The practical problems of existing methods of walnut cracking under compression loading, including incomplete walnut-shell crushing, broken walnut kernels, and so on, are widespread in walnut processing and are constraints that hinder mechanized walnut processing. Therefore, attempts have been made to design and optimize a multi-point extrusion walnut cracking device. For this, walnuts were fed manually into a cracking unit through the hopper. The tangential force of the grading roller graded the walnuts and dropped them into the gap between the rotating cracking roller and extrusion plate, causing them to crack. The developed machine was tested and the parameters were optimized using a central composite design (CCD). The objective functions involving the cracking angle (CA: 0.17, 0.27, 0.52, 0.76, 0.86°) and roller speed (RS: 63, 75, 105, 135, 147 r/min) were calculated. The shell cracking rate (SCR), whole kernel rate (WKR), and specific energy consumption (Es) regression models were established using the quadratic regression orthogonal combination test and the parameters were optimized using MATLAB software. The results showed that the most significant factors for the RS were the linear terms of the SCR and WKR, whereas for the CA the most significant factor was the linear term of the Es. The interaction term of the two factors had a significant effect on the three indicators. The optimal parameter combination was determined to be 0.47° for the CA and 108 r/min for the RS. On this basis, the adaptability test showed that the cracking device had a better cracking effect on walnuts with a gap between the walnut shell and kernel greater than 1.6 mm and a shell thickness less than 1.2 mm. The results have practical significance for the design of walnut cracking devices.

Simulation can be used to visualize the mechanical classification of walnuts. It can collect microscopic information about walnuts in the classification roller and guide its optimization design. In this process, simulation parameters are essential factors that ensure the effectiveness of the simulation. In this study, the crucial parameters of thin-skinned walnut particles in classification simulation were determined by combining the discrete element method (DEM) and physical tests. Firstly, the moisture content, shear modulus, stacking angle, and some contact parameters in the shell and kernel were obtained by drying test, compression test, cylinder lifting test, and physical test of contact parameters, respectively. A walnut model was constructed using reverse modeling technology. Then, the ranges of the rest contact parameters were determined using simulation inversion based on the Generic EDEM Material Model database. Second, the parameters significantly influencing the stacking angle were screened via the Plackett–Burman test using contact parameters as factors and stacking angle as the index. The results revealed that the walnut–walnut static friction coefficient, walnut–walnut rolling friction coefficient, and walnut–steel plate static friction coefficient significantly affect the stacking angle. The steepest ascent experiment produced the optimal value intervals of crucial parameters. Besides, a quadratic regression model of important parameters was built using the Box–Behnken test to achieve the optimal parameter combination. The stacking and classification experiments verified that the stacking angle and morphology are mostly similar under calibration parameters without any considerable differences. The relative error was only 0.068%. Notably, the relative error of the average staying time of walnut in the classification roller was 0.671%, and the dimensionless distribution curves of stay time were consistent. This study provides technological support to the simulation analysis of walnut classification and recommends novel methods and references to determine the parameters of other shell materials.

Recycling residual plastic film is an important measure to prevent plastic pollution and achieve farmland sustainability, with farmers’ participation being the key. To understand the determinants of farmers’ participation in the recycling of residual plastic film, this study developed an extended TPB framework by introducing moral norm and responsibility attribution as additional explanatory constructs. Based on questionnaire responses from 429 cotton farmers in southern Xinjiang, China, the proposed relationships were examined through structural equation modeling. The empirical results indicated that recycling intentions were mainly determined by attitudes and perceived behavioral control, and responsibility attribution had a positive effect on subjective norm. Furthermore, the influence of moral norm served as a minor mediating factor in the connection between subjective norms and intentions to recycle. These findings not only deepen the understanding of the behavioral mechanisms underlying residual film recovery, but also provide policy-relevant insights for strengthening government efforts to address plastic film pollution.

Walnut shell-kernel separation after cracking is crucial for providing raw materials for further processing. However, impurities and losses during separation limit complete separation. To address this, a two-stage tandem separation device was designed and optimized. Computational fluid dynamics (CFD) was used to analyze the effects of four bent duct structures on the flow field. Response surface methodology (RSM) and artificial neural networks (ANN) were employed to predict separation performance under various conditions. Both models accurately predicted performance, with ANN showing superior predictive ability. The optimal design was determined using non-dominated sorting genetic algorithm-II (NSGA-II) and technique for order preference by similarity to an ideal solution (TOPSIS): the inclination of the first stage deflector plate (x 1) was 39°,the inclination of the second stage deflector plate (x 2) was 36°, the wind speed of the first stage fan (x 3) was 21 m/s, and the wind speed of the second stage fan (x 4) was 13.5 m/s; impurity rate (y 1) was 4.51%, and loss rate (y 2) was 6.62%. Compared with traditional single-stage devices, the optimized device reduced impurity rate by 73.98% to 77.55% and loss rate by 9.44% to 53.96%, significantly improving separation efficiency and quality. This study provides theoretical guidance for designing and optimizing shell-kernel separation devices.