Crop straw chopping and returning technology has gained global implementation to enhance soil structure and fertility, facilitating increased crop yield. Nevertheless, technological adoption faces challenges from inherent limitations in machinery performance, including poor chopping and returning quality and high energy consumption. Consequently, this review first presented a theoretical framework that described the mechanical properties of straw, its fracture dynamics, interactions with airflow, and motion characteristics during the chopping process. Then, based on the straw returning process, the chopping devices were classified into five types: the chopped blade, the chopping machine, the chopping device combined with a no-tillage or reduced-tillage seeder, the chopping and ditch-burying machine, the chopping and mixing machine, and the harvester-powered chopping device. Advancements in spreading devices were also summarized. Finally, six key directions for future research were proposed: developing an intelligent field straw distribution mapping system, engineering adaptive self-regulating mechanisms for chopping and returning equipment, elucidating the mechanics and kinematics of straw in the chopping and returning process, implementing real-time quality assessment systems for straw returning operations, pioneering high forward-speed (>8 km/h) straw returning machines, and establishing context-specific straw residue management frameworks. This review provided a reference and offered support for the global application of straw returning technology.

In view of the prominent problem that nonlinear vibration of a belt conveyor can easily occur during the vertical grain-conveyance process due to the coupling effect of airflow clamping and traction of the conveyor belt, which seriously affects the efficiency and stability of conveying materials by the belt conveyor, a method of solving the vibration analysis of the vertical lifting section of the bulk grain belt unloader by using nonlinear vibration is proposed. Firstly, based on the laminated plate theory, the vertical lifting belt and the grain material clamped by the belt are laminated. The nonlinear vibration differential equation of the vertical lifting section of the bulk grain-carrying ship unloader is established by elastic–plastic mechanics, and solved by perturbation theory and Galerkin discrete analysis. The vibration response curve and structural natural frequency of the vertical lifting section of the bulk grain-carrying machine are obtained by numerical solution, and the influence of the volume content of the clamped material on the vibration response and structural natural frequency of the lifting section is analyzed. This study provides theoretical support for the design of pressure-supply parameters, overall structure and operation parameters of the subsequent entrainment ship unloader, promotes the rapid development of the entrainment ship unloader, provides theoretical support for the design, manufacture, later operation, and maintenance of the entrainment ship unloader, and thus provides equipment and technical support for building an efficient and intelligent port.

The main causes of high productional costs and greenhouse gas emissions in the chopping process of maize straws are high energy consumption and breaking force. Addressing these issues, this paper proposes a solution that leverages machine-learning algorithms to select appropriate operational parameters for chopping devices, thereby reducing energy consumption and the cutting force. In this study, the peak breaking force of the stalk (PB), the energy consumption of the stalk chopping (EC) and the slide-cutting momentum of the disc blade (SM) were set as dependent variables, and the rotation speed of the Y-type blade (RSY), transmission ratio (TR) and slide-cutting angle (SA) were set as independent variables. Various techniques, including back-propagation (BP), a radial basis function (RBF), an artificial neural network (ANN), support vector regression and a stepwise polynomial regression model, were applied using a 6-fold cross-validation approach to determine the most effective predictive models. The results indicated that the BP-ANN model performs best in predicting the PB (R²_Test = 0.9860) and SM (R²_Test = 0.9561), while the RBF-ANN model yields the highest accuracy in predicting the EC (R²_Test = 0.9255) under the optimal parameters. Subsequently, a verification test was conducted using randomly selected training and testing data based on the selected predicted functions. The results demonstrated that the R²_Train and R²_Test data for PB, EC and SM are all above 0.95, indicating that the BP and RBF neural networks are capable of accurately predicting the nonlinear relationship between the dependent variables (EC, SM and PB) and independent variables (RSY, TR and SA) in practical applications.

Distribution properties of airflow in the full envelope type maize stalk chopping chamber

Middle borosilicate glass tubes are critical materials for medical packaging, with the drawing machine playing a pivotal role in their production process. However, the effects of the drawing wheel’s motion parameters on the glass tube remain underexplored. Therefore, based on the finite element method simulation and validation experiment, the effects of experimental factors (angle of upper and lower drawing wheel, AUD; friction factor of glass and rubber, FFGR; and distance of upper and lower drawing wheel, DUD) on experimental indexes (maximum effective stress of glass tube, MES; rotational speed of glass tube, RSG; maximum and average forward driving force, MFDF and AFDF; maximum and average rotational driving force, MRDF and ARDF; total energy consumption and power, TEC and TPD; maximum and average forward driving power, MFDP and AFDP; and maximum and average rotational driving power, MRDP and ARDP) were analyzed. The results indicated that compared to FFGR and AUD, the influence of DUD on experimental indexes was the highest. The positive influence of AUD on RSG, ARDP, and MRDP, the positive influence of FFGR and the negative influence of DUD on MRDF, ARDF, TEC, TPD, ARDP, and MRDP were found, respectively. These findings will provide a theoretical reference for the optimization of drawing machines.

Anaerobic digestion is crucial for safe treatment and energy recovery from municipal sludge. However, seasonal variations in sludge physicochemical properties challenge the continuous, stable operation of anaerobic digestion systems. To investigate the seasonal variations in characteristics of municipal sludge and their impact, this study collected sludge samples from a Beijing plant over a year, analyzed their properties and microbial communities, and evaluated their biogas potential through four-week batch anaerobic digestion tests. The results demonstrated that spring sludge exhibited the highest organic matter (68.7% of total solids, TS), including soluble proteins, sugars, and lipids, while the lignocellulose content peaked in autumn (17% TS). These fluctuations were primarily driven by variations in rainfall, temperature, and human activities. The microbial community shifted significantly: Proteiniclasticum and other hydrolytic bacteria were dominant in spring, whereas Candidatus_Microthrix was notably enriched in winter. Consequently, the biochemical methane potential (BMP) was highest in spring (342.5 mL/g volatile solids) and lowest in autumn (255.8 mL/g volatile solids). Spearman’s correlation analysis indicated a significant positive correlation between BMP and soluble protein content, and a weak negative correlation with cellulose content. These findings provide essential data support for seasonal regulation of sludge anaerobic digestion systems, facilitating strategies to achieve stable biogas production.