the two inlets work simultaneously and adopt different wind speeds, i.e., 12 m/s and 15 m/s, the wind speed is higher than the critical value of the floating rate buckwheat kernel. Under this condition, the wind speed inlet area was increased, and the flow field velocity between the threshing drum and the concave grid plate ranged from 3.8 m/s-8.3 m/s. The flow velocity below the plate ranged from 7 m/s-15 m/s, higher than the floating speed of buckwheat kernels, which was the best choice. Based on these simulation results, a centrifugal fan was designed, which meets the buckwheat thresher’s cleaning performance.
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